NIPS 2018 接收论文list 完整清单

NIPS2018 接收论文包括poster、tutorial、workshop等，目前官网公布了论文清单：

https://nips.cc/Conferences/2018/Schedule

Poster paper

>~1. Zero-Shot Transfer with Deictic Object-Oriented Representation in Reinforcement Learning
~2. The Price of Fair PCA: One Extra dimension
~3. Transfer of Deep Reactive Policies for MDP Planning
~4. Sequential Data Classification for Resource-constrained Devices
~5. Sparse PCA from Sparse Linear Regression
~6. Computationally and Statistically Efficient Learning of Bayes Nets Using Path Queries
~7. Point process latent variable models of freely swimming larval zebrafish
~8. Contrastive Learning from Pairwise Measurements
~9. Topkapi: Parallel and Fast Algorithm for Finding Top-K Frequent Elements
~10. Removing Hidden Confounding by Experimental Grounding
~11. Semidefinite relaxations for certifying robustness to adversarial examples
~12. MixLasso: Generalized Mixed Regression via Convex Atomic-Norm Regularization
~13. Smoothed Analysis of Discrete Tensor Decomposition and Assemblies of Neurons
~14. Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
~15. Multi-value Rule Sets for Interpretable Classification with Feature-Efficient Representations
~16. Differentially Private Change-Point Detection
~17. Support Recovery for Orthogonal Matching Pursuit: Upper and Lower bounds
~18. Fast and Effective Robustness Certification
~19. Bias and Generalization in Deep Generative Models: An Empirical Study
~20. Learning Temporal Point Processes via Reinforcement Learning
~21. Benefits of overparameterization with EM
~22. Learning Beam Search Policies via Imitation Learning
~23. Data-Driven Clustering
~24. Understanding Regularized Spectral Clustering via Graph Conductance
~25. Fully Neural Network Based Speech Recognition on Mobile and Embedded Devices
~26. Connecting Optimization and Regularization Paths
~27. Sketching Method for Large Scale Combinatorial Inference
~28. Regret Bounds for Online Portfolio Selection with a Cardinality Constraint
~29. Improved Network Robustness with Adversary Critic
~30. Fast deep reinforcement learning using online adjustments from the past
~31. Streamlining constraints for random k-SAT
~32. Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders
~33. Gated Complex Recurrent Neural Networks
~34. Bayesian Structure Learning by Recursive Bootstrap
~35. The Sparse Manifold Transform
~36. Deep Generative Models with Learnable Knowledge Constraints
~37. Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
~38. Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
~39. Discretely Relaxing Continuous Variables for tractable Variational Inference
~40. Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
~41. Temporal alignment and latent Gaussian process factor inference in population spike trains
~42. Bounded-Loss Private Prediction Markets
~43. Learning Abstract Options
~44. Deep Learning for Supercomputers: Distributed Tensor Layouts Define Distributed Computation
~45. Convex Elicitation of Continuous Properties
~46. Context-aware Synthesis and Placement of Object Instances
~47. 3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
~48. Gaussian Process Prior Variational Autoencoders
~49. Adversarial Risk and Robustness for Discrete Distributions
~50. Unsupervised Image-to-Image Translation Using Domain-Specific Variational Information Bound
~51. Using Quantum Graphical Models to Perform Inference in Hilbert Space
~52. Lifted Weighted Mini-Bucket
~53. Learning to solve SMT formulas
~54. PCA of high dimensional stochastic processes
~55. Improving Simple Models with Confidence Profiles
~56. Robust Learning of Fixed-Structure Bayesian Networks
~57. Learning conditional GAN using noisy labels
~58. Predictive Approximate Bayesian Computation via Saddle Points
~59. Learning to Share and Hide Intentions using Information Regularization
~60. Generalizing Point Embeddings using the Wasserstein Space of Elliptical Distributions
~61. Glow: Generative Flow with Invertible 1x1 Convolutions
~62. Total stochastic gradient algorithms and applications in reinforcement learning
~63. Learning with SGD and Random Features
~64. Backpropagation with Callbacks: Towards Efficient and Expressive Differentiable Programming
~65. Learning To Learn Around A Common Mean
~66. Human-in-the-Loop Interpretability Prior
~67. Synaptic Strength For Convolutional Neural Network
~68. A Spectral View of Adversarially Robust Features
~69. Bayesian Nonparametric Spectral Estimation
~70. Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
~71. A Simple Cache Model for Image Recognition
~72. Low-rank Tucker decomposition of large tensors using TensorSketch
~73. Blockwise Parallel Decoding for Deep Autoregressive Models
~74. Thwarting Adversarial Examples: An $L_0$-Robust Sparse Fourier Transform
~75. Testing for Families of Distributions via the Fourier Transform
~76. A Retrieve-and-Edit Framework for Predicting Structured Outputs
~77. Scalable Laplacian K-modes
~78. Blind Deconvolutional Phase Retrieval via Convex Programming
~79. Neural Voice Cloning with a Few Samples
~80. Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
~81. Memory Augmented Policy Optimization for Program Synthesis with Generalization
~82. Learning to Reason with Third Order Tensor Products
~83. Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
~84. Using Large Ensembles of Control Variates for Variational Inference
~85. Non-delusional Q-learning and Value-iteration
~86. Learning Invariances using the Marginal Likelihood
~87. Uplift Modeling from Separate Labels
~88. Online Robust Policy Learning in the Presence of Unknown Adversaries
~89. Variance-Reduced Stochastic Gradient Descent on Streaming Data
~90. On Markov Chain Gradient Descent
~91. Maximizing acquisition functions for Bayesian optimization
~92. Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies
~93. Dynamic Network Model from Partial Observations
~94. ATOMO: Communication-efficient Learning via Atomic Sparsification
~95. Reinforcement Learning for Solving the Vehicle Routing Problem
~96. Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation
~97. Temporal abstraction for recurrent dynamical models
~98. Object-Oriented Dynamics Predictor
~99. Adaptive Methods for Nonconvex Optimization
~100. Entropy Rate Estimation for Markov Chains with Large State Space

-----------100 papers-----------


>~101. Large Scale computation of Means and Clusters for Persistence Diagrams using Optimal Transport
~102. Deep Anomaly Detection Using Geometric Transformations
~103. Generalization Bounds for Uniformly Stable Algorithms
~104. Unsupervised Depth Estimation, 3D Face Rotation and Replacement
~105. Towards Deep Conversational Recommendations
~106. Latent Alignment and Variational Attention
~107. Improving Explorability in Variational Inference with Annealed Variational Objectives
~108. Coupled Variational Bayes via Optimization Embedding
~109. Theoretical guarantees for EM under misspecified Gaussian mixture models
~110. Non-convex Optimization with Discretized Diffusions
~111. Improving Online Algorithms via ML Predictions
~112. Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
~113. Ex ante correlation and collusion in zero-sum multi-player extensive-form games
~114. Invertibility of Convolutional Generative Networks from Partial Measurements
~115. Trading robust representations for sample complexity through self-supervised visual experience
~116. An intriguing failing of convolutional neural networks and the CoordConv solution
~117. Optimal Algorithms for Continuous   Non-monotone Submodular and DR-Submodular Maximization
~118. To What Extent Do Different Neural Networks Learn the Same Representation: A Neuron Activation Subspace Match Approach
~119. Neural Proximal Gradient Descent for Compressive Imaging
~120. Learning convex bounds for linear quadratic control policy synthesis
~121. Fast Approximate Natural Gradient Descent in a Kronecker Factored Eigenbasis
~122. e-SNLI: Natural Language Inference with Natural Language Explanations
~123. Reinforcement Learning with Multiple Experts: A Bayesian Model Combination Approach
~124. Uncertainty-Aware Few-Shot Learning with Probabilistic Model-Agnostic Meta-Learning
~125. Sanity Checks for Saliency Maps
~126. Multi-objective Maximization of Monotone Submodular Functions with Cardinality Constraint
~127. PAC-Bayes Tree: weighted subtrees with guarantees
~128. DAGs with NO TEARS: Continuous Optimization for Structure Learning
~129. Implicit Bias of Gradient Descent on Linear Convolutional Networks
~130. Learning and Testing Causal Models with Interventions
~131. Discovering Feedback Codes via Deep Learning
~132. Identification and Estimation of Causal Effects from Dependent Data
~133. Quantifying Linguistic Shifts: The Global Anchor Method and Its Applications
~134. Gather-Scatter: Context Propagation for ConvNets
~135. The emergence of multiple retinal cell types through efficient coding of natural movies
~136. Learning Attractor Dynamics for Generative Memory
~137. Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
~138. Statistical and Computational Trade-Offs in Kernel K-Means
~139. Co-regularized Alignment for Unsupervised Domain Adaptation
~140. Hardware Conditioned Policies for Multi-Robot Transfer Learning
~141. Sample Complexity of Nonparametric Semi-Supervised Learning
~142. SNIPER: Efficient Multi-Scale Training
~143. The Effect of Network Width on the Performance of  Large-batch Training
~144. Representer Point Selection for Explaining Deep Neural Networks
~145. The Importance of Sampling inMeta-Reinforcement Learning
~146. Confounding-Robust Policy Improvement
~147. Deep Dynamical Modeling and Control of Unsteady Fluid Flows
~148. Coordinate Descent with Bandit Sampling
~149. The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization
~150. Beyond Grids: Learning Graph Representations for Visual Recognition
~151. PAC-Bayes bounds for stable algorithms with instance-dependent priors
~152. Deep Predictive Coding Network with Local Recurrent Processing for Object Recognition
~153. Visual Goal-Conditioned Reinforcement Learning by Representation Learning
~154. Watch Your Step: Learning Node Embeddings via Graph Attention
~155. A Stein variational Newton method
~156. Reducing Network Agnostophobia
~157. Quadrature-based features for kernel approximation
~158. Phase Retrieval Under a Generative Prior
~159. Learning SMaLL Predictors
~160. Bayesian multi-domain learning for cancer subtype discovery from next-generation sequencing count data
~161. Learning safe policies with expert guidance
~162. Robot Learning in Homes: Improving Generalization and Reducing Dataset Bias
~163. Evading the Adversary in Invariant Representation
~164. Iterative Value-Aware Model Learning
~165. Theoretical Linear Convergence of Unfolded ISTA and Its Practical Weights and Thresholds
~166. Learning Compressed Transforms with Low Displacement Rank
~167. SING: Symbol-to-Instrument Neural Generator
~168. Reversible Recurrent Neural Networks
~169. FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
~170. Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features
~171. A Structured Prediction Approach for Label Ranking
~172. Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
~173. Wavelet regression and additive models for irregularly spaced data
~174. Online Learning of Quantum States
~175. Unsupervisedly Learned Latent Graphs as Transferable Representations
~176. Policy-Conditioned Uncertainty Sets for Robust Markov Decision Processes
~177. Adaptive Path-Integral Approach to Representation Learning and Planning for Dynamical Systems
~178. Improving Neural Program Synthesis with Inferred Execution Traces
~179. Distributed Multitask Reinforcement Learning with Quadratic Convergence
~180. Balanced Policy Evaluation and Learning
~181. Statistical Recurrent Models on Manifold valued Data
~182. Exploration in Structured Reinforcement Learning
~183. Differential Privacy for Growing Databases
~184. Stein Variational Gradient Descent as Moment Matching
~185. Group Equivariant Capsule Networks
~186. Data Amplification: A Unified and Competitive Approach to Property Estimation
~187. Reinforcement Learning of Theorem Proving
~188. Legendre Decomposition for Tensors
~189. A flexible neural representation for physics prediction
~190. Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs
~191. Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
~192. A Bayesian Nonparametric View on Count-Min Sketch
~193. Automatic differentiation in ML: Where we are and where we should be going
~194. Uniform Convergence of Gradients for Non-Convex Learning and Optimization
~195. Learning Plannable Representations with Causal InfoGAN
~196. Dendritic cortical microcircuits approximate the backpropagation algorithm
~197. Orthogonally Decoupled Variational Gaussian Processes
~198. Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
~199. Synthesis of Differentiable Functional Programs for Lifelong Learning
~200. DeepPINK: reproducible feature selection in deep neural networks

-----------200 papers-----------


>~201. Estimators for Multivariate Information Measures in General Probability Spaces
~202. Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
~203. Learning without Phase: Regularized PhaseMax Achieves Optimal Sample Complexity
~204. Minimax Rates in Contextual Partial Monitoring
~205. Compact Representation of Uncertainty In Clustering
~206. Randomized Prior Functions for Deep Reinforcement Learning
~207. Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
~208. A Likelihood-Free Inference Framework for Population Genetic Data using Exchangeable Neural Networks
~209. A statistical model for graph partitioning with high-dimensional covariates
~210. Neural Tangent Kernel: Convergence and Generalization in Neural Networks
~211. Adversarial Multiple Source Domain Adaptation
~212. A convex program for bilinear inversion of sparse vectors
~213. An Event-Based Framework for Task Specification and Control
~214. Co-teaching: Robust Training Deep Neural Networks with Extremely Noisy Labels
~215. Clustering Redemption–Beyond the Impossibility of Kleinberg’s Axioms
~216. Adversarial Regularizers in Inverse Problems
~217. Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization
~218. Generalisation of structural knowledge in the Hippocampal-Entorhinal system
~219. Wasserstein Distributionally Robust Kalman Filtering
~220. Teaching Inverse Reinforcement Learners via Features and Demonstrations
~221. Continuity vs. Injectivity in Dimensionality Reduction: a Quantitative Topology View
~222. Deep Poisson gamma dynamical systems
~223. Data-dependent PAC-Bayes priors via differential privacy
~224. Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
~225. Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
~226. Scaling provable adversarial defenses
~227. Learning to Play With Intrinsically-Motivated, Self-Aware Agents
~228. On avoiding discrimination in online learning
~229. Stochastic Primal-Dual Method for Empirical Risk Minimization with O(1) Per-Iteration Complexity
~230. Transfer Learning with Neural AutoML
~231. Distributionally Robust Graphical Models
~232. Learning Conditioned Graph Structures for Interpretable Visual Question Answering
~233. Information-theoretic Limits for Community Detection in Network Models
~234. Generative Adversarial Examples
~235. Bilevel learning of the Group Lasso structure
~236. Differentiable MPC for End-to-end Planning and Control
~237. Constrained Cross-Entropy Method for Safe Reinforcement Learning
~238. How to tell when a clustering is (approximately) correct using convex relaxations
~239. Revisiting $(\epsilon, \gamma, \tau)$-similarity learning for domain adaptation
~240. Stochastic Chebyshev Gradient Descent for Spectral Optimization
~241. Out-of-Distribution Detection using Multiple Semantic Label Representations
~242. Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
~243. Unsupervised Cross-Modal Alignment of Speech and Text Embedding Spaces
~244. Disconnected Manifold Learning for Generative  Adversarial Networks
~245. Bayesian Model-Agnostic Meta-Learning
~246. Exploring Sparse Features in Deep Reinforcement Learning towards Fast Disease Diagnosis
~247. Streaming~Kernel~PCA~with~$\tilde{O}(\sqrt{n})$~Random~Features
~248. Relational recurrent neural networks
~249. Unsupervised Text Style Transfer using Language Models as Discriminators
~250. Bandit Learning with Implicit Feedback
~251. Training Deep Models Faster with Robust, Approximate Importance Sampling
~252. Learning Attentional Communication for Multi-Agent Cooperation
~253. Implicit Probabilistic Integrators for ODEs
~254. Chaining Mutual Information and Tightening Generalization Bounds
~255. Efficient Loss-Based Decoding On Graphs For Extreme Classification
~256. Distributed Multi-Player Bandits - a Game of Thrones Approach
~257. Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction
~258. Stimulus domain transfer in recurrent models for large scale cortical population prediction on video
~259. BRUNO: A Deep Recurrent Model for Exchangeable Data
~260. End-to-End Differentiable Physics for Learning and Control
~261. A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks
~262. Multitask Reinforcement Learning for Zero-shot Generalization with Subtask Dependencies
~263. Parameters as interacting particles: asymptotic scaling, convexity, and error of neural networks
~264. Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
~265. Provably Correct Automatic Sub-Differentiation for Qualified Programs
~266. Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
~267. Model-Agnostic Private Learning
~268. On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
~269. Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
~270. A probabilistic population code based on neural samples
~271. Dual Policy Iteration
~272. Predictive Uncertainty Estimation via Prior Networks
~273. GILBO: One Metric to Measure Them All
~274. Efficient online algorithms for fast-rate regret bounds under sparsity
~275. Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
~276. Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks
~277. Bayesian Alignments of Warped Multi-Output Gaussian Processes
~278. Causal Inference via Kernel Deviance Measures
~279. Unorganized Malicious Attacks Detection
~280. A Probabilistic U-Net for Segmentation of Ambiguous Images
~281. Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss
~282. Joint Autoregressive and Hierarchical Priors for Learned Image Compression
~283. Decentralize and Randomize: Faster Algorithm for Wasserstein Barycenters
~284. With Friends Like These, Who Needs Adversaries?
~285. Forward Modeling for Partial Observation Strategy Games - A StarCraft Defogger
~286. DropBlock: A regularization method for convolutional networks
~287. Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language
~288. Analysis of Krylov Subspace Solutions of  Regularized Non-Convex Quadratic Problems
~289. Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
~290. Robust Subspace Approximation in a Stream
~291. Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
~292. Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
~293. rho-POMDPs have Lipschitz-Continuous epsilon-Optimal Value Functions
~294. Causal Inference with Noisy and Missing Covariates via Matrix Factorization
~295. Maximizing Induced Cardinality Under a Determinantal Point Process
~296. Efficient Convex Completion of Coupled Tensors using Coupled Nuclear Norms
~297. Bayesian Adversarial Learning
~298. Differentially Private Testing of Identity and Closeness of Discrete Distributions
~299. Scaling Gaussian Process Regression with Derivatives
~300. Stochastic Nonparametric Event-Tensor Decomposition

-----------300 papers-----------


>~301. Scalable Hyperparameter Transfer Learning
~302. Diminishing Returns Shape Constraints for Interpretability and Regularization
~303. Generative Probabilistic Novelty Detection with Adversarial Autoencoders
~304. Efficient Gradient Computation for Structured Output Learning with Rational and Tropical Losses
~305. Extracting Relationships by Multi-Domain Matching
~306. M-Walk: Learning to Walk in Graph  with Monte Carlo Tree Search
~307. BRITS: Bidirectional Recurrent Imputation for Time Series
~308. Provable Gaussian Embedding with One Observation
~309. Banach Wasserstein GAN
~310. A Theory-Based Evaluation of Nearest Neighbor Models Put Into Practice
~311. Policy Regret in Repeated Games
~312. Large-Scale Stochastic Sampling from the Probability Simplex
~313. Heterogeneous Multi-output Gaussian Process Prediction
~314. On gradient regularizers for MMD GANs
~315. Model-based targeted dimensionality reduction for neuronal population data
~316. Representation Learning of Compositional Data
~317. Modeling Dynamic Missingness of Implicit Feedback for Recommendation
~318. Training Neural Networks Using Features Replay
~319. Query K-means Clustering and the Double Dixie Cup Problem
~320. CatBoost: unbiased boosting with categorical features
~321. Incorporating Context into Language Encoding Models for fMRI
~322. An Improved Analysis of Alternating Minimization for Structured Multi-Response Regression
~323. Contamination Attacks in Multi-Party Machine Learning
~324. Approximating Real-Time Recurrent Learning with Random Kronecker Factors
~325. Unsupervised Learning of Artistic Styles with Archetypal Style Analysis
~326. Black-box ODE Solvers as a Modeling Primitive
~327. On Coresets for Logistic Regression
~328. Proximal SCOPE for Distributed Sparse Learning
~329. Lipschitz-Margin Training: Scalable Certification of Perturbation Invariance for Deep Neural Networks
~330. The Everlasting Database: Statistical Validity at a Fair Price
~331. On the Local Hessian in Back-propagation
~332. Compact Generalized Non-local Network
~333. Online Adaptive Methods, Universality and Acceleration
~334. Size-Noise Tradeoffs in Generative Networks
~335. Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation
~336. Learning to Teach with Dynamic Loss Functions
~337. Turbo Learning for Captionbot and Drawingbot
~338. Learning Latent Subspaces in Variational Autoencoders
~339. L4: Practical loss-based stepsize adaptation for deep learning
~340. Rich gets richer, Poor gets zero: On Sparse Alternatives to Softmax
~341. Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
~342. The Limits of Post-Selection Generalization
~343. Visualizing the Loss Landscape of Neural Nets
~344. Bayesian Distributed Stochastic Gradient Descent
~345. Efficient Formal Safety Analysis of Neural Networks
~346. A no-regret generalization of hierarchical softmax to extreme multi-label classification
~347. Distributed Learning without Distress: Privacy-Preserving Empirical Risk Minimization
~348. Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
~349. Deep Structured Prediction via Nonlinear Output Transformations
~350. Navigating with Graph Representations for Fast and Scalable Decoding of Neural Language Models
~351. Algebraic tests of general Gaussian latent tree models
~352. Exponentially Weighted Imitation Learning for Batched Historical Data
~353. Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences
~354. Multi-domain Causal Structure Learning in Linear Systems
~355. Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
~356. SLANG: Fast Structured Covariance Approximations for Bayesian Deep Learning with Natural Gradient
~357. LF-Net: Learning Local Features from Images
~358. Learning towards Minimum Hyperspherical Energy
~359. Deep Neural Networks with Box Convolutions
~360. Sharp Bounds for Generalized Uniformity Testing
~361. The Cluster Description Problem - Complexity Results, Formulations and Approximations
~362. Transfer of Value Functions via Variational Methods
~363. ResNet with one-neuron hidden layers is a Universal Approximator
~364. Deep State Space Models for Unconditional Word Generation
~365. Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
~366. Online convex optimization for cumulative constraints
~367. Recurrent Transformer Networks for Semantic Correspondence
~368. Information Constraints on Auto-Encoding Variational Bayes
~369. Poison Frogs! Targeted Clean-Label PoisoningAttacks on Neural Networks
~370. MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
~371. Variational Learning on Aggregate Outputs with Gaussian Processes
~372. Graphical Generative Adversarial Networks
~373. Learning to Infer Graphics Programs from Hand-Drawn Images
~374. Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
~375. Stochastic fairness in clustering
~376. Dimensionally Tight Bounds for Second-Order Hamiltonian Monte Carlo
~377. Multi-Task Zipping via Layer-wise Neuron Sharing
~378. Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification
~379. Stacked Semantics-Guided Attention Model for Fine-Grained Zero-Shot Learning
~380. Automating Bayesian optimization with Bayesian optimization
~381. The Convergence of Sparsified Gradient Methods
~382. Memory Replay GANs: Learning to Generate New Categories without Forgetting
~383. Constructing Fast Network through Deconstruction of Convolution
~384. Exact natural gradient in deep linear networks and its application to the nonlinear case
~385. Deep Generative Models for Distribution-Preserving Lossy Compression
~386. Binary Classification from Positive-Confidence Data
~387. Diverse Ensemble Evolution: Curriculum based Data-Model Marriage
~388. Dual Swap Disentangling
~389. A Bayes-Sard Cubature Method
~390. Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching
~391. Differential Properties of Sinkhorn Approximation for Learning with Wasserstein Distance
~392. On GANs and GMMs
~393. Masking: A New Perspective of Noisy Supervision
~394. Gamma-Poisson Dynamic Matrix Factorization Embedded with Metadata Influence
~395. CapProNet: Deep Feature Learning via Orthogonal Projections onto Capsule Subspaces
~396. Transparency by Disentangling Interactions
~397. Computing Kantorovich-Wasserstein Distances on $d$-dimensional histograms using $(d+1)$-partite graphs
~398. Loss Functions for Multiset Prediction
~399. Learning to Multitask
~400. Adversarially Robust Optimization with Gaussian Processes

-----------400 papers-----------


>~401. Mental Sampling in Multimodal Representations
~402. Variational Inference with Tail Adapted f-Divergence
~403. Insights on representational similarity in neural networks with canonical correlation
~404. Critical initialisation for deep signal propagation in noisy rectifier neural networks
~405. Learning convex polytopes with margin
~406. Efficient inference for time-varying behavior during learning
~407. Unsupervised Video Object Segmentation for Deep Reinforcement Learning
~408. On Fast Leverage Score Sampling and Optimal Learning
~409. Bandit Learning in Concave N-Person Games
~410. Online Improper Learning with an Approximation Oracle
~411. Contextual Pricing for Lipschitz Buyers
~412. Learning Others' Intentional Models in Multi-Agent Settings Using Interactive POMDPs
~413. Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
~414. Manifold Structured Prediction
~415. Impossibility of deducing preferences and rationality from human policy
~416. How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
~417. Multimodal Generative Models for Scalable Weakly-Supervised Learning
~418. A Simple Proximal Stochastic Gradient Method for Nonsmooth Nonconvex Optimization
~419. Reparameterization Gradient for Non-differentiable Models
~420. To Trust Or Not To Trust A Classifier
~421. First-order Stochastic Algorithms for Escaping From Saddle Points in Almost Linear Time
~422. Middle-Out Decoding
~423. Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing
~424. Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
~425. A Riemannian approach to trace norm regularized low-rank tensor completion
~426. Community Exploration: From Offline Optimization to Online Learning
~427. Algorithmic Assurance: An Active Approach to Algorithmic Testing using Bayesian Optimisation
~428. Estimating Learnability in the Sublinear Data Regime
~429. Adversarial Logit Pairing
~430. Policy Optimization via Importance Sampling
~431. Differentially Private k-Means with Constant Multiplicative Error
~432. Learning Concave Conditional Likelihood Models for Improved Analysis of Tandem Mass Spectra
~433. The Spectrum of the Fisher Information Matrix of a Single-Hidden-Layer Neural Network
~434. Evolved Policy Gradients
~435. Fully Understanding The Hashing Trick
~436. Learning an olfactory topography from neural activity in piriform cortex
~437. Learning Task Specifications from Demonstrations
~438. Breaking the Curse of Horizon: Infinite-Horizon Off-policy Estimation
~439. Hyperbolic Neural Networks
~440. Generalizing to Unseen Domains via Adversarial Data Augmentation
~441. Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
~442. Sample Efficient Stochastic Gradient Iterative Hard Thresholding Method for Stochastic Sparse Linear Regression with Limited Attribute Observation
~443. Meta-Reinforcement Learning of Structured Exploration Strategies
~444. Task-Driven Convolutional Recurrent Models of the Visual System
~445. Experimental Design for Cost-Aware Learning of Causal Graphs
~446. Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression
~447. Horizon-Independent Minimax Linear Regression
~448. A Convex Duality Framework for GANs
~449. Multiple-Step Greedy Policies in Approximate and Online Reinforcement Learning
~450. Assessing Generative Models via Precision and Recall
~451. Contour location via entropy reduction leveraging multiple information sources
~452. Causal Inference and Mechanism Clustering of a Mixture of Additive Noise Models
~453. ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
~454. Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model
~455. Why so gloomy? A Bayesian explanation of human pessimism bias in the multi-armed bandit task
~456. Learning Link Prediction Heuristics from Local Subgraphs: Theory and Practice
~457. Dropping Symmetry for Fast Symmetric Nonnegative Matrix Factorization
~458. Scalable methods for 8-bit training of neural networks
~459. Multi-agent Online Learning with Asynchronous Feedback Loss
~460. GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
~461. Multi-armed Bandits with Compensation
~462. Content preserving text generation with attribute controls
~463. Scalable Robust Matrix Factorization with Nonconvex Loss
~464. LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
~465. Practical exact algorithm for trembling-hand equilibrium refinements in games
~466. Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents
~467. Adversarially Robust Generalization Requires More Data 
~468. Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks
~469. Supervising Unsupervised Learning
~470. Learning from Group Comparisons: Exploiting Higher Order Interactions
~471. Objective and efficient inference for couplings in neuronal networks
~472. Neural Edit Operations for Biological Sequences
~473. Hessian-based Analysis of Large Batch Training and Robustness to Adversaries
~474. Efficient Neural Network Robustness Certification with General Activation Functions
~475. Learning Confidence Sets using Support Vector Machines
~476. Bandit Learning with Positive Externalities
~477. Densely Connected Attention Propagation for Reading Comprehension
~478. On the Local Minima of the Empirical Risk
~479. Measures of distortion for machine learning
~480. Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
~481. Is Q-Learning Provably Efficient?
~482. Adaptive Negative Curvature Descent with Applications in Non-convex Optimization
~483. Fairness Through Computationally-Bounded Awareness
~484. Porcupine Neural Networks: Approximating Neural Network Landscapes
~485. Information-based Adaptive Stimulus Selection to Optimize Communication Efficiency in Brain-Computer Interfaces
~486. Non-Ergodic Alternating Proximal  Augmented Lagrangian Algorithms with Optimal Rates
~487. Hierarchical Graph Representation Learning with Differentiable Pooling
~488. A Unified View of Piecewise Linear Neural Network Verification
~489. Context-dependent upper-confidence bounds for directed exploration
~490. A Smoother Way to Train Structured Prediction
~491. Data-Efficient Model-based Reinforcement Learning with Deep Probabilistic Dynamics Models
~492. Fast greedy algorithms for dictionary selection with generalized sparsity constraints
~493. Recurrently Controlled Recurrent Networks
~494. Non-metric Similarity Graphs for Maximum Inner Product Search
~495. Negotiable Reinforcement Learning for Pareto Optimal Sequential Decision-Making
~496. A Mathematical Model For Optimal Decisions In A Representative Democracy 
~497. Learning Bounds for Greedy Approximation with Multiple Explicit Feature Maps
~498. Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
~499. Adversarial Text Generation via Feature-Mover's Distance
~500. Boolean Decision Rules via Column Generation

-----------500 papers-----------


>~501. On Learning Intrinsic Rewards for Policy Gradient Methods
~502. Spectral Filtering for General Linear Dynamical Systems
~503. PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits
~504. Optimal Byzantine-Resilient Stochastic Gradient Descent
~505. Learning filter widths of spectral decompositions with wavelets
~506. Active Matting
~507. Towards Robust Detection of Adversarial Examples
~508. How SGD selects the global minima in over-parameterized learning: A stability perspective
~509. The promises and pitfalls of Stochastic Gradient Langevin Dynamics
~510. Online Reciprocal Recommendation with Theoretical Performance Guarantees
~511. Algorithms and Theory for Multiple-Source Adaptation
~512. Efficient Online Portfolio with Logarithmic Regret
~513. Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion
~514. Variational Bayesian Monte Carlo
~515. Statistical mechanics of low-rank tensor decomposition
~516. Sequential Monte Carlo for probabilistic graphical models via twisted targets
~517. Modelling and unsupervised learning of symmetric deformable object categories
~518. Hamiltonian Variational Auto-Encoder
~519. Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
~520. Bayesian Control of Large MDPs with Uncertain Dynamics in Data-Poor Environments
~521. Proximal Graphical Event Models
~522. Does mitigating ML's impact disparity require treatment disparity?
~523. Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes
~524. Credit Assignment For Collective Multiagent RL With Global Rewards
~525. A Lyapunov-based Approach to Safe Reinforcement Learning
~526. Learning to Specialize with Knowledge Distillation for Visual Question Answering
~527. Efficient Anomaly Detection via Matrix Sketching
~528. Dendritic Neural Network with Great Expressive Power
~529. Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training
~530. Neural Arithmetic Logic Units
~531. Approximate Knowledge Compilation by Online Collapsed Importance Sampling
~532. Reward learning from human preferences and demonstrations in Atari
~533. Spectral Signatures in Backdoor Attacks on Deep Nets
~534. The challenge of realistic music generation: modelling raw audio at scale
~535. Submodular Maximization via Gradient Ascent: The Case of Deep Submodular   Functions
~536. Stochastic Expectation Maximization with Variance Reduction
~537. Dirichlet belief networks as structured topic prior
~538. Layer-Wise Coordination between Encoder and Decoder for Neural Machine Translation
~539. Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
~540. Monte-Carlo Tree Search for Constrained POMDPs
~541. Robust Detection of Adversarial Attacks by Modeling the Intrinsic Properties of Deep Neural Networks
~542. Robust Hypothesis Testing Using Wasserstein Uncertainty Sets
~543. RenderNet: A deep convolutional network for differentiable rendering from 3D shapes
~544. Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
~545. A Reduction for Efficient LDA Topic Reconstruction
~546. A General Method for Amortizing Variational Filtering
~547. Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo
~548. Distributed $k$-Clustering for Data with Heavy Noise
~549. Preference Based Adaptation for Learning Objectives
~550. Neural Architecture Optimization
~551. Learning Libraries of Subroutines for Neurally–Guided Bayesian Program Learning
~552. Constrained Graph Variational Autoencoders for Molecule Design
~553. Deep State Space Models for Time Series Forecasting
~554. Towards Robust Interpretability with Self-Explaining Neural Networks
~555. Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization
~556. Learning Loop Invariants for Program Verification
~557. Breaking the Activation Function Bottleneck through Adaptive Parameterization
~558. On Neuronal Capacity
~559. Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
~560. Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
~561. Understanding Batch Normalization
~562. Scalar Posterior Sampling with Applications 
~563. Training Deep Neural Networks with 8-bit Floating Point Numbers
~564. Depth-Limited Solving for Imperfect-Information Games
~565. Communication Compression for Decentralized Training
~566. Sparse Attentive Backtracking: Temporal Credit Assignment Through Reminding
~567. Improved Algorithms for Collaborative PAC Learning
~568. Rectangular Bounding Process
~569. VideoCapsuleNet: A Simplified Network for Action Detection
~570. Edward2: Simple, Dynamic, Accelerated
~571. Diffusion Maps for Textual Network Embedding
~572. Blackbox Matrix×Matrix Gaussian Process Inference
~573. cpSGD: Communication-efficient and differentially-private distributed SGD
~574. Towards Text Generation with Adversarially Learned Neural Outlines
~575. Generalisation in humans and deep neural networks
~576. Non-Adversarial Mapping with VAEs
~577. Knowledge Distillation by On-the-Fly Native Ensemble
~578. Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
~579. Generative modeling for protein structures
~580. Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks
~581. Adaptive Learning with Unknown Information Flows
~582. Multi-Agent Generative Adversarial Imitation Learning
~583. Plug-in Estimation in High-Dimensional Linear Inverse Problems: A Rigorous Analysis
~584. A Bayesian Approach to Generative Adversarial Imitation Learning
~585. Constant Regret, Generalized Mixability, and Mirror Descent
~586. Hunting for Discriminatory Proxies in Linear Regression Models
~587. Adaptive Sampling Towards Fast Graph Representation Learning
~588. MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
~589. COLA: Decentralized Linear Learning
~590. Third-order Smoothness Helps: Faster Stochastic Optimization Algorithms for Finding Local Minima
~591. Explaining Deep Learning Models -- A Bayesian Non-parametric Approach
~592. Lifelong Inverse Reinforcement Learning
~593. Expanding Holographic Embeddings for Knowledge Completion
~594. Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
~595. Importance Weighting and Varational Inference
~596. Exponentiated Strongly Rayleigh Distributions
~597. Sparsified SGD with Memory
~598. End-to-end Symmetry Preserving Inter-atomic Potential Energy Model for Finite and Extended Systems
~599. Semi-Supervised Learning with Declaratively Specified Entropy Constraints
~600. Limited Memory Kelley's Method Converges for Composite Convex and Submodular Objectives

-----------600 papers-----------


>~601. Maximum Causal Tsallis Entropy Imitation Learning
~602. Amortized Inference Regularization
~603. Top-k lists: Models and Algorithms
~604. The Physical Systems Behind Optimization Algorithms
~605. Mean-field theory of graph neural networks in graph partitioning
~606. Adding One Neuron Can Eliminate All Bad Local Minima
~607. Optimization of Smooth Functions with Noisy Observations: Local Minimax Rates
~608. Completing State Representations using Spectral Learning
~609. A Bridging Framework for Model Optimization and Deep Propagation
~610. Submodular Field Grammars: Representation, Inference, and Application to Image Parsing
~611. Differentially Private Contextual Linear Bandits
~612. SimplE Embedding for Link Prediction in Knowledge Graphs
~613. Binary Rating Estimation with Graph Side Information
~614. Can We Gain More from Orthogonality Regularizations in Training Deep Networks?
~615. Inexact trust-region algorithm on Riemannian manifolds
~616. BML: A High-performance, Low-cost Gradient Synchronization Algorithm for DML Training
~617. Integrated accounts of behavioral and neuroimaging data using flexible recurrent neural network models
~618. Scalable Coordinated Exploration in Concurrent Reinforcement Learning
~619. Differentially Private Uniformly Most Powerful Tests for Binomial Data
~620. Bilevel Distance Metric Learning for Robust Image Recognition
~621. Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
~622. The Price of Privacy for Low-rank Factorization
~623. Flexible and accurate inference and learning for deep generative models
~624. An Information-Theoretic Analysis of Thompson Sampling for Large Action Spaces
~625. Meta-Learning MCMC Proposals
~626. Differentially Private Robust PCA
~627. JCNN-sLDA: Joint constraint neural networks (JCNN), a novel factored neural network structure with applications to supervised text classification
~628. TETRIS: TilE-matching the TRemendous Irregular Sparsity
~629. Efficient Projection onto the Perfect Phylogeny Model
~630. Parallel Weight Consolidation: A Brain Segmentation Case Study
~631. Beauty-in-averageness and its contextual modulations: A Bayesian statistical account
~632. Neural Networks Trained to Solve Differential Equations Learn General Representations
~633. GumBolt: Extending Gumbel trick to Boltzmann priors
~634. KONG: Kernels for ordered-neighborhood graphs
~635. The streaming rollout of deep networks - towards fully model-parallel execution
~636. Probabilistic Neural Programmed Networks for Scene Generation
~637. Conditional Image Generation for Learning the Structure of Visual Objects
~638. Heterogeneous Bitwidth Binarization in Convolutional Neural Networks
~639. Solving Non-smooth Constrained Programs with Lower Complexity than $\mathcal{O}(1/\varepsilon)$: A Primal-Dual Homotopy Smoothing Approach
~640. Early Stopping for Nonparametric Testing 
~641. Deep Generative Markov State Models
~642. RetGK: Graph Kernels based on Return Probabilities of Random Walks
~643. Learning from discriminative feature feedback
~644. TopRank: A practical algorithm for online stochastic ranking
~645. Faster Neural Networks Straight from JPEG
~646. Stochastic Nested Variance Reduced Gradient Descent for Nonconvex Optimization
~647. Adversarial Examples that Fool both Computer Vision and Time-Limited Humans
~648. Direct Runge-Kutta Discretization Achieves Acceleration
~649. Faster Online Learning of Optimal Threshold for  Consistent F-measure Optimization
~650. Learning sparse neural networks via sensitivity-driven regularization
~651. Bipartite Stochastic Block Models with Tiny Clusters
~652. Leveraging the Exact Likelihood of Deep Latent Variable Models
~653. Minimax Estimation of Neural Net Distance
~654. Lipschitz regularity of deep neural networks: analysis and efficient estimation
~655. Acceleration through Optimistic No-Regret Dynamics
~656. Data center cooling using model-predictive control
~657. Bayesian Inference of Temporal Task Specifications from Demonstrations
~658. Variational PDEs for Acceleration on Manifolds and Application to Diffeomorphisms
~659. Sublinear Time Low-Rank Approximation of Distance Matrices
~660. Direct Estimation of Differences in Causal Graphs
~661. Convergence of Cubic Regularization for Nonconvex Optimization under KL Property
~662. DeepProbLog:  Neural Probabilistic Logic Programming
~663. Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting
~664. Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization
~665. NEON 2: Finding Local Minima via First-Order Oracles
~666. Inferring Networks From Random Walk-Based Node Similarities
~667. Unsupervised Attention-guided Image-to-Image Translation
~668. Towards Understanding Acceleration Tradeoff between Momentum and Asynchrony in Nonconvex Stochastic Optimization
~669. Equality of Opportunity in Classification: A Causal Approach
~670. A Bandit Approach to Sequential Experimental Design with False Discovery Control
~671. Optimal Subsampling with Influence Functions
~672. Adversarial Attacks on Stochastic Bandits
~673. Escaping Saddle Points in Constrained Optimization
~674. Modern Neural Networks Generalize on Small Data Sets
~675. BinGAN: Learning Compact Binary Descriptors with a Regularized GAN
~676. Tight Bounds for Collaborative PAC Learning via Multiplicative Weights
~677. Neural Code Comprehension: A Learnable Representation of Code Semantics
~678. Communication Efficient Parallel Algorithms for Optimization on Manifolds
~679. Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning
~680. Multi-Layered Gradient Boosting Decision Trees
~681. Why Is My Classifier Discriminatory?
~682. Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games
~683. Scaling the Poisson GLM to massive neural datasets through polynomial approximations
~684. Sequence-to-Segment Networks for Segment Detection
~685. Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization
~686. Infinite-Horizon Gaussian Processes
~687. Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
~688. Latent Gaussian Activity Propagation: Using Smoothness and Structure to Separate and Localize Sounds in Large Noisy Environments
~689. Zeroth-order (Non)-Convex Stochastic Optimization via Conditional Gradient and Gradient Updates
~690. Derivative Estimation in Random Design
~691. Step Size Matters in Deep Learning
~692. Actor-Critic Policy Optimization in PartiallyObservable Multiagent Environments
~693. Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
~694. Boosting Black Box Variational Inference
~695. Learning to Optimize Tensor Programs
~696. But How Does It Work in Theory? Linear SVM with Random Features
~697. Recurrent Relational Networks
~698. Stochastic Spectral and Conjugate Descent Methods
~699. High-dimensional Bayesian optimization via collaborative filtering
~700. Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds

-----------700 papers-----------


>~701. Inequity aversion improves cooperation in intertemporal social dilemmas
~702. Speaker-Follower Models for Vision-and-Language Navigation
~703. Data-Efficient Hierarchical Reinforcement Learning
~704. Multivariate Convolutional Sparse Coding for Electromagnetic Brain Signals
~705. Deep, complex networks for inversion of transmission effects in multimode optical fibres
~706. Re-evaluating evaluation
~707. Training deep learning based denoisers without ground truth data
~708. Contextual Combinatorial Multi-armed Bandits with Volatile Arms and Submodular Reward
~709. Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
~710. The committee machine: Computational to statistical gaps in learning a two-layers neural network
~711. Semi-crowdsourced Clustering withDeep Generative Models
~712. Single-Agent Policy Tree Search With Guarantees
~713. Parsimonious Bayesian deep networks
~714. Evidential Deep Learning to Quantify Classification Uncertainty
~715. Deep Reinforcement Learning of Marked Temporal Point Processes
~716. The Nearest Neighbor Information Estimator is Adaptively Near Minimax Rate-Optimal
~717. Learning latent variable structured prediction models with Gaussian perturbations
~718. Efficiency of adaptive importance sampling
~719. Global Convergence of Langevin Dynamics Based Algorithms for Nonconvex Optimization
~720. Q-learning with Nearest Neighbors
~721. Near-Optimal Policies for Dynamic Multinomial Logit Assortment Selection Models
~722. On Binary Classification in Extreme Regions
~723. From Stochastic Planning to Marginal MAP
~724. Faithful Inversion of Generative Models for Effective Amortized Inference
~725. Weakly Supervised Dense Event Captioning in Videos
~726. Constructing Deep Neural Networks by Bayesian Network Structure Learning
~727. On the Global Convergence of Gradient Descent for Over-parameterized Models using Optimal Transport
~728. NAIS-Net: Stable Deep Networks from Non-Autonomous  Differential Equations
~729. Practical Methods for Graph Two-Sample Testing
~730. Optimistic Optimization of a Brownian
~731. Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
~732. When do random forests fail?
~733. Fast Estimation of Causal Interactions using Wold Processes
~734. Optimization over Continuous and Multi-dimensional Decisions with Observational Data
~735. Norm-Ranging LSH for Maximum Inner Product Search
~736. Dialog-to-Action: Conversational Question Answering over Large-Scale Knowledge Base
~737. Playing hard exploration games by watching YouTube
~738. Differentially Private Bayesian Inference for Exponential Families
~739. Adaptation to Easy Data in Prediction with Limited Advice
~740. Stochastic Cubic Regularization for Fast Nonconvex Optimization
~741. Moonshine: Distilling with Cheap Convolutions
~742. Mirrored Langevin Dynamics
~743. Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization
~744. Metric on Nonlinear Dynamical Systems with Koopman Operators
~745. Delta-encoder: an effective sample synthesis method for few-shot object recognition
~746. Factored Bandits
~747. Gradient Descent Meets Shift-and-Invert Preconditioning for Eigenvector Computation
~748. Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
~749. Unsupervised Learning of Shape and Pose with Differentiable Point Clouds
~750. Empirical Risk Minimization Under Fairness Constraints
~751. Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation
~752. Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
~753. Paraphrasing Complex Network: Network Compression via Factor Transfer
~754. Computing Higher Order Derivatives of Matrix and Tensor Expressions
~755. Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
~756. Safe Active Learning for Time-Series Modeling with Gaussian Processes
~757. Processing of missing data by neural networks
~758. Learning Hierarchical Semantic Image Manipulation through Structured Representations
~759. Provable Variational Inference for Constrained Log-Submodular Models
~760. Minimax Statistical Learning with Wasserstein distances
~761. Natasha 2: Faster Non-Convex Optimization Than SGD
~762. Causal Inference on Discrete Data using Hidden Compact Representation
~763. Out of the Box: Reasoning with Graph Convolution Nets for Factual Visual Question Answering
~764. Representation Balancing MDPs for Off-policy Policy Evaluation
~765. Representation Learning for Treatment Effect Estimation from Observational Data
~766. Contextual bandits with surrogate losses: Margin bounds and efficient algorithms
~767. Isolating Sources of Disentanglement in Variational Autoencoders
~768. Online Learning with an Unknown Fairness Metric
~769. A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation
~770. Answerer in Questioner's Mind: Information Theoretic Approach to Goal-Oriented Visual Dialog
~771. Structural Causal Bandits: Where to Intervene?
~772. Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks
~773. Tree-to-tree Neural Networks for Program Translation
~774. Active Learning for Non-Parametric Regression Using Purely Random Trees
~775. A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
~776. Supervised Local Modeling for Interpretability
~777. Leveraged volume sampling for linear regression
~778. Verifiable Reinforcement Learning via Policy Extraction
~779. How Does Batch Normalization Help Optimization? (No, It Is Not About Internal Covariate Shift)
~780. Wasserstein Variational Inference
~781. Ridge Regression and Provable Deterministic Ridge Leverage Score Sampling
~782. Recurrent World Models Facilitate Policy Evolution
~783. A theory on the absence of spurious optimality
~784. Query Complexity of Bayesian Private Learning
~785. Learning to Navigate in Cities Without a Map
~786. Modular Networks: Learning to Decompose Neural Computation
~787. Meta-Gradient Reinforcement Learning
~788. Gaussian Process Conditional Density Estimation
~789. Local Differential Privacy for Evolving Data
~790. MetaGAN: An Adversarial Approach to Few-Shot Learning
~791. Non-monotone Submodular Maximization in Exponentially Fewer Iterations
~792. Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
~793. GIANT: Globally Improved Approximate Newton Method for Distributed Optimization
~794. Structured Local Minima in Sparse Blind Deconvolution
~795. Breaking the Span Assumption Yields Fast Finite-Sum Minimization
~796. Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate
~797. GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
~798. Smoothed analysis of the low-rank approach for smooth semidefinite programs
~799. BourGAN: Generative Networks with Metric Embeddings
~800. On the Generalization of Single-View 3D Reconstruction Algorithms

-----------800 papers-----------


>~801. A Practical Algorithm for Distributed Clustering and Outlier Detection
~802. Unsupervised Adversarial Invariance
~803. Active Geometry-Aware Visual Recognition in Cluttered Scenes
~804. Power-law efficient neural codes provide general link between perceptual bias and discriminability
~805. Revisiting Decomposable Submodular Function Minimization with Incidence Relations
~806. A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
~807. The Description Length of Deep Learning models
~808. Trajectory Convolution for Action Recognition
~809. Mixture Matrix Completion
~810. MULAN: A Blind and Off-Grid Method for Multichannel Echo Retrieval
~811. Dual Principal Component Pursuit: Improved Analysis and Efficient Algorithms
~812. Norm matters: efficient and accurate normalization schemes in deep networks
~813. DeepExposure: Learn to Expose Photos with Asynchronously Reinforced Adversarial Learning
~814. Algorithmic Linearly Constrained Gaussian Processes
~815. Overlapping Clustering, and One (class) SVM to Bind Them All
~816. Regularizing by the Variance of the Activations' Sample-Variances
~817. One-Shot Unsupervised Cross Domain Translation
~818. Automatic Program Synthesis of Long Programs with a Learned Garbage Collector
~819. SEGA: Variance Reduction via Gradient Sketching
~820. Nonparametric learning for Bayesian models via randomized objective functions
~821. Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
~822. Sequential Context Encoding for Duplicate Removal
~823. Learning Optimal Reserve Price against Non-myopic Bidders
~824. Querying Complex Networks in Vector Space
~825. Neural Architecture Search with Bayesian Optimisation and Optimal Transport
~826. Generalized Zero-Shot Learning with Deep Calibration Network
~827. SplineNets: Continuous Neural Decision Graphs
~828. Efficient Stochastic Gradient Hard Thresholding
~829. Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors
~830. Universal Growth in Production Economies
~831. Pelee: A Real-Time Object Detection System on Mobile Devices
~832. Attention in Convolutional LSTM for Gesture Recognition
~833. Virtual Class Enhanced Discriminative Embedding Learning
~834. Deep Attentive Tracking via Reciprocative Learning
~835. Evaluating Range-Based Anomaly Detectors
~836. Distributed Stochastic Optimization via Adaptive SGD
~837. Random Feature Stein Discrepancies
~838. 3D-Aware Scene Manipulation via Inverse Graphics
~839. Partially-Supervised Image Captioning
~840. DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
~841. Symbolic Graph Reasoning Meets Convolutions
~842. High Dimensional Linear Regression using Lattice Basis Reduction
~843. Collaborative Learning for Deep Neural Networks
~844. Entropy and mutual information in models of deep neural networks
~845. Generating Informative and Diverse Conversational Responses via Adversarial Information Maximization
~846. Simple random search of static linear policies is competitive for reinforcement learning
~847. The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
~848. Temporal Regularization for Markov Decision Process
~849. Enhancing the Accuracy and Fairness of Human Decision Making
~850. Fighting Boredom in Recommender Systems with Linear Reinforcement Learning
~851. Genetic-Gated Networks for Deep Reinforcement Learning
~852. Neural Guided Constraint Logic Programming for Program Synthesis
~853. Learning to Exploit Stability for 3D Scene Parsing
~854. Distilled Wasserstein Learning for Word Embedding and Topic Modeling
~855. Video Prediction via Selective Sampling
~856. Foreground Clustering for Joint Segmentation and Localization in Videos and Images
~857. Bayesian Semi-supervised Learning with Graph Gaussian Processes
~858. Non-Local Recurrent Network for Image Restoration
~859. Relating Leverage Scores and Density using Regularized Christoffel Functions
~860. Neighbourhood Consensus Networks
~861. Conditional Adversarial Domain Adaptation
~862. DifNet: Semantic Segmentation by Diffusion Networks
~863. Accelerated Stochastic Matrix Inversion:  General Theory and  Speeding up BFGS Rules for Faster Second-Order Optimization
~864. Learning Versatile Filters for Efficient Convolutional Neural Networks
~865. Multivariate Time Series Imputation with Generative Adversarial Networks
~866. Multi-Class Learning: From Theory to Algorithm
~867. Parsimonious Quantile Regression of Asymmetrically Heavy-tailed Financial Return Series
~868. Bilinear Attention Networks
~869. Hybrid Knowledge Routed Modules for Large-scale Object Detection
~870. Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
~871. Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation
~872. Stochastic Composite Mirror Descent: Optimal Bounds with High Probabilities
~873. Variational Memory Encoder-Decoder
~874. PacGAN: The power of two samples in generative adversarial networks
~875. A loss framework for calibrated anomaly detection
~876. Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners
~877. Designing by Training: Acceleration Neural Network for Fast High-Dimensional Convolution
~878. Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behavior
~879. Generalizing Tree Probability Estimation via Bayesian Networks
~880. Gradient Descent for Spiking Neural Networks
~881. On Oracle-Efficient PAC RL with Rich Observations
~882. SLAYER: Spike Layer Error Reassignment in Time
~883. Geometry Based Data Generation
~884. Multitask Boosting for Survival Analysis with Competing Risks
~885. Regularization Learning Networks
~886. Joint Active Feature Acquisition and Classification with Variable-Size Set Encoding
~887. Found Graph Data and Planted Vertex Covers
~888. Generative Neural Machine Translation
~889. Improving Word Embedding by Adversarial Training
~890. Adaptive Online Learning in Dynamic Environments
~891. Revisiting Multi-Task Learning with ROCK: a Deep Residual Auxiliary Block for Visual Detection
~892. Gradient Sparsification for Communication-Efficient Distributed Optimization
~893.  Image-to-image translation for cross-domain disentanglement
~894. Global Gated Mixture of Second-order Pooling for Improving Deep Convolutional Neural Networks
~895. Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
~896. Unsupervised Learning of View-invariant Action Representations
~897. The Lingering of Gradients: How to Reuse Gradients Over Time
~898. New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity
~899. FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
~900. Alternating optimization of decision trees, with application to learning sparse oblique trees

-----------900 papers-----------


>~901. Toddler-Inspired Visual Object Learning
~902. Evolutionary Reinforcement Learning
~903. Robustness of classifiers under generative models
~904. Synthesize Policies for Transfer and Adaptation across Environments and Tasks
~905. How To Make the Gradients Small Stochastically
~906. Video-to-Video Synthesis
~907. Global Geometry of Multichannel Sparse Blind Deconvolution on the Sphere
~908. Interactive Structure Learning with Structural Query-by-Committee
~909. A Game-Theoretic Approach to Recommendation Systems with Strategic Content Providers
~910. Efficient nonmyopic batch active search
~911. Neural Nearest Neighbors Networks for Image Restoration
~912. $\ell_1$-regression with Heavy-tailed Distributions
~913. A Block Coordinate Ascent Algorithm for Mean-Variance Optimization
~914. Quadratic Decomposable Submodular Function Minimization
~915. Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
~916. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
~917. Domain-Invariant Projection Learning for Zero-Shot Recognition
~918. Boosted Sparse and Low-Rank Tensor Regression
~919. MetaReg: Towards Domain Generalization using Meta-Regularization
~920. Learning semantic similarity in a continuous space
~921. Low-shot Learning via Covariance-Preserving Adversarial Augmentation Network
~922. Empirical Risk Minimization in Non-interactive Local Differential Privacy Revisited
~923. A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents
~924. A flexible model for training action localization with varying levels of supervision
~925. Posterior Concentration for Sparse Deep Learning
~926. DropMax: Adaptive Variational Softmax
~927. Uncertainty-Aware Attention for Reliable Interpretation and Prediction
~928. Reinforced Continual Learning
~929. On Word Embedding Dimensionality
~930. Discrimination-aware Channel Pruning for Deep Neural Networks
~931. Solving Large Sequential Games with the Excessive Gap Technique
~932. Generalizing Graph Matching beyond Quadratic Assignment Model
~933. Large Margin Deep Networks for Classification
~934. Connectionist Temporal Classification with Maximum Entropy Regularization
~935. PointCNN
~936. Informative Features for Model Comparison
~937. Greedy Hash: Towards Fast Optimization for Accurate Hash Coding in CNN
~938. Long short-term memory and Learning-to-learn in networks of spiking neurons
~939. Distilling Knowledge with Adversarial Networks
~940. Visual Memory for Robust Path Following
~941. FishNet: the Beauty of Feature Preservation and Refinement
~942. Deep Neural Nets with Interpolating Function as Output Activation
~943. Sparse Covariance Modeling in High Dimensions with Gaussian Processes
~944. Do Less, Get More: Streaming Submodular Maximization with Subsampling
~945. Improved few-shot learning with task conditioning and metric scaling
~946. Learning Disentangled Joint Continuous and Discrete Representations
~947. Are GANs Created Equal? A Large-Scale Study
~948. Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator
~949. Dialog-based Interactive Image Retrieval
~950. Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
~951. A Neural Compositional Paradigm for Image Captioning
~952. On Learning Markov Chains
~953. Maximum-Entropy Fine Grained Classification
~954. Removing the Feature Correlation Effect of Multiplicative Noise
~955. A Unified Framework for Extensive-Form Game Abstraction with Bounds
~956. HitNet: Hybrid Ternary Recurrent Neural Network
~957. Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives
~958. Which Neural Net Architectures Give Rise to Exploding and Vanishing Gradients?
~959. How to Start Training: The Effect of Initialization and Architecture
~960. LinkNet: Relational Embedding for Scene Graph
~961. Self-Handicapping Network for Integral Object Attention
~962. Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
~963. Multi-Task Learning as Multi-Objective Optimization
~964. Learning to Decompose and Disentangle Representations for Video Prediction
~965. Are ResNets Provably Better than Linear Predictors?
~966. Nonlocal Neural Networks, Nonlocal Diffusion and Nonlocal Modeling
~967. Deep Functional Dictionaries: Learning Consistent Semantic Structures on 3D Models from Functions
~968. Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesis
~969. A Model for Learned Bloom Filters and Optimizing by Sandwiching
~970. Training DNNs with Hybrid Block Floating Point
~971. Implicit Reparameterization Gradients
~972. Rest-Katyusha: Exploiting the Solution's Structure via Scheduled Restart Schemes
~973. Deep Defense: Training DNNs with Improved Adversarial Robustness
~974. (Probably) Concave Graph Matching
~975. Optimization for Approximate Submodularity
~976. Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced
~977. How Many Samples are Needed to Learn a Convolutional Neural Network?
~978. Self-Supervised Generation of Spatial Audio for 360-degree Video
~979. A^2-Nets: Double Attention Networks
~980. On Misinformation Containment in Online Social Networks
~981. Image Inpainting via Generative Multi-column Convolutional Neural Networks
~982. MetaAnchor: Learning to Detect Objects with Customized Anchors
~983. Probabilistic Pose Graph Optimization via Bingham Distributions and Tempered Geodesic MCMC
~984. Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation
~985. Sigsoftmax: Reanalysis of the Softmax Bottleneck
~986. Chain of Reasoning for Visual Question Answering
~987. See and Think: Disentangling Semantic Scene Completion
~988. Snap ML: A Hierarchical Framework for Machine Learning
~989. Sparse DNNs with Improved Adversarial Robustness
~990. PAC-learning in the presence of adversaries
~991. An Efficient Pruning Algorithm for Robust Isotonic Regression
~992. Cooperative Holistic 3D Scene Understanding from a Single RGB Image
~993. Geometrically Coupled Monte Carlo Sampling
~994. Learning Deep Disentangled Embeddings With the F-Statistic Loss
~995. Fast Similarity Search via Optimal Sparse Lifting
~996. Joint Sub-bands Learning with Clique Structures for Wavelet Domain Super-Resolution
~997. Learning long-range spatial dependencies with horizontal gated-recurrent units
~998. Learning Pipelines with Limited Data and Domain Knowledge: A Study in Parsing Physics Problems
~999. Understanding Weight Normalized Deep Neural Networks with Rectified Linear Units
~1000. Visual Object Networks: Natural Image Generation with Disentangled 3D Representation

-----------1000 papers-----------

>~1001. Supervised autoencoders: Improving generalization performance with unsupervised regularizers
~1002. An Off-policy Policy Gradient Theorem Using Emphatic Weightings
~1003. Generalized Inverse Optimization through Online Learning
~1004. Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning
~1005. Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with $\beta$-Divergences
~1006. IntroVAE: Introspective Variational Autoencoders for Photographic Image Synthesis
~1007. Text-Adaptive Generative Adversarial Networks: Manipulating Images with Natural Language
~1008. HOGWILD!-Gibbs can be PanAccurate
~1009. Kalman Normalization
~1010. Structure-Aware Convolutional Neural Networks
~1011. Efficient Algorithms for Non-convex Isotonic Regression through Submodular Optimization
~1012. Automatic Machine Learning
~1013. Adversarial Robustness: Theory and Practice
~1014. Statistical Learning Theory: a Hitchhiker's Guide
~1015. Negative Dependence, Stable Polynomials, and All That
~1016. Unsupervised Deep Learning
~1017. Visualization for Machine Learning
~1018. Scalable Bayesian Inference
~1019. Common Pitfalls for Studying the Human Side of Machine Learning
~1020. Counterfactual Inference
~1021. 2nd Workshop on Machine Learning on the Phone and other Consumer Devices (MLPCD 2)
~1022. Modeling and decision-making in the spatiotemporal domain
~1023. Workshop on Security in Machine Learning
~1024. Continual Learning
~1025. NIPS 2018 workshop on Compact Deep Neural Networks with industrial applications
~1026. Machine Learning for Geophysical & Geochemical Signals
~1027. Visually grounded interaction and language
~1028. Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy
~1029. Critiquing and Correcting Trends in Machine Learning
~1030. Deep Reinforcement Learning
~1031. All of Bayesian Nonparametrics (Especially the Useful Bits)
~1032. MLSys: Workshop on Systems for ML and Open Source Software
~1033. Imitation Learning and its Challenges in Robotics
~1034. NIPS 2018 Competition Track Day 1
~1035. The second Conversational AI workshop – today's practice and tomorrow's potential
~1036. Modeling the Physical World: Learning, Perception, and Control
~1037. Smooth Games Optimization and Machine Learning
~1038. Bayesian Deep Learning
~1039. Causal Learning
~1040. Workshop on Ethical, Social and Governance Issues in AI
~1041. NIPS Workshop on Machine Learning for Intelligent Transportation Systems 2018
~1042. Relational Representation Learning
~1043. Machine Learning for Molecules and Materials
~1044. Second Workshop on Machine Learning for Creativity and Design
~1045. CiML 2018 - Machine Learning competitions "in the wild": Playing in the real world or in real time
~1046. Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare
~1047. Infer to Control: Probabilistic Reinforcement Learning and Structured Control
~1048. Emergent Communication Workshop
~1049. Learning by Instruction
~1050. NIPS 2018 Workshop on Meta-Learning
~1051. Interpretability and Robustness in Audio, Speech, and Language
~1052. Machine Learning Open Source Software 2018: Sustainable communities
~1053. Integration of Deep Learning Theories
~1054. Wordplay: Reinforcement and Language Learning in Text-based Games
~1055. AI for social good
~1056. Privacy Preserving Machine Learning
~1057. Reinforcement Learning under Partial Observability
~1058. Machine Learning for the Developing World (ML4D): Achieving sustainable impact
~1059. NIPS 2018 Competition Track Day 2
~1060. Medical Imaging meets NIPS
~1061. Machine Learning for Systems

这里是一个统计图表, 以及一些总结的论文下载

同时还给出了workshop介绍,对应清单和链接请看link