NeurIPS(NIPS) 2017 论文列表
Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA.
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Multi-view Matrix Factorization for Linear Dynamical System Estimation.
A General Framework for Robust Interactive Learning.
On clustering network-valued data.
Maxing and Ranking with Few Assumptions.
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models.
Stochastic Mirror Descent in Variationally Coherent Optimization Problems.
Learning to Model the Tail.
Experimental Design for Learning Causal Graphs with Latent Variables.
Active Learning from Peers.
When Cyclic Coordinate Descent Outperforms Randomized Coordinate Descent.
Efficient and Flexible Inference for Stochastic Systems.
Houdini: Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples.
Real Time Image Saliency for Black Box Classifiers.
Convergence of Gradient EM on Multi-component Mixture of Gaussians.
Kernel Feature Selection via Conditional Covariance Minimization.
Efficient Approximation Algorithms for Strings Kernel Based Sequence Classification.
Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference.
Predicting Scene Parsing and Motion Dynamics in the Future.
A Meta-Learning Perspective on Cold-Start Recommendations for Items.
Linear Time Computation of Moments in Sum-Product Networks.
A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening.
Unsupervised Transformation Learning via Convex Relaxations.
Affinity Clustering: Hierarchical Clustering at Scale.
Stochastic Submodular Maximization: The Case of Coverage Functions.
Cross-Spectral Factor Analysis.
Style Transfer from Non-Parallel Text by Cross-Alignment.
An Error Detection and Correction Framework for Connectomics.
Kernel functions based on triplet comparisons.
Acceleration and Averaging in Stochastic Descent Dynamics.
Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise.
Inverse Reward Design.
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process.
Subspace Clustering via Tangent Cones.
Neural Variational Inference and Learning in Undirected Graphical Models.
Learning Hierarchical Information Flow with Recurrent Neural Modules.
Z-Forcing: Training Stochastic Recurrent Networks.
Learning Linear Dynamical Systems via Spectral Filtering.
Neural Expectation Maximization.
Spectral Mixture Kernels for Multi-Output Gaussian Processes.
Few-Shot Adversarial Domain Adaptation.
The power of absolute discounting: all-dimensional distribution estimation.
Approximation Algorithms for l0-Low Rank Approximation.
The Scaling Limit of High-Dimensional Online Independent Component Analysis.
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium.
Practical Hash Functions for Similarity Estimation and Dimensionality Reduction.
Learning Mixture of Gaussians with Streaming Data.
Modulating early visual processing by language.
On Frank-Wolfe and Equilibrium Computation.
Filtering Variational Objectives.
Random Projection Filter Bank for Time Series Data.
Towards Generalization and Simplicity in Continuous Control.
Solid Harmonic Wavelet Scattering: Predicting Quantum Molecular Energy from Invariant Descriptors of 3D Electronic Densities.
Protein Interface Prediction using Graph Convolutional Networks.
On Blackbox Backpropagation and Jacobian Sensing.
Good Semi-supervised Learning That Requires a Bad GAN.
Clustering Stable Instances of Euclidean k-means.
Semisupervised Clustering, AND-Queries and Locally Encodable Source Coding.
Effective Parallelisation for Machine Learning.
Gradient Episodic Memory for Continual Learning.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity.
Causal Effect Inference with Deep Latent-Variable Models.
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons.
Matrix Norm Estimation from a Few Entries.
When Worlds Collide: Integrating Different Counterfactual Assumptions in Fairness.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.
Communication-Efficient Distributed Learning of Discrete Distributions.
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments.
Learning with Bandit Feedback in Potential Games.
Robust Conditional Probabilities.
Learning Combinatorial Optimization Algorithms over Graphs.
Poincaré Embeddings for Learning Hierarchical Representations.
Scalable Log Determinants for Gaussian Process Kernel Learning.
Generalizing GANs: A Turing Perspective.
Neural Discrete Representation Learning.
Learned in Translation: Contextualized Word Vectors.
Boltzmann Exploration Done Right.
Scalable Planning with Tensorflow for Hybrid Nonlinear Domains.
Population Matching Discrepancy and Applications in Deep Learning.
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes.
Spectrally-normalized margin bounds for neural networks.
The Expressive Power of Neural Networks: A View from the Width.
Statistical Cost Sharing.
Invariance and Stability of Deep Convolutional Representations.
Hierarchical Clustering Beyond the Worst-Case.
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls.
Asynchronous Coordinate Descent under More Realistic Assumptions.
Countering Feedback Delays in Multi-Agent Learning.
Optimal Shrinkage of Singular Values Under Random Data Contamination.
Implicit Regularization in Matrix Factorization.
Efficient Second-Order Online Kernel Learning with Adaptive Embedding.
A Learning Error Analysis for Structured Prediction with Approximate Inference.
Value Prediction Network.
Gaussian Quadrature for Kernel Features.
Estimating High-dimensional Non-Gaussian Multiple Index Models via Stein's Lemma.
Convolutional Phase Retrieval.
SVCCA: Singular Vector Canonical Correlation Analysis for Deep Learning Dynamics and Interpretability.
Early stopping for kernel boosting algorithms: A general analysis with localized complexities.
Predictive State Recurrent Neural Networks.
Unbounded cache model for online language modeling with open vocabulary.
Bregman Divergence for Stochastic Variance Reduction: Saddle-Point and Adversarial Prediction.
Parameter-Free Online Learning via Model Selection.
Recurrent Ladder Networks.
Attention is All you Need.
Estimating Mutual Information for Discrete-Continuous Mixtures.
Action Centered Contextual Bandits.
Fader Networks: Manipulating Images by Sliding Attributes.
A framework for Multi-A(rmed)/B(andit) Testing with Online FDR Control.
Exploring Generalization in Deep Learning.
Self-Supervised Intrinsic Image Decomposition.
Learning Disentangled Representations with Semi-Supervised Deep Generative Models.
Fast-Slow Recurrent Neural Networks.
Collaborative Deep Learning in Fixed Topology Networks.
A KL-LUCB algorithm for Large-Scale Crowdsourcing.
Learning Neural Representations of Human Cognition across Many fMRI Studies.
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos.
The Importance of Communities for Learning to Influence.
Smooth Primal-Dual Coordinate Descent Algorithms for Nonsmooth Convex Optimization.
Gradient Methods for Submodular Maximization.
Time-dependent spatially varying graphical models, with application to brain fMRI data analysis.
Permutation-based Causal Inference Algorithms with Interventions.
Training Quantized Nets: A Deeper Understanding.
Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods.
Clustering with Noisy Queries.
Learning Populations of Parameters.
Improved Training of Wasserstein GANs.
Diverse and Accurate Image Description Using a Variational Auto-Encoder with an Additive Gaussian Encoding Space.
Multiscale Quantization for Fast Similarity Search.
Asynchronous Parallel Coordinate Minimization for MAP Inference.
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra.
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning.
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations.
Imagination-Augmented Agents for Deep Reinforcement Learning.
On Fairness and Calibration.
Discriminative State Space Models.
Learning from uncertain curves: The 2-Wasserstein metric for Gaussian processes.
Online control of the false discovery rate with decaying memory.
Learning from Complementary Labels.
Do Deep Neural Networks Suffer from Crowding?
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model.
Dualing GANs.
Toward Robustness against Label Noise in Training Deep Discriminative Neural Networks.
Gradient descent GAN optimization is locally stable.
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
Min-Max Propagation.
From Bayesian Sparsity to Gated Recurrent Nets.
Approximation and Convergence Properties of Generative Adversarial Learning.
Semi-supervised Learning with GANs: Manifold Invariance with Improved Inference.
Hierarchical Implicit Models and Likelihood-Free Variational Inference.
On the Complexity of Learning Neural Networks.
Finite sample analysis of the GTD Policy Evaluation Algorithms in Markov Setting.
ALICE: Towards Understanding Adversarial Learning for Joint Distribution Matching.
Task-based End-to-end Model Learning in Stochastic Optimization.
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models.
SVD-Softmax: Fast Softmax Approximation on Large Vocabulary Neural Networks.
A Greedy Approach for Budgeted Maximum Inner Product Search.
Multi-View Decision Processes: The Helper-AI Problem.
Straggler Mitigation in Distributed Optimization Through Data Encoding.
AdaGAN: Boosting Generative Models.
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification.
Approximate Supermodularity Bounds for Experimental Design.
Hybrid Reward Architecture for Reinforcement Learning.
Improving the Expected Improvement Algorithm.
EEG-GRAPH: A Factor-Graph-Based Model for Capturing Spatial, Temporal, and Observational Relationships in Electroencephalograms.
Thinking Fast and Slow with Deep Learning and Tree Search.
A Sample Complexity Measure with Applications to Learning Optimal Auctions.
Local Aggregative Games.
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent.
Robust Imitation of Diverse Behaviors.
Identification of Gaussian Process State Space Models.
Online Learning with a Hint.
Renyi Differential Privacy Mechanisms for Posterior Sampling.
Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation.
Bayesian Optimization with Gradients.
PRUNE: Preserving Proximity and Global Ranking for Network Embedding.
Triangle Generative Adversarial Networks.
Self-supervised Learning of Motion Capture.
Riemannian approach to batch normalization.
Online Learning with Transductive Regret.
Identifying Outlier Arms in Multi-Armed Bandit.
K-Medoids For K-Means Seeding.
Variational Inference for Gaussian Process Models with Linear Complexity.
Saliency-based Sequential Image Attention with Multiset Prediction.
Real-Time Bidding with Side Information.
Excess Risk Bounds for the Bayes Risk using Variational Inference in Latent Gaussian Models.
A-NICE-MC: Adversarial Training for MCMC.
Efficient Modeling of Latent Information in Supervised Learning using Gaussian Processes.
Learning Graph Representations with Embedding Propagation.
Regularizing Deep Neural Networks by Noise: Its Interpretation and Optimization.
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models.
Perturbative Black Box Variational Inference.
Teaching Machines to Describe Images with Natural Language Feedback.
Stochastic and Adversarial Online Learning without Hyperparameters.
Hindsight Experience Replay.
Polynomial time algorithms for dual volume sampling.
Fair Clustering Through Fairlets.
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling.
Active Exploration for Learning Symbolic Representations.
Position-based Multiple-play Bandit Problem with Unknown Position Bias.
Online Reinforcement Learning in Stochastic Games.
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes.
A simple neural network module for relational reasoning.
DropoutNet: Addressing Cold Start in Recommender Systems.
Maximum Margin Interval Trees.
Online Learning for Multivariate Hawkes Processes.
Hash Embeddings for Efficient Word Representations.
Fast, Sample-Efficient Algorithms for Structured Phase Retrieval.
Group Additive Structure Identification for Kernel Nonparametric Regression.
Monte-Carlo Tree Search by Best Arm Identification.
Minimax Estimation of Bandable Precision Matrices.
Selective Classification for Deep Neural Networks.
Accelerated First-order Methods for Geodesically Convex Optimization on Riemannian Manifolds.
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon.
Multi-Task Learning for Contextual Bandits.
Accelerated Stochastic Greedy Coordinate Descent by Soft Thresholding Projection onto Simplex.
Working hard to know your neighbor's margins: Local descriptor learning loss.
Context Selection for Embedding Models.
Scalable Variational Inference for Dynamical Systems.
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions.
Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice.
Stochastic Approximation for Canonical Correlation Analysis.
A Unified Approach to Interpreting Model Predictions.
Beyond Worst-case: A Probabilistic Analysis of Affine Policies in Dynamic Optimization.
Affine-Invariant Online Optimization and the Low-rank Experts Problem.
Convergence rates of a partition based Bayesian multivariate density estimation method.
Adaptive Classification for Prediction Under a Budget.
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation.
Robust Optimization for Non-Convex Objectives.
QMDP-Net: Deep Learning for Planning under Partial Observability.
Query Complexity of Clustering with Side Information.
SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud.
Balancing information exposure in social networks.
Overcoming Catastrophic Forgetting by Incremental Moment Matching.
Non-Stationary Spectral Kernels.
Near-Optimal Edge Evaluation in Explicit Generalized Binomial Graphs.
Targeting EEG/LFP Synchrony with Neural Nets.
Scalable Model Selection for Belief Networks.
Ranking Data with Continuous Labels through Oriented Recursive Partitions.
Doubly Stochastic Variational Inference for Deep Gaussian Processes.
Discovering Potential Correlations via Hypercontractivity.
Simple strategies for recovering inner products from coarsely quantized random projections.
Streaming Robust Submodular Maximization: A Partitioned Thresholding Approach.
Reconstruct & Crush Network.
Visual Interaction Networks: Learning a Physics Simulator from Video.
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems.
Trimmed Density Ratio Estimation.
Online Learning of Optimal Bidding Strategy in Repeated Multi-Commodity Auctions.
Distral: Robust multitask reinforcement learning.
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks.
Faster and Non-ergodic O(1/K) Stochastic Alternating Direction Method of Multipliers.
Dual Path Networks.
Improved Graph Laplacian via Geometric Self-Consistency.
The Expxorcist: Nonparametric Graphical Models Via Conditional Exponential Densities.
Is Input Sparsity Time Possible for Kernel Low-Rank Approximation?
Federated Multi-Task Learning.
Unsupervised Learning of Disentangled Representations from Video.
Polynomial Codes: an Optimal Design for High-Dimensional Coded Matrix Multiplication.
Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net.
Safe Adaptive Importance Sampling.
A Decomposition of Forecast Error in Prediction Markets.
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach.
A Minimax Optimal Algorithm for Crowdsourcing.
Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays.
Adversarial Symmetric Variational Autoencoder.
Policy Gradient With Value Function Approximation For Collective Multiagent Planning.
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks.
Deep Reinforcement Learning from Human Preferences.
Multi-Information Source Optimization.
Sobolev Training for Neural Networks.
Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks.
Efficient Use of Limited-Memory Accelerators for Linear Learning on Heterogeneous Systems.
Reconstructing perceived faces from brain activations with deep adversarial neural decoding.
VAE Learning via Stein Variational Gradient Descent.
Learning Active Learning from Data.
Non-parametric Structured Output Networks.
Inverse Filtering for Hidden Markov Models.
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning.
Random Permutation Online Isotonic Regression.
Deconvolutional Paragraph Representation Learning.
Aggressive Sampling for Multi-class to Binary Reduction with Applications to Text Classification.
The Marginal Value of Adaptive Gradient Methods in Machine Learning.
Learning Low-Dimensional Metrics.
Learning A Structured Optimal Bipartite Graph for Co-Clustering.
Multi-Armed Bandits with Metric Movement Costs.
Mapping distinct timescales of functional interactions among brain networks.
Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation.
Triple Generative Adversarial Nets.
Prototypical Networks for Few-shot Learning.
Counterfactual Fairness.
Successor Features for Transfer in Reinforcement Learning.
Streaming Weak Submodularity: Interpreting Neural Networks on the Fly.
Adaptive Active Hypothesis Testing under Limited Information.
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders.
Independence clustering (without a matrix).
YASS: Yet Another Spike Sorter.
Optimized Pre-Processing for Discrimination Prevention.
Nonlinear Acceleration of Stochastic Algorithms.
On-the-fly Operation Batching in Dynamic Computation Graphs.
Learning Deep Structured Multi-Scale Features using Attention-Gated CRFs for Contour Prediction.
Deep Hyperspherical Learning.
Scalable Levy Process Priors for Spectral Kernel Learning.
On Tensor Train Rank Minimization : Statistical Efficiency and Scalable Algorithm.
Variational Memory Addressing in Generative Models.
Conservative Contextual Linear Bandits.
Structured Generative Adversarial Networks.
FALKON: An Optimal Large Scale Kernel Method.
Conic Scan-and-Cover algorithms for nonparametric topic modeling.
Incorporating Side Information by Adaptive Convolution.
Dynamic Routing Between Capsules.
Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.
Recursive Sampling for the Nystrom Method.
Variational Laws of Visual Attention for Dynamic Scenes.
InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations.
Influence Maximization with ε-Almost Submodular Threshold Functions.
End-to-end Differentiable Proving.
Diving into the shallows: a computational perspective on large-scale shallow learning.
Welfare Guarantees from Data.
Universal consistency and minimax rates for online Mondrian Forests.
Collapsed variational Bayes for Markov jump processes.
Multiresolution Kernel Approximation for Gaussian Process Regression.
Joint distribution optimal transportation for domain adaptation.
Visual Reference Resolution using Attention Memory for Visual Dialog.
Reducing Reparameterization Gradient Variance.
Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks.
Quantifying how much sensory information in a neural code is relevant for behavior.
Sparse convolutional coding for neuronal assembly detection.
High-Order Attention Models for Visual Question Answering.
On the Optimization Landscape of Tensor Decompositions.
A multi-agent reinforcement learning model of common-pool resource appropriation.
Off-policy evaluation for slate recommendation.
Bayesian GAN.
PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference.
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning.
Adaptive Batch Size for Safe Policy Gradients.
Concrete Dropout.
Collecting Telemetry Data Privately.
Subset Selection under Noise.
Unsupervised Sequence Classification using Sequential Output Statistics.
Limitations on Variance-Reduction and Acceleration Schemes for Finite Sums Optimization.
Eigen-Distortions of Hierarchical Representations.
Certified Defenses for Data Poisoning Attacks.
Neural system identification for large populations separating "what" and "where".
Gaussian process based nonlinear latent structure discovery in multivariate spike train data.
Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit.
Tractability in Structured Probability Spaces.
Near Optimal Sketching of Low-Rank Tensor Regression.
On Optimal Generalizability in Parametric Learning.
Efficient Optimization for Linear Dynamical Systems with Applications to Clustering and Sparse Coding.
Spherical convolutions and their application in molecular modelling.
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes.
Process-constrained batch Bayesian optimisation.
ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events.
Deep Sets.
A Universal Analysis of Large-Scale Regularized Least Squares Solutions.
Multi-Objective Non-parametric Sequential Prediction.
Clustering Billions of Reads for DNA Data Storage.
Multi-output Polynomial Networks and Factorization Machines.
A Regularized Framework for Sparse and Structured Neural Attention.
Dynamic-Depth Context Tree Weighting.
Sparse Embedded k-Means Clustering.
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning.
Streaming Sparse Gaussian Process Approximations.
Bayesian Compression for Deep Learning.
Adaptive SVRG Methods under Error Bound Conditions with Unknown Growth Parameter.
Language Modeling with Recurrent Highway Hypernetworks.
Ensemble Sampling.
Wasserstein Learning of Deep Generative Point Process Models.
Learning to Compose Domain-Specific Transformations for Data Augmentation.
Differentially private Bayesian learning on distributed data.
Generalization Properties of Learning with Random Features.
Is the Bellman residual a bad proxy?
Dynamic Importance Sampling for Anytime Bounds of the Partition Function.
Graph Matching via Multiplicative Update Algorithm.
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee.
Regret Minimization in MDPs with Options without Prior Knowledge.
Adversarial Ranking for Language Generation.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree.
Shallow Updates for Deep Reinforcement Learning.
Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery.
Stein Variational Gradient Descent as Gradient Flow.
Adaptive Accelerated Gradient Converging Method under H\"{o}lderian Error Bound Condition.
Approximation Bounds for Hierarchical Clustering: Average Linkage, Bisecting K-means, and Local Search.
Unbiased estimates for linear regression via volume sampling.
Rotting Bandits.
Tomography of the London Underground: a Scalable Model for Origin-Destination Data.
Hierarchical Attentive Recurrent Tracking.
Reinforcement Learning under Model Mismatch.
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem.
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback.
Learning Causal Structures Using Regression Invariance.
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms.
Continual Learning with Deep Generative Replay.
Deep Lattice Networks and Partial Monotonic Functions.
Variance-based Regularization with Convex Objectives.
Deep Voice 2: Multi-Speaker Neural Text-to-Speech.
Model-Powered Conditional Independence Test.
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach.
A PAC-Bayesian Analysis of Randomized Learning with Application to Stochastic Gradient Descent.
Beyond Parity: Fairness Objectives for Collaborative Filtering.
State Aware Imitation Learning.
Robust Estimation of Neural Signals in Calcium Imaging.
Deep Recurrent Neural Network-Based Identification of Precursor microRNAs.
Gauging Variational Inference.
Decomposable Submodular Function Minimization: Discrete and Continuous.
Mean Field Residual Networks: On the Edge of Chaos.
Estimation of the covariance structure of heavy-tailed distributions.
Convolutional Gaussian Processes.
Alternating Estimation for Structured High-Dimensional Multi-Response Models.
Online Dynamic Programming.
Cold-Start Reinforcement Learning with Softmax Policy Gradient.
Principles of Riemannian Geometry in Neural Networks.
A Bayesian Data Augmentation Approach for Learning Deep Models.
Premise Selection for Theorem Proving by Deep Graph Embedding.
Bridging the Gap Between Value and Policy Based Reinforcement Learning.
An Empirical Study on The Properties of Random Bases for Kernel Methods.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning.
On Quadratic Convergence of DC Proximal Newton Algorithm in Nonconvex Sparse Learning.
Variational Inference via \chi Upper Bound Minimization.
Differentially Private Empirical Risk Minimization Revisited: Faster and More General.
An Empirical Bayes Approach to Optimizing Machine Learning Algorithms.
VAIN: Attentional Multi-agent Predictive Modeling.
Decomposition-Invariant Conditional Gradient for General Polytopes with Line Search.
Dynamic Revenue Sharing.
Dual Discriminator Generative Adversarial Nets.
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games.
Parallel Streaming Wasserstein Barycenters.
Nonlinear random matrix theory for deep learning.
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models.
Practical Data-Dependent Metric Compression with Provable Guarantees.
Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network.
Multi-way Interacting Regression via Factorization Machines.
Multitask Spectral Learning of Weighted Automata.
EX2: Exploration with Exemplar Models for Deep Reinforcement Learning.
Accuracy First: Selecting a Differential Privacy Level for Accuracy Constrained ERM.
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System.
Rigorous Dynamics and Consistent Estimation in Arbitrarily Conditioned Linear Systems.
Sparse Approximate Conic Hulls.
Information-theoretic analysis of generalization capability of learning algorithms.
Fisher GAN.
How regularization affects the critical points in linear networks.
Using Options and Covariance Testing for Long Horizon Off-Policy Policy Evaluation.
Deep Supervised Discrete Hashing.
Fitting Low-Rank Tensors in Constant Time.
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications.
Generative Local Metric Learning for Kernel Regression.
Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems.
Noise-Tolerant Interactive Learning Using Pairwise Comparisons.
SGD Learns the Conjugate Kernel Class of the Network.
Scalable Demand-Aware Recommendation.
Fast Black-box Variational Inference through Stochastic Trust-Region Optimization.
Collaborative PAC Learning.
OnACID: Online Analysis of Calcium Imaging Data in Real Time.
An inner-loop free solution to inverse problems using deep neural networks.
Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting.
Non-convex Finite-Sum Optimization Via SCSG Methods.
Masked Autoregressive Flow for Density Estimation.
Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks.
Differentiable Learning of Logical Rules for Knowledge Base Reasoning.
Inhomogeneous Hypergraph Clustering with Applications.
Large-Scale Quadratically Constrained Quadratic Program via Low-Discrepancy Sequences.
Practical Locally Private Heavy Hitters.
Associative Embedding: End-to-End Learning for Joint Detection and Grouping.
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation.
Few-Shot Learning Through an Information Retrieval Lens.
Expectation Propagation for t-Exponential Family Using q-Algebra.
Zap Q-Learning.
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe.
The Reversible Residual Network: Backpropagation Without Storing Activations.
MMD GAN: Towards Deeper Understanding of Moment Matching Network.
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks.
Runtime Neural Pruning.
Pixels to Graphs by Associative Embedding.
Training Deep Networks without Learning Rates Through Coin Betting.
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols.
Elementary Symmetric Polynomials for Optimal Experimental Design.
Bandits Dueling on Partially Ordered Sets.
Natural Value Approximators: Learning when to Trust Past Estimates.
Consistent Robust Regression.
A graph-theoretic approach to multitasking.
Bayesian Dyadic Trees and Histograms for Regression.
Neural Program Meta-Induction.
Hiding Images in Plain Sight: Deep Steganography.
First-Order Adaptive Sample Size Methods to Reduce Complexity of Empirical Risk Minimization.
Compatible Reward Inverse Reinforcement Learning.
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs.
Expectation Propagation with Stochastic Kinetic Model in Complex Interaction Systems.
Stabilizing Training of Generative Adversarial Networks through Regularization.
Learning ReLUs via Gradient Descent.
Alternating minimization for dictionary learning with random initialization.
Consistent Multitask Learning with Nonlinear Output Relations.
PixelGAN Autoencoders.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration.
Generating steganographic images via adversarial training.
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models.
Speeding Up Latent Variable Gaussian Graphical Model Estimation via Nonconvex Optimization.
Revisit Fuzzy Neural Network: Demystifying Batch Normalization and ReLU with Generalized Hamming Network.
Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts.
Hierarchical Methods of Moments.
Lookahead Bayesian Optimization with Inequality Constraints.
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data.
Solving Most Systems of Random Quadratic Equations.
Revenue Optimization with Approximate Bid Predictions.
Learning Chordal Markov Networks via Branch and Bound.
Practical Bayesian Optimization for Model Fitting with Bayesian Adaptive Direct Search.
The Numerics of GANs.
Repeated Inverse Reinforcement Learning.
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning.
Adaptive Clustering through Semidefinite Programming.
Deliberation Networks: Sequence Generation Beyond One-Pass Decoding.
Learned D-AMP: Principled Neural Network based Compressive Image Recovery.
Minimal Exploration in Structured Stochastic Bandits.
Model evidence from nonequilibrium simulations.
Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks.
Train longer, generalize better: closing the generalization gap in large batch training of neural networks.
Convergent Block Coordinate Descent for Training Tikhonov Regularized Deep Neural Networks.
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding.
Reliable Decision Support using Counterfactual Models.
Optimal Sample Complexity of M-wise Data for Top-K Ranking.
Positive-Unlabeled Learning with Non-Negative Risk Estimator.
Deep Dynamic Poisson Factorization Model.
Submultiplicative Glivenko-Cantelli and Uniform Convergence of Revenues.
Predicting User Activity Level In Point Processes With Mass Transport Equation.
Deep Learning with Topological Signatures.
Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite Sum Structure.
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes.
Deep Hyperalignment.
Learning Multiple Tasks with Multilinear Relationship Networks.
A Scale Free Algorithm for Stochastic Bandits with Bounded Kurtosis.
Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions.
Probabilistic Rule Realization and Selection.
Cost efficient gradient boosting.
NeuralFDR: Learning Discovery Thresholds from Hypothesis Features.
Linear regression without correspondence.
Learning Affinity via Spatial Propagation Networks.
TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning.
Best Response Regression.
Regret Analysis for Continuous Dueling Bandit.
A New Alternating Direction Method for Linear Programming.
From which world is your graph.
Translation Synchronization via Truncated Least Squares.
Regularized Modal Regression with Applications in Cognitive Impairment Prediction.
Max-Margin Invariant Features from Transformed Unlabelled Data.
Online Convex Optimization with Stochastic Constraints.
Learning with Feature Evolvable Streams.
Nonbacktracking Bounds on the Influence in Independent Cascade Models.
Adaptive stimulus selection for optimizing neural population responses.
Generalized Linear Model Regression under Distance-to-set Penalties.
Accelerated consensus via Min-Sum Splitting.
Deanonymization in the Bitcoin P2P Network.
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering.
Testing and Learning on Distributions with Symmetric Noise Invariance.
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach.
DPSCREEN: Dynamic Personalized Screening.
Tensor Biclustering.
Online Prediction with Selfish Experts.
Flexible statistical inference for mechanistic models of neural dynamics.
Shape and Material from Sound.
ADMM without a Fixed Penalty Parameter: Faster Convergence with New Adaptive Penalization.
Adaptive Bayesian Sampling with Monte Carlo EM.
Learning to Inpaint for Image Compression.
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets.
Fixed-Rank Approximation of a Positive-Semidefinite Matrix from Streaming Data.
Linearly constrained Gaussian processes.
Matching neural paths: transfer from recognition to correspondence search.
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results.
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds.
Predictive-State Decoders: Encoding the Future into Recurrent Networks.
Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications.
Learning spatiotemporal piecewise-geodesic trajectories from longitudinal manifold-valued data.
Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations.
Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition.
Sharpness, Restart and Acceleration.
Integration Methods and Optimization Algorithms.
Learning the Morphology of Brain Signals Using Alpha-Stable Convolutional Sparse Coding.
One-Shot Imitation Learning.
Union of Intersections (UoI) for Interpretable Data Driven Discovery and Prediction.
Gradient Descent Can Take Exponential Time to Escape Saddle Points.
Revisiting Perceptron: Efficient and Label-Optimal Learning of Halfspaces.
Question Asking as Program Generation.
Subset Selection and Summarization in Sequential Data.
Inductive Representation Learning on Large Graphs.
Differentiable Learning of Submodular Functions.
Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples.
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.
Learning to Pivot with Adversarial Networks.
Self-Normalizing Neural Networks.
Decoupling "when to update" from "how to update".
GP CaKe: Effective brain connectivity with causal kernels.
Learning Overcomplete HMMs.
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation.
Online multiclass boosting.
Safe Model-based Reinforcement Learning with Stability Guarantees.
Contrastive Learning for Image Captioning.
Detrended Partial Cross Correlation for Brain Connectivity Analysis.
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs.
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces.
Compression-aware Training of Deep Networks.
Unsupervised learning of object frames by dense equivariant image labelling.
Label Distribution Learning Forests.
Introspective Classification with Convolutional Nets.
Minimizing a Submodular Function from Samples.
Lower bounds on the robustness to adversarial perturbations.
Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes.
A New Theory for Matrix Completion.
Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees.
Deep Mean-Shift Priors for Image Restoration.
One-Sided Unsupervised Domain Mapping.
Learning Efficient Object Detection Models with Knowledge Distillation.
Improved Dynamic Regret for Non-degenerate Functions.
A Screening Rule for l1-Regularized Ising Model Estimation.
Coded Distributed Computing for Inverse Problems.
Unsupervised Image-to-Image Translation Networks.
Safe and Nested Subgame Solving for Imperfect-Information Games.
Recycling Privileged Learning and Distribution Matching for Fairness.
Nonparametric Online Regression while Learning the Metric.
Avoiding Discrimination through Causal Reasoning.
Diffusion Approximations for Online Principal Component Estimation and Global Convergence.
Geometric Descent Method for Convex Composite Minimization.
Efficient Online Linear Optimization with Approximation Algorithms.
Langevin Dynamics with Continuous Tempering for Training Deep Neural Networks.
Doubly Accelerated Stochastic Variance Reduced Dual Averaging Method for Regularized Empirical Risk Minimization.
Convergence Analysis of Two-layer Neural Networks with ReLU Activation.
Controllable Invariance through Adversarial Feature Learning.
Hypothesis Transfer Learning via Transformation Functions.
Adversarial Surrogate Losses for Ordinal Regression.
Multimodal Learning and Reasoning for Visual Question Answering.
MarrNet: 3D Shape Reconstruction via 2.5D Sketches.
Learning Spherical Convolution for Fast Features from 360° Imagery.
Dykstra's Algorithm, ADMM, and Coordinate Descent: Connections, Insights, and Extensions.
Learning multiple visual domains with residual adapters.
Learning with Average Top-k Loss.
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms.
Mixture-Rank Matrix Approximation for Collaborative Filtering.
Toward Multimodal Image-to-Image Translation.
f-GANs in an Information Geometric Nutshell.
On the Power of Truncated SVD for General High-rank Matrix Estimation Problems.
Preventing Gradient Explosions in Gated Recurrent Units.
Variable Importance Using Decision Trees.
Inference in Graphical Models via Semidefinite Programming Hierarchies.
Pose Guided Person Image Generation.
On the Model Shrinkage Effect of Gamma Process Edge Partition Models.
Universal Style Transfer via Feature Transforms.
Phase Transitions in the Pooled Data Problem.
Learning a Multi-View Stereo Machine.
Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks.
Towards Accurate Binary Convolutional Neural Network.
Gated Recurrent Convolution Neural Network for OCR.
MaskRNN: Instance Level Video Object Segmentation.
Best of Both Worlds: Transferring Knowledge from Discriminative Learning to a Generative Visual Dialog Model.
On Structured Prediction Theory with Calibrated Convex Surrogate Losses.
A simple model of recognition and recall memory.
k-Support and Ordered Weighted Sparsity for Overlapping Groups: Hardness and Algorithms.
Cortical microcircuits as gated-recurrent neural networks.
A Linear-Time Kernel Goodness-of-Fit Test.
Structured Embedding Models for Grouped Data.
Inferring Generative Model Structure with Static Analysis.
From Parity to Preference-based Notions of Fairness in Classification.
The Unreasonable Effectiveness of Structured Random Orthogonal Embeddings.
Uprooting and Rerooting Higher-Order Graphical Models.
Group Sparse Additive Machine.
Parametric Simplex Method for Sparse Learning.
Decoding with Value Networks for Neural Machine Translation.
Label Efficient Learning of Transferable Representations acrosss Domains and Tasks.
Learning to See Physics via Visual De-animation.
Interactive Submodular Bandit.
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning.
Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent.
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models.
Scalable Generalized Linear Bandits: Online Computation and Hashing.
Hunt For The Unique, Stable, Sparse And Fast Feature Learning On Graphs.
Dilated Recurrent Neural Networks.
Dual-Agent GANs for Photorealistic and Identity Preserving Profile Face Synthesis.
Breaking the Nonsmooth Barrier: A Scalable Parallel Method for Composite Optimization.
On the Consistency of Quick Shift.
Attentional Pooling for Action Recognition.
Deep Subspace Clustering Networks.
Concentration of Multilinear Functions of the Ising Model with Applications to Network Data.
Wider and Deeper, Cheaper and Faster: Tensorized LSTMs for Sequence Learning.
