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Comparing Dynamics: Deep Neural Networks versus Glassy Systems
The Hierarchical Adaptive Forgetting Variational Filter
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
Stochastic Training of Graph Convolutional Networks with Variance Reduction
Not All Samples Are Created Equal: Deep Learning with Importance Sampling
Continual Reinforcement Learning with Complex Synapses
Solving Partial Assignment Problems using Random Clique Complexes
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors
Bucket Renormalization for Approximate Inference
Overcoming Catastrophic Forgetting with Hard Attention to the Task
A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning
TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service
GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms
Convolutional Imputation of Matrix Networks
Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope
Investigating Human Priors for Playing Video Games
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Classification from Pairwise Similarity and Unlabeled Data
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Constrained Interacting Submodular Groupings
Level-Set Methods for Finite-Sum Constrained Convex Optimization
Semi-Implicit Variational Inference
Policy Optimization as Wasserstein Gradient Flows
Differentiable Dynamic Programming for Structured Prediction and Attention
Detecting and Correcting for Label Shift with Black Box Predictors
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits
Online Learning with Abstention
Decoupled Parallel Backpropagation with Convergence Guarantee
RLlib: Abstractions for Distributed Reinforcement Learning
Riemannian Stochastic Recursive Gradient Algorithm with Retraction and Vector Transport and Its Convergence Analysis
Inductive Two-Layer Modeling with Parametric Bregman Transfer
Stein Variational Gradient Descent Without Gradient
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Neural Dynamic Programming for Musical Self Similarity
On Learning Sparsely Used Dictionaries from Incomplete Samples
SQL-Rank: A Listwise Approach to Collaborative Ranking
Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information
Improving Regression Performance with Distributional Losses
Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning
On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo
The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference
State Abstractions for Lifelong Reinforcement Learning
Learning and Memorization
Neural Relational Inference for Interacting Systems
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Understanding and Simplifying One-Shot Architecture Search
Probabilistic Recurrent State-Space Models
More Robust Doubly Robust Off-policy Evaluation
Constant-Time Predictive Distributions for Gaussian Processes
Hierarchical Clustering with Structural Constraints
Frank-Wolfe with Subsampling Oracle
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?
Attention-based Deep Multiple Instance Learning
Fast Approximate Spectral Clustering for Dynamic Networks
Clipped Action Policy Gradient
Noise2Noise: Learning Image Restoration without Clean Data
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients
Importance Weighted Transfer of Samples in Reinforcement Learning
The Multilinear Structure of ReLU Networks
Composite Marginal Likelihood Methods for Random Utility Models
Dropout Training, Data-dependent Regularization, and Generalization Bounds
Probabilistic Boolean Tensor Decomposition
Visualizing and Understanding Atari Agents
Learning One Convolutional Layer with Overlapping Patches
Data-Dependent Stability of Stochastic Gradient Descent
An Estimation and Analysis Framework for the Rasch Model
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
Differentially Private Database Release via Kernel Mean Embeddings
Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow
Focused Hierarchical RNNs for Conditional Sequence Processing
Discovering Interpretable Representations for Both Deep Generative and Discriminative Models
Improving Sign Random Projections With Additional Information
Delayed Impact of Fair Machine Learning
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Bayesian Model Selection for Change Point Detection and Clustering
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms
Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order
A Spectral Approach to Gradient Estimation for Implicit Distributions
Accurate Uncertainties for Deep Learning Using Calibrated Regression
Gated Path Planning Networks
Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization
Adversarial Regression with Multiple Learners
DRACO: Byzantine-resilient Distributed Training via Redundant Gradients
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Covariates
Lipschitz Continuity in Model-based Reinforcement Learning
Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn's Algorithm
Deep Density Destructors
SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation
Fairness Without Demographics in Repeated Loss Minimization
An Inference-Based Policy Gradient Method for Learning Options
Deep Models of Interactions Across Sets
Mutual Information Neural Estimation
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
Learning Steady-States of Iterative Algorithms over Graphs
Autoregressive Quantile Networks for Generative Modeling
Tighter Variational Bounds are Not Necessarily Better
Finding Influential Training Samples for Gradient Boosted Decision Trees
Neural Autoregressive Flows
Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Shampoo: Preconditioned Stochastic Tensor Optimization
Distilling the Posterior in Bayesian Neural Networks
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems
On the Implicit Bias of Dropout
Rates of Convergence of Spectral Methods for Graphon Estimation
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations
Stochastic Wasserstein Barycenters
Learning to Act in Decentralized Partially Observable MDPs
Linear Spectral Estimators and an Application to Phase Retrieval
Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
Parameterized Algorithms for the Matrix Completion Problem
Learning Memory Access Patterns
Prediction Rule Reshaping
Optimization, fast and slow: optimally switching between local and Bayesian optimization
Weightless: Lossy weight encoding for deep neural network compression
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?
Graph Networks as Learnable Physics Engines for Inference and Control
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
Stein Variational Message Passing for Continuous Graphical Models
Constraining the Dynamics of Deep Probabilistic Models
SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate
Learning in Reproducing Kernel Kreı̆n Spaces
Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers
Dynamic Evaluation of Neural Sequence Models
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
Fair and Diverse DPP-Based Data Summarization
On Acceleration with Noise-Corrupted Gradients
Model-Level Dual Learning
Conditional Noise-Contrastive Estimation of Unnormalised Models
Geometry Score: A Method For Comparing Generative Adversarial Networks
Disentangling by Factorising
Machine Theory of Mind
Transfer Learning via Learning to Transfer
Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF)
Beyond the One-Step Greedy Approach in Reinforcement Learning
Thompson Sampling for Combinatorial Semi-Bandits
Progress & Compress: A scalable framework for continual learning
A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Open Category Detection with PAC Guarantees
Extreme Learning to Rank via Low Rank Assumption
The Limits of Maxing, Ranking, and Preference Learning
Self-Imitation Learning
A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator
Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing
Smoothed Action Value Functions for Learning Gaussian Policies
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement
Differentiable plasticity: training plastic neural networks with backpropagation
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples
Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering
A Progressive Batching L-BFGS Method for Machine Learning
Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering
Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization
An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method
The Mechanics of n-Player Differentiable Games
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Revealing Common Statistical Behaviors in Heterogeneous Populations
Learning Dynamics of Linear Denoising Autoencoders
Learning Localized Spatio-Temporal Models From Streaming Data
Learning long term dependencies via Fourier recurrent units
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks
Active Learning with Logged Data
Reviving and Improving Recurrent Back-Propagation
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning
Rapid Adaptation with Conditionally Shifted Neurons
Learning with Abandonment
Active Testing: An Efficient and Robust Framework for Estimating Accuracy
Non-convex Conditional Gradient Sliding
An Alternative View: When Does SGD Escape Local Minima?
Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks
The Generalization Error of Dictionary Learning with Moreau Envelopes
Theoretical Analysis of Sparse Subspace Clustering with Missing Entries
Coded Sparse Matrix Multiplication
Invariance of Weight Distributions in Rectified MLPs
Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction
Learning Low-Dimensional Temporal Representations
Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer
Neural Program Synthesis from Diverse Demonstration Videos
DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding
Blind Justice: Fairness with Encrypted Sensitive Attributes
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Gradually Updated Neural Networks for Large-Scale Image Recognition
Adversarial Learning with Local Coordinate Coding
Which Training Methods for GANs do actually Converge?
An Iterative, Sketching-based Framework for Ridge Regression
SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions
Theoretical Analysis of Image-to-Image Translation with Adversarial Learning
Learning Diffusion using Hyperparameters
Learning to Optimize Combinatorial Functions
Variational Network Inference: Strong and Stable with Concrete Support
On Nesting Monte Carlo Estimators
Anonymous Walk Embeddings
INSPECTRE: Privately Estimating the Unseen
Tempered Adversarial Networks
Adaptive Three Operator Splitting
Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap
$D^2$: Decentralized Training over Decentralized Data
Efficient Gradient-Free Variational Inference using Policy Search
Practical Contextual Bandits with Regression Oracles
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
Stagewise Safe Bayesian Optimization with Gaussian Processes
Convergent Tree Backup and Retrace with Function Approximation
Time Limits in Reinforcement Learning
Deep Variational Reinforcement Learning for POMDPs
MAGAN: Aligning Biological Manifolds
Bayesian Optimization of Combinatorial Structures
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
Learning Policy Representations in Multiagent Systems
Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs
Alternating Randomized Block Coordinate Descent
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time
Image Transformer
Is Generator Conditioning Causally Related to GAN Performance?
Feedback-Based Tree Search for Reinforcement Learning
Differentially Private Identity and Equivalence Testing of Discrete Distributions
A Classification-Based Study of Covariate Shift in GAN Distributions
Semi-Supervised Learning via Compact Latent Space Clustering
Noisin: Unbiased Regularization for Recurrent Neural Networks
Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates
Orthogonal Machine Learning: Power and Limitations
Learning to Speed Up Structured Output Prediction
Hyperbolic Entailment Cones for Learning Hierarchical Embeddings
Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models
On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
prDeep: Robust Phase Retrieval with a Flexible Deep Network
Deep One-Class Classification
Differentiable Abstract Interpretation for Provably Robust Neural Networks
Local Density Estimation in High Dimensions
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling
Firing Bandits: Optimizing Crowdfunding
Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization
Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks
TACO: Learning Task Decomposition via Temporal Alignment for Control
Mean Field Multi-Agent Reinforcement Learning
On the Relationship between Data Efficiency and Error for Uncertainty Sampling
Bounding and Counting Linear Regions of Deep Neural Networks
Representation Learning on Graphs with Jumping Knowledge Networks
Fixing a Broken ELBO
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
Subspace Embedding and Linear Regression with Orlicz Norm
Implicit Quantile Networks for Distributional Reinforcement Learning
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Distributed Clustering via LSH Based Data Partitioning
Inference Suboptimality in Variational Autoencoders
A Distributed Second-Order Algorithm You Can Trust
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Training Neural Machines with Trace-Based Supervision
Asynchronous Decentralized Parallel Stochastic Gradient Descent
Deep Predictive Coding Network for Object Recognition
Minibatch Gibbs Sampling on Large Graphical Models
Streaming Principal Component Analysis in Noisy Setting
Black-box Adversarial Attacks with Limited Queries and Information
Accelerating Natural Gradient with Higher-Order Invariance
Selecting Representative Examples for Program Synthesis
Sound Abstraction and Decomposition of Probabilistic Programs
Fast Decoding in Sequence Models Using Discrete Latent Variables
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory
A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music
Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks
GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
Fourier Policy Gradients
oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis
Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data
Born Again Neural Networks
Differentiable Compositional Kernel Learning for Gaussian Processes
Differentially Private Matrix Completion Revisited
Compressing Neural Networks using the Variational Information Bottelneck
Variational Inference and Model Selection with Generalized Evidence Bounds
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Scalable approximate Bayesian inference for particle tracking data
ADMM and Accelerated ADMM as Continuous Dynamical Systems
WSNet: Compact and Efficient Networks Through Weight Sampling
The Well-Tempered Lasso
Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy
Towards Black-box Iterative Machine Teaching
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Loss Decomposition for Fast Learning in Large Output Spaces
Chi-square Generative Adversarial Network
Predict and Constrain: Modeling Cardinality in Deep Structured Prediction
Using Inherent Structures to design Lean 2-layer RBMs
Characterizing Implicit Bias in Terms of Optimization Geometry
Learning Adversarially Fair and Transferable Representations
Probably Approximately Metric-Fair Learning
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Compiling Combinatorial Prediction Games
Improved large-scale graph learning through ridge spectral sparsification
Stronger Generalization Bounds for Deep Nets via a Compression Approach
Hierarchical Long-term Video Prediction without Supervision
Competitive Caching with Machine Learned Advice
Essentially No Barriers in Neural Network Energy Landscape
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Composable Planning with Attributes
Learning in Integer Latent Variable Models with Nested Automatic Differentiation
Improving Optimization in Models With Continuous Symmetry Breaking
The Mirage of Action-Dependent Baselines in Reinforcement Learning
A Two-Step Computation of the Exact GAN Wasserstein Distance
Learning equations for extrapolation and control
Provable Variable Selection for Streaming Features
A Robust Approach to Sequential Information Theoretic Planning
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
Path Consistency Learning in Tsallis Entropy Regularized MDPs
Robust and Scalable Models of Microbiome Dynamics
On the Limitations of First-Order Approximation in GAN Dynamics
Efficient First-Order Algorithms for Adaptive Signal Denoising
Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings
Decoupling Gradient-Like Learning Rules from Representations
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
signSGD: Compressed Optimisation for Non-Convex Problems
Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions
Latent Space Policies for Hierarchical Reinforcement Learning
The Hidden Vulnerability of Distributed Learning in Byzantium
Local Private Hypothesis Testing: Chi-Square Tests
Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control
A Reductions Approach to Fair Classification
High Performance Zero-Memory Overhead Direct Convolutions
Variational Bayesian dropout: pitfalls and fixes
Transformation Autoregressive Networks
Mitigating Bias in Adaptive Data Gathering via Differential Privacy
Adversarial Time-to-Event Modeling
Modeling Others using Oneself in Multi-Agent Reinforcement Learning
Network Global Testing by Counting Graphlets
Modeling Sparse Deviations for Compressed Sensing using Generative Models
Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors
Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search
One-Shot Segmentation in Clutter
Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit
Regret Minimization for Partially Observable Deep Reinforcement Learning
Scalable Bilinear Pi Learning Using State and Action Features
Non-linear motor control by local learning in spiking neural networks
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Fast Parametric Learning with Activation Memorization
The Weighted Kendall and High-order Kernels for Permutations
A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Structured Variationally Auto-encoded Optimization
Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training
Clustering Semi-Random Mixtures of Gaussians
PixelSNAIL: An Improved Autoregressive Generative Model
Efficient Neural Audio Synthesis
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Randomized Block Cubic Newton Method
A Spline Theory of Deep Learning
Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework
Adversarial Attack on Graph Structured Data
Fast Bellman Updates for Robust MDPs
Representation Tradeoffs for Hyperbolic Embeddings
Analyzing Uncertainty in Neural Machine Translation
Autoregressive Convolutional Neural Networks for Asynchronous Time Series
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
Approximate message passing for amplitude based optimization
Nearly Optimal Robust Subspace Tracking
Quasi-Monte Carlo Variational Inference
Residual Unfairness in Fair Machine Learning from Prejudiced Data
Semiparametric Contextual Bandits
DiCE: The Infinitely Differentiable Monte Carlo Estimator
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
A Boo(n) for Evaluating Architecture Performance
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning
Hierarchical Multi-Label Classification Networks
End-to-End Learning for the Deep Multivariate Probit Model
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Continuous-Time Flows for Efficient Inference and Density Estimation
Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Synthesizing Robust Adversarial Examples
Efficient Neural Architecture Search via Parameters Sharing
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
Conditional Neural Processes
Ultra Large-Scale Feature Selection using Count-Sketches
Learning to Branch
Addressing Function Approximation Error in Actor-Critic Methods
Stochastic Variance-Reduced Cubic Regularized Newton Method
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
Temporal Poisson Square Root Graphical Models
Estimation of Markov Chain via Rank-constrained Likelihood
BOCK : Bayesian Optimization with Cylindrical Kernels
Bounds on the Approximation Power of Feedforward Neural Networks
On Matching Pursuit and Coordinate Descent
Pathwise Derivatives Beyond the Reparameterization Trick
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data
High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach
Fast Variance Reduction Method with Stochastic Batch Size
Mix & Match - Agent Curricula for Reinforcement Learning
Accelerating Greedy Coordinate Descent Methods
Spline Filters For End-to-End Deep Learning
Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go?
ContextNet: Deep learning for Star Galaxy Classification
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
Functional Gradient Boosting based on Residual Network Perception
Automatic Goal Generation for Reinforcement Learning Agents
Inter and Intra Topic Structure Learning with Word Embeddings
Hierarchical Imitation and Reinforcement Learning
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Convergence guarantees for a class of non-convex and non-smooth optimization problems
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Tree Edit Distance Learning via Adaptive Symbol Embeddings
Structured Control Nets for Deep Reinforcement Learning
CRVI: Convex Relaxation for Variational Inference
Stein Points
Learning by Playing - Solving Sparse Reward Tasks from Scratch
DVAE++: Discrete Variational Autoencoders with Overlapping Transformations
IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
StrassenNets: Deep Learning with a Multiplication Budget
Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems
Efficient and Consistent Adversarial Bipartite Matching
Learning unknown ODE models with Gaussian processes
Knowledge Transfer with Jacobian Matching
Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees
QuantTree: Histograms for Change Detection in Multivariate Data Streams
Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions
Policy and Value Transfer in Lifelong Reinforcement Learning
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Adversarially Regularized Autoencoders
Disentangled Sequential Autoencoder
Escaping Saddles with Stochastic Gradients
Testing Sparsity over Known and Unknown Bases
LaVAN: Localized and Visible Adversarial Noise
Multi-Fidelity Black-Box Optimization with Hierarchical Partitions
Fitting New Speakers Based on a Short Untranscribed Sample
Candidates vs. Noises Estimation for Large Multi-Class Classification Problem
LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration
Data Summarization at Scale: A Two-Stage Submodular Approach
Hierarchical Text Generation and Planning for Strategic Dialogue
Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care
Efficient end-to-end learning for quantizable representations
Online Linear Quadratic Control
Kronecker Recurrent Units
Celer: a Fast Solver for the Lasso with Dual Extrapolation
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Learning Longer-term Dependencies in RNNs with Auxiliary Losses
Been There, Done That: Meta-Learning with Episodic Recall
Learning Implicit Generative Models with the Method of Learned Moments
Kernelized Synaptic Weight Matrices
Generative Temporal Models with Spatial Memory for Partially Observed Environments
Configurable Markov Decision Processes
Noisy Natural Gradient as Variational Inference
Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising
Canonical Tensor Decomposition for Knowledge Base Completion
Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions
Bilevel Programming for Hyperparameter Optimization and Meta-Learning
Iterative Amortized Inference
Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation
Deep Bayesian Nonparametric Tracking
Learning a Mixture of Two Multinomial Logits
Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations
Understanding the Loss Surface of Neural Networks for Binary Classification
Max-Mahalanobis Linear Discriminant Analysis Networks
Budgeted Experiment Design for Causal Structure Learning
Locally Private Hypothesis Testing
Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization
Optimizing the Latent Space of Generative Networks
Mixed batches and symmetric discriminators for GAN training
Binary Classification with Karmic, Threshold-Quasi-Concave Metrics
State Space Gaussian Processes with Non-Gaussian Likelihood
K-means clustering using random matrix sparsification
Stochastic Variance-Reduced Policy Gradient
DCFNet: Deep Neural Network with Decomposed Convolutional Filters
Learning to search with MCTSnets
Tropical Geometry of Deep Neural Networks
Gradient Coding from Cyclic MDS Codes and Expander Graphs
Causal Bandits with Propagating Inference
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
First Order Generative Adversarial Networks
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design
Crowdsourcing with Arbitrary Adversaries
Accurate Inference for Adaptive Linear Models
Feasible Arm Identification
Distributed Nonparametric Regression under Communication Constraints
CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning
Bayesian Uncertainty Estimation for Batch Normalized Deep Networks
Ranking Distributions based on Noisy Sorting
Recurrent Predictive State Policy Networks
Yes, but Did It Work?: Evaluating Variational Inference
AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
Learning Registered Point Processes from Idiosyncratic Observations
Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data
Bandits with Delayed, Aggregated Anonymous Feedback
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Nonconvex Optimization for Regression with Fairness Constraints
Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Approximation Algorithms for Cascading Prediction Models
Towards Fast Computation of Certified Robustness for ReLU Networks
Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering
Improved nearest neighbor search using auxiliary information and priority functions
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Nonparametric variable importance using an augmented neural network with multi-task learning
Learning Independent Causal Mechanisms
Programmatically Interpretable Reinforcement Learning
SparseMAP: Differentiable Sparse Structured Inference
To Understand Deep Learning We Need to Understand Kernel Learning
Path-Level Network Transformation for Efficient Architecture Search
The Dynamics of Learning: A Random Matrix Approach
Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace
Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization
GAIN: Missing Data Imputation using Generative Adversarial Nets
Structured Output Learning with Abstention: Application to Accurate Opinion Prediction
Black-Box Variational Inference for Stochastic Differential Equations
Optimization Landscape and Expressivity of Deep CNNs
Spotlight: Optimizing Device Placement for Training Deep Neural Networks
Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines
A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates
Stability and Generalization of Learning Algorithms that Converge to Global Optima
Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks
Measuring abstract reasoning in neural networks
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Composite Functional Gradient Learning of Generative Adversarial Models
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
NetGAN: Generating Graphs via Random Walks
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods
Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global
An Efficient Semismooth Newton based Algorithm for Convex Clustering
Accelerated Spectral Ranking
Communication-Computation Efficient Gradient Coding
Unbiased Objective Estimation in Predictive Optimization
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration
Detecting non-causal artifacts in multivariate linear regression models
Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games
Large-Scale Cox Process Inference using Variational Fourier Features
Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits
Dynamic Regret of Strongly Adaptive Methods
Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator
Comparison-Based Random Forests
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory
Learning to Reweight Examples for Robust Deep Learning
Synthesizing Programs for Images using Reinforced Adversarial Learning
Semi-Amortized Variational Autoencoders
Fast Information-theoretic Bayesian Optimisation
K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning
Rectify Heterogeneous Models with Semantic Mapping
The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
Learning Semantic Representations for Unsupervised Domain Adaptation
Best Arm Identification in Linear Bandits with Linear Dimension Dependency
The Uncertainty Bellman Equation and Exploration
Black Box FDR
Deep Asymmetric Multi-task Feature Learning
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Dimensionality-Driven Learning with Noisy Labels
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
Learning to Explain: An Information-Theoretic Perspective on Model Interpretation
Policy Optimization with Demonstrations
Asynchronous Byzantine Machine Learning (the case of SGD)
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent
Adaptive Sampled Softmax with Kernel Based Sampling
Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
Spectrally Approximating Large Graphs with Smaller Graphs
Tight Regret Bounds for Bayesian Optimization in One Dimension
Dependent Relational Gamma Process Models for Longitudinal Networks
Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate
Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
Learning to Explore via Meta-Policy Gradient
Geodesic Convolutional Shape Optimization
Out-of-sample extension of graph adjacency spectral embedding
A Unified Framework for Structured Low-rank Matrix Learning
Learning Compact Neural Networks with Regularization
Binary Partitions with Approximate Minimum Impurity
PDE-Net: Learning PDEs from Data
Understanding Generalization and Optimization Performance of Deep CNNs
Learning the Reward Function for a Misspecified Model
Learning Representations and Generative Models for 3D Point Clouds
Stochastic Proximal Algorithms for AUC Maximization
Coordinated Exploration in Concurrent Reinforcement Learning
Video Prediction with Appearance and Motion Conditions
On the Spectrum of Random Features Maps of High Dimensional Data
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice
Neural Inverse Rendering for General Reflectance Photometric Stereo
Parallel Bayesian Network Structure Learning
Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion
Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
Junction Tree Variational Autoencoder for Molecular Graph Generation
Goodness-of-fit Testing for Discrete Distributions via Stein Discrepancy
Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning
Towards Binary-Valued Gates for Robust LSTM Training
Improved Training of Generative Adversarial Networks Using Representative Features
Message Passing Stein Variational Gradient Descent
Bayesian Quadrature for Multiple Related Integrals
Approximation Guarantees for Adaptive Sampling
End-to-end Active Object Tracking via Reinforcement Learning
Safe Element Screening for Submodular Function Minimization
Topological mixture estimation
Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection
Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization
Nonoverlap-Promoting Variable Selection
Do Outliers Ruin Collaboration?
Parallel and Streaming Algorithms for K-Core Decomposition
Stochastic Video Generation with a Learned Prior
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