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A state-space model of cross-region dynamic connectivity in MEG/EEG
Multi-step learning and underlying structure in statistical models
Fast learning rates with heavy-tailed losses
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
Incremental Variational Sparse Gaussian Process Regression
Reconstructing Parameters of Spreading Models from Partial Observations
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
Improved Techniques for Training GANs
Ancestral Causal Inference
On Regularizing Rademacher Observation Losses
A Simple Practical Accelerated Method for Finite Sums
Ladder Variational Autoencoders
High Dimensional Structured Superposition Models
SDP Relaxation with Randomized Rounding for Energy Disaggregation
Achieving budget-optimality with adaptive schemes in crowdsourcing
Combining Adversarial Guarantees and Stochastic Fast Rates in Online Learning
Feature-distributed sparse regression: a screen-and-clean approach
Dense Associative Memory for Pattern Recognition
Monday Posters
Deep Learning without Poor Local Minima
Doubly Convolutional Neural Networks
A Multi-Batch L-BFGS Method for Machine Learning
Mixed Linear Regression with Multiple Components
Structured Matrix Recovery via the Generalized Dantzig Selector
Sparse Support Recovery with Non-smooth Loss Functions
beta-risk: a New Surrogate Risk for Learning from Weakly Labeled Data
Batched Gaussian Process Bandit Optimization via Determinantal Point Processes
Guided Policy Search via Approximate Mirror Descent
NESTT: A Nonconvex Primal-Dual Splitting Method for Distributed and Stochastic Optimization
Stochastic Gradient Methods for Distributionally Robust Optimization with f-divergences
Dimensionality Reduction of Massive Sparse Datasets Using Coresets
Stochastic Optimization for Large-scale Optimal Transport
Clustering with Same-Cluster Queries
Launch and Iterate: Reducing Prediction Churn
Using Fast Weights to Attend to the Recent Past
Robust Spectral Detection of Global Structures in the Data by Learning a Regularization
Inference by Reparameterization in Neural Population Codes
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Data Poisoning Attacks on Factorization-Based Collaborative Filtering
Gaussian Processes for Survival Analysis
Without-Replacement Sampling for Stochastic Gradient Methods
Local Similarity-Aware Deep Feature Embedding
Correlated-PCA: Principal Components' Analysis when Data and Noise are Correlated
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity
Efficient Nonparametric Smoothness Estimation
Long-term Causal Effects via Behavioral Game Theory
(Withdrawn)Only H is left: Near-tight Episodic PAC RL
Multiple-Play Bandits in the Position-Based Model
Differential Privacy without Sensitivity
Generalized Correspondence-LDA Models (GC-LDA) for Identifying Functional Regions in the Brain
Hierarchical Clustering via Spreading Metrics
Variance Reduction in Stochastic Gradient Langevin Dynamics
Fast Mixing Markov Chains for Strongly Rayleigh Measures, DPPs, and Constrained Sampling
Online Pricing with Strategic and Patient Buyers
Unsupervised Learning of 3D Structure from Images
Blazing the trails before beating the path: Sample-efficient Monte-Carlo planning
Synthesis of MCMC and Belief Propagation
Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences
Matrix Completion has No Spurious Local Minimum
MetaGrad: Multiple Learning Rates in Online Learning
The Multiple Quantile Graphical Model
Fast and Provably Good Seedings for k-Means
Unsupervised Feature Extraction by Time-Contrastive Learning and Nonlinear ICA
Tractable Operations for Arithmetic Circuits of Probabilistic Models
Deep Learning for Predicting Human Strategic Behavior
Supervised learning through the lens of compression
Testing for Differences in Gaussian Graphical Models: Applications to Brain Connectivity
Unsupervised Learning from Noisy Networks with Applications to Hi-C Data
Graphons, mergeons, and so on!
Threshold Bandits, With and Without Censored Feedback
Learning from Rational Behavior: Predicting Solutions to Unknown Linear Programs
Improved Error Bounds for Tree Representations of Metric Spaces
SoundNet: Learning Sound Representations from Unlabeled Video
Maximizing Influence in an Ising Network: A Mean-Field Optimal Solution
A Powerful Generative Model Using Random Weights for the Deep Image Representation
A Bio-inspired Redundant Sensing Architecture
An Architecture for Deep, Hierarchical Generative Models
Learning Supervised PageRank with Gradient-Based and Gradient-Free Optimization Methods
Higher-Order Factorization Machines
Mistake Bounds for Binary Matrix Completion
Completely random measures for modelling block-structured sparse networks
Fairness in Learning: Classic and Contextual Bandits
A posteriori error bounds for joint matrix decomposition problems
DISCO Nets : DISsimilarity COefficients Networks
Linear Relaxations for Finding Diverse Elements in Metric Spaces
Communication-Optimal Distributed Clustering
MoCap-guided Data Augmentation for 3D Pose Estimation in the Wild
Object based Scene Representations using Fisher Scores of Local Subspace Projections
Improved Dropout for Shallow and Deep Learning
Regret of Queueing Bandits
Combinatorial semi-bandit with known covariance
On Robustness of Kernel Clustering
Bayesian Intermittent Demand Forecasting for Large Inventories
Hierarchical Object Representation for Open-Ended Object Category Learning and Recognition
Examples are not enough, learn to criticize! Criticism for Interpretability
Relevant sparse codes with variational information bottleneck
Interpretable Distribution Features with Maximum Testing Power
Beyond Exchangeability: The Chinese Voting Process
Bayesian Optimization with Robust Bayesian Neural Networks
Showing versus doing: Teaching by demonstration
The Multiscale Laplacian Graph Kernel
Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
Large-Scale Price Optimization via Network Flow
Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
Generalization of ERM in Stochastic Convex Optimization: The Dimension Strikes Back
Universal Correspondence Network
Learning to Poke by Poking: Experiential Learning of Intuitive Physics
Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images
Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks
Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation
Stochastic Online AUC Maximization
Orthogonal Random Features
End-to-End Kernel Learning with Supervised Convolutional Kernel Networks
Accelerating Stochastic Composition Optimization
Poisson-Gamma dynamical systems
Regularized Nonlinear Acceleration
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks
Single-Image Depth Perception in the Wild
R-FCN: Object Detection via Region-based Fully Convolutional Networks
Learning What and Where to Draw
Linear Contextual Bandits with Knapsacks
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
Supervised Word Mover's Distance
Deep Neural Networks with Inexact Matching for Person Re-Identification
Learning Bound for Parameter Transfer Learning
Average-case hardness of RIP certification
Can Active Memory Replace Attention?
Measuring the reliability of MCMC inference with bidirectional Monte Carlo
Selective inference for group-sparse linear models
CliqueCNN: Deep Unsupervised Exemplar Learning
Structured Sparse Regression via Greedy Hard Thresholding
Infinite Hidden Semi-Markov Modulated Interaction Point Process
Professor Forcing: A New Algorithm for Training Recurrent Networks
Multistage Campaigning in Social Networks
Learning in Games: Robustness of Fast Convergence
Coordinate-wise Power Method
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
Discriminative Gaifman Models
Barzilai-Borwein Step Size for Stochastic Gradient Descent
Sequential Neural Models with Stochastic Layers
Pruning Random Forests for Prediction on a Budget
Dynamic Network Surgery for Efficient DNNs
Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
Learning Sensor Multiplexing Design through Back-propagation
Maximization of Approximately Submodular Functions
Deep ADMM-Net for Compressive Sensing MRI
Combining Low-Density Separators with CNNs
Active Nearest-Neighbor Learning in Metric Spaces
High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
Select-and-Sample for Spike-and-Slab Sparse Coding
Adversarial Multiclass Classification: A Risk Minimization Perspective
Learning Influence Functions from Incomplete Observations
Bayesian latent structure discovery from multi-neuron recordings
Wasserstein Training of Restricted Boltzmann Machines
Fast recovery from a union of subspaces
Learning Deep Embeddings with Histogram Loss
Solving Marginal MAP Problems with NP Oracles and Parity Constraints
Swapout: Learning an ensemble of deep architectures
Double Thompson Sampling for Dueling Bandits
Refined Lower Bounds for Adversarial Bandits
A Unified Approach for Learning the Parameters of Sum-Product Networks
Assortment Optimization Under the Mallows model
General Tensor Spectral Co-clustering for Higher-Order Data
Dimension-Free Iteration Complexity of Finite Sum Optimization Problems
A Consistent Regularization Approach for Structured Prediction
Low-Rank Regression with Tensor Responses
CRF-CNN: Modeling Structured Information in Human Pose Estimation
Linear dynamical neural population models through nonlinear embeddings
Dynamic matrix recovery from incomplete observations under an exact low-rank constraint
Kernel Bayesian Inference with Posterior Regularization
Adaptive Averaging in Accelerated Descent Dynamics
Operator Variational Inference
Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation
SPALS: Fast Alternating Least Squares via Implicit Leverage Scores Sampling
Estimating the Size of a Large Network and its Communities from a Random Sample
Rényi Divergence Variational Inference
Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making
Backprop KF: Learning Discriminative Deterministic State Estimators
Regret Bounds for Non-decomposable Metrics with Missing Labels
Bayesian Optimization for Probabilistic Programs
Disease Trajectory Maps
Minimax Optimal Alternating Minimization for Kernel Nonparametric Tensor Learning
Learning under uncertainty: a comparison between R-W and Bayesian approach
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
On Mixtures of Markov Chains
Online Bayesian Moment Matching for Topic Modeling with Unknown Number of Topics
What Makes Objects Similar: A Unified Multi-Metric Learning Approach
Optimal Black-Box Reductions Between Optimization Objectives
Efficient Globally Convergent Stochastic Optimization for Canonical Correlation Analysis
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation
Cyclades: Conflict-free Asynchronous Machine Learning
Convolutional Neural Fabrics
Mixed vine copulas as joint models of spike counts and local field potentials
On the Recursive Teaching Dimension of VC Classes
Stochastic Structured Prediction under Bandit Feedback
Dialog-based Language Learning
A Sparse Interactive Model for Matrix Completion with Side Information
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
Proximal Deep Structured Models
A Pseudo-Bayesian Algorithm for Robust PCA
Single Pass PCA of Matrix Products
Learning values across many orders of magnitude
Unsupervised Learning of Spoken Language with Visual Context
Structured Prediction Theory Based on Factor Graph Complexity
Variational Inference in Mixed Probabilistic Submodular Models
Variational Autoencoder for Deep Learning of Images, Labels and Captions
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Split LBI: An Iterative Regularization Path with Structural Sparsity
Online Convex Optimization with Unconstrained Domains and Losses
New Liftable Classes for First-Order Probabilistic Inference
Safe Policy Improvement by Minimizing Robust Baseline Regret
Fast and Flexible Monotonic Functions with Ensembles of Lattices
Error Analysis of Generalized Nyström Kernel Regression
Approximate maximum entropy principles via Goemans-Williamson with applications to provable variational methods
Parameter Learning for Log-supermodular Distributions
Breaking the Bandwidth Barrier: Geometrical Adaptive Entropy Estimation
Unifying Count-Based Exploration and Intrinsic Motivation
Architectural Complexity Measures of Recurrent Neural Networks
Learning feed-forward one-shot learners
Exact Recovery of Hard Thresholding Pursuit
A Multi-step Inertial Forward-Backward Splitting Method for Non-convex Optimization
Asynchronous Parallel Greedy Coordinate Descent
Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels
The Power of Adaptivity in Identifying Statistical Alternatives
A Probabilistic Framework for Deep Learning
End-to-End Goal-Driven Web Navigation
Structure-Blind Signal Recovery
Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition
CMA-ES with Optimal Covariance Update and Storage Complexity
Solving Random Systems of Quadratic Equations via Truncated Generalized Gradient Flow
Large Margin Discriminant Dimensionality Reduction in Prediction Space
Latent Attention For If-Then Program Synthesis
Tight Complexity Bounds for Optimizing Composite Objectives
The Sound of APALM Clapping: Faster Nonsmooth Nonconvex Optimization with Stochastic Asynchronous PALM
Tagger: Deep Unsupervised Perceptual Grouping
A Probabilistic Programming Approach To Probabilistic Data Analysis
Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
An ensemble diversity approach to supervised binary hashing
A scaled Bregman theorem with applications
An Efficient Streaming Algorithm for the Submodular Cover Problem
Conditional Image Generation with PixelCNN Decoders
Spatiotemporal Residual Networks for Video Action Recognition
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
Improved Deep Metric Learning with Multi-class N-pair Loss Objective
Adaptive Neural Compilation
Natural-Parameter Networks: A Class of Probabilistic Neural Networks
Learning Bayesian networks with ancestral constraints
The Robustness of Estimator Composition
Automatic Neuron Detection in Calcium Imaging Data Using Convolutional Networks
Privacy Odometers and Filters: Pay-as-you-Go Composition
Brains on Beats
Local Minimax Complexity of Stochastic Convex Optimization
Bayesian optimization under mixed constraints with a slack-variable augmented Lagrangian
Exploiting the Structure: Stochastic Gradient Methods Using Raw Clusters
Designing smoothing functions for improved worst-case competitive ratio in online optimization
Leveraging Sparsity for Efficient Submodular Data Summarization
A Communication-Efficient Parallel Algorithm for Decision Tree
The Forget-me-not Process
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
Kronecker Determinantal Point Processes
Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervision
Scalable Adaptive Stochastic Optimization Using Random Projections
Preference Completion from Partial Rankings
Clustering Signed Networks with the Geometric Mean of Laplacians
Spectral Learning of Dynamic Systems from Nonequilibrium Data
Generating Videos with Scene Dynamics
A Theoretically Grounded Application of Dropout in Recurrent Neural Networks
Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes
Optimal Architectures in a Solvable Model of Deep Networks
Value Iteration Networks
Learning Structured Sparsity in Deep Neural Networks
Global Analysis of Expectation Maximization for Mixtures of Two Gaussians
Optimal Tagging with Markov Chain Optimization
Efficient Neural Codes under Metabolic Constraints
Minimizing Quadratic Functions in Constant Time
Learning Deep Parsimonious Representations
Learned Region Sparsity and Diversity Also Predicts Visual Attention
Bayesian optimization for automated model selection
Blind Attacks on Machine Learners
Unified Methods for Exploiting Piecewise Linear Structure in Convex Optimization
Adaptive Concentration Inequalities for Sequential Decision Problems
Towards Unifying Hamiltonian Monte Carlo and Slice Sampling
The Generalized Reparameterization Gradient
Short-Dot: Computing Large Linear Transforms Distributedly Using Coded Short Dot Products
Learning HMMs with Nonparametric Emissions via Spectral Decompositions of Continuous Matrices
A Credit Assignment Compiler for Joint Prediction
Spatio-Temporal Hilbert Maps for Continuous Occupancy Representation in Dynamic Environments
Cooperative Graphical Models
Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models
An equivalence between high dimensional Bayes optimal inference and M-estimation
Sub-sampled Newton Methods with Non-uniform Sampling
Stochastic Gradient Geodesic MCMC Methods
Optimistic Gittins Indices
Learning Multiagent Communication with Backpropagation
Integrated perception with recurrent multi-task neural networks
Coresets for Scalable Bayesian Logistic Regression
Optimal Binary Classifier Aggregation for General Losses
Conditional Generative Moment-Matching Networks
The Parallel Knowledge Gradient Method for Batch Bayesian Optimization
Mapping Estimation for Discrete Optimal Transport
Optimizing affinity-based binary hashing using auxiliary coordinates
Fast and accurate spike sorting of high-channel count probes with KiloSort
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing
Using Social Dynamics to Make Individual Predictions: Variational Inference with a Stochastic Kinetic Model
A Minimax Approach to Supervised Learning
Fast Distributed Submodular Cover: Public-Private Data Summarization
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Multimodal Residual Learning for Visual QA
Joint Line Segmentation and Transcription for End-to-End Handwritten Paragraph Recognition
Learning Additive Exponential Family Graphical Models via $\ell_{2,1}$-norm Regularized M-Estimation
Residual Networks Behave Like Ensembles of Relatively Shallow Networks
A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics
Full-Capacity Unitary Recurrent Neural Networks
Reshaped Wirtinger Flow for Solving Quadratic System of Equations
Domain Separation Networks
VIME: Variational Information Maximizing Exploration
A primal-dual method for conic constrained distributed optimization problems
Optimal Sparse Linear Encoders and Sparse PCA
Human Decision-Making under Limited Time
Deconvolving Feedback Loops in Recommender Systems
Training and Evaluating Multimodal Word Embeddings with Large-scale Web Annotated Images
Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization
Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles
Even Faster SVD Decomposition Yet Without Agonizing Pain
Quantum Perceptron Models
Variational Information Maximization for Feature Selection
SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
Neural Universal Discrete Denoiser
On Multiplicative Integration with Recurrent Neural Networks
Simple and Efficient Weighted Minwise Hashing
Statistical Inference for Pairwise Graphical Models Using Score Matching
Sublinear Time Orthogonal Tensor Decomposition
Depth from a Single Image by Harmonizing Overcomplete Local Network Predictions
Linear Feature Encoding for Reinforcement Learning
Joint quantile regression in vector-valued RKHSs
Eliciting Categorical Data for Optimal Aggregation
An algorithm for L1 nearest neighbor search via monotonic embedding
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification
Semiparametric Differential Graph Models
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods
Learning User Perceived Clusters with Feature-Level Supervision
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
Graphical Time Warping for Joint Alignment of Multiple Curves
Efficient state-space modularization for planning: theory, behavioral and neural signatures
Combinatorial Energy Learning for Image Segmentation
On Valid Optimal Assignment Kernels and Applications to Graph Classification
Learnable Visual Markers
Hardness of Online Sleeping Combinatorial Optimization Problems
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Interpretable Nonlinear Dynamic Modeling of Neural Trajectories
Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy
Constraints Based Convex Belief Propagation
Stochastic Variance Reduction Methods for Saddle-Point Problems
Exponential expressivity in deep neural networks through transient chaos
GAP Safe Screening Rules for Sparse-Group Lasso
Identification and Overidentification of Linear Structural Equation Models
Towards Conceptual Compression
Can Peripheral Representations Improve Clutter Metrics on Complex Scenes?
Visual Question Answering with Question Representation Update (QRU)
Optimal spectral transportation with application to music transcription
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings
Learning Treewidth-Bounded Bayesian Networks with Thousands of Variables
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities
Agnostic Estimation for Misspecified Phase Retrieval Models
Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation
Variational Bayes on Monte Carlo Steroids
The Product Cut
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Feature selection in functional data classification with recursive maxima hunting
Budgeted stream-based active learning via adaptive submodular maximization
Direct Feedback Alignment Provides Learning in Deep Neural Networks
Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition
An Online Sequence-to-Sequence Model Using Partial Conditioning
Memory-Efficient Backpropagation Through Time
How Deep is the Feature Analysis underlying Rapid Visual Categorization?
Deep Learning Games
Anchor-Free Correlated Topic Modeling: Identifiability and Algorithm
Multivariate tests of association based on univariate tests
Optimal Learning for Multi-pass Stochastic Gradient Methods
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much
SURGE: Surface Regularized Geometry Estimation from a Single Image
Minimizing Regret on Reflexive Banach Spaces and Nash Equilibria in Continuous Zero-Sum Games
Fast Active Set Methods for Online Spike Inference from Calcium Imaging
LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
Nested Mini-Batch K-Means
“Congruent” and “Opposite” Neurons: Sisters for Multisensory Integration and Segregation
Homotopy Smoothing for Non-Smooth Problems with Lower Complexity than $O(1/\epsilon)$
Statistical Inference for Cluster Trees
Lifelong Learning with Weighted Majority Votes
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators
Density Estimation via Discrepancy Based Adaptive Sequential Partition
Diffusion-Convolutional Neural Networks
Convex Two-Layer Modeling with Latent Structure
Bi-Objective Online Matching and Submodular Allocations
A Locally Adaptive Normal Distribution
Contextual semibandits via supervised learning oracles
One-vs-Each Approximation to Softmax for Scalable Estimation of Probabilities
Supervised Learning with Tensor Networks
Generative Adversarial Imitation Learning
Satisfying Real-world Goals with Dataset Constraints
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
Understanding Probabilistic Sparse Gaussian Process Approximations
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering
Fundamental Limits of Budget-Fidelity Trade-off in Label Crowdsourcing
Hypothesis Testing in Unsupervised Domain Adaptation with Applications in Alzheimer's Disease
Faster Projection-free Convex Optimization over the Spectrahedron
Causal Bandits: Learning Good Interventions via Causal Inference
Fast Algorithms for Robust PCA via Gradient Descent
Data driven estimation of Laplace-Beltrami operator
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++
Unsupervised Domain Adaptation with Residual Transfer Networks
Computational and Statistical Tradeoffs in Learning to Rank
A state-space model of cross-region dynamic connectivity in MEG/EEG
The Power of Optimization from Samples
Efficient Second Order Online Learning by Sketching
Unsupervised Risk Estimation Using Only Conditional Independence Structure
Global Optimality of Local Search for Low Rank Matrix Recovery
Observational-Interventional Priors for Dose-Response Learning
DeepMath - Deep Sequence Models for Premise Selection
Pairwise Choice Markov Chains
Combinatorial Multi-Armed Bandit with General Reward Functions
Iterative Refinement of the Approximate Posterior for Directed Belief Networks
Adaptive Maximization of Pointwise Submodular Functions With Budget Constraint
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability
Learning to learn by gradient descent by gradient descent
Bayesian Optimization with a Finite Budget: An Approximate Dynamic Programming Approach
Hierarchical Question-Image Co-Attention for Visual Question Answering
Near-Optimal Smoothing of Structured Conditional Probability Matrices
Dual Learning for Machine Translation
Probabilistic Inference with Generating Functions for Poisson Latent Variable Models
Dual Space Gradient Descent for Online Learning
The Multi-fidelity Multi-armed Bandit
An urn model for majority voting in classification ensembles
Data Programming: Creating Large Training Sets, Quickly
Optimal Cluster Recovery in the Labeled Stochastic Block Model
Automated scalable segmentation of neurons from multispectral images
Learning shape correspondence with anisotropic convolutional neural networks
Edge-exchangeable graphs and sparsity
Verification Based Solution for Structured MAB Problems
Probing the Compositionality of Intuitive Functions
Neurally-Guided Procedural Models: Amortized Inference for Procedural Graphics Programs using Neural Networks
Learning and Forecasting Opinion Dynamics in Social Networks
CNNpack: Packing Convolutional Neural Networks in the Frequency Domain
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering
Safe and Efficient Off-Policy Reinforcement Learning
Sample Complexity of Automated Mechanism Design
Scaling Factorial Hidden Markov Models: Stochastic Variational Inference without Messages
Efficient and Robust Spiking Neural Circuit for Navigation Inspired by Echolocating Bats
Yggdrasil: An Optimized System for Training Deep Decision Trees at Scale
Theoretical Comparisons of Positive-Unlabeled Learning against Positive-Negative Learning
Deep Exploration via Bootstrapped DQN
A Bandit Framework for Strategic Regression
Learning brain regions via large-scale online structured sparse dictionary learning
Search Improves Label for Active Learning
Kernel Observers: Systems-Theoretic Modeling and Inference of Spatiotemporally Evolving Processes
Deep Learning Models of the Retinal Response to Natural Scenes
Tracking the Best Expert in Non-stationary Stochastic Environments
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition
Coupled Generative Adversarial Networks
DECOrrelated feature space partitioning for distributed sparse regression
Disentangling factors of variation in deep representation using adversarial training
Geometric Dirichlet Means Algorithm for topic inference
Consistent Kernel Mean Estimation for Functions of Random Variables
Online and Differentially-Private Tensor Decomposition
Coin Betting and Parameter-Free Online Learning
Matching Networks for One Shot Learning
Distributed Flexible Nonlinear Tensor Factorization
A Comprehensive Linear Speedup Analysis for Asynchronous Stochastic Parallel Optimization from Zeroth-Order to First-Order
Multi-armed Bandits: Competing with Optimal Sequences
Learning Parametric Sparse Models for Image Super-Resolution
Stochastic Gradient MCMC with Stale Gradients
Adaptive Skills Adaptive Partitions (ASAP)
Maximal Sparsity with Deep Networks?
Riemannian SVRG: Fast Stochastic Optimization on Riemannian Manifolds
Robustness of classifiers: from adversarial to random noise
Interaction Screening: Efficient and Sample-Optimal Learning of Ising Models
Catching heuristics are optimal control policies
Learning Kernels with Random Features
Efficient High-Order Interaction-Aware Feature Selection Based on Conditional Mutual Information
Normalized Spectral Map Synchronization
On Explore-Then-Commit strategies
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula
Stochastic Gradient Richardson-Romberg Markov Chain Monte Carlo
Flexible Models for Microclustering with Application to Entity Resolution
Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations
High-Rank Matrix Completion and Clustering under Self-Expressive Models
Probabilistic Linear Multistep Methods
Dynamic Mode Decomposition with Reproducing Kernels for Koopman Spectral Analysis
The non-convex Burer-Monteiro approach works on smooth semidefinite programs
Stochastic Three-Composite Convex Minimization
Greedy Feature Construction
Learning the Number of Neurons in Deep Networks
Active Learning from Imperfect Labelers
Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics
More Supervision, Less Computation: Statistical-Computational Tradeoffs in Weakly Supervised Learning
Deep Submodular Functions: Definitions and Learning
Strategic Attentive Writer for Learning Macro-Actions
Scaled Least Squares Estimator for GLMs in Large-Scale Problems
Tree-Structured Reinforcement Learning for Sequential Object Localization
Stochastic Variational Deep Kernel Learning
Convergence guarantees for kernel-based quadrature rules in misspecified settings
Dueling Bandits: Beyond Condorcet Winners to General Tournament Solutions
Learning a Metric Embedding for Face Recognition using the Multibatch Method
Community Detection on Evolving Graphs
Clustering with Bregman Divergences: an Asymptotic Analysis
Computing and maximizing influence in linear threshold and triggering models
Optimistic Bandit Convex Optimization
Sampling for Bayesian Program Learning
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
Crowdsourced Clustering: Querying Edges vs Triangles
Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain
The Limits of Learning with Missing Data
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes
Causal meets Submodular: Subset Selection with Directed Information
Improving Variational Autoencoders with Inverse Autoregressive Flow
FPNN: Field Probing Neural Networks for 3D Data
Boosting with Abstention
Estimating the class prior and posterior from noisy positives and unlabeled data
Adaptive Smoothed Online Multi-Task Learning
Bootstrap Model Aggregation for Distributed Statistical Learning
Noise-Tolerant Life-Long Matrix Completion via Adaptive Sampling
Generating Long-term Trajectories Using Deep Hierarchical Networks
Review Networks for Caption Generation
Robust k-means: a Theoretical Revisit
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares
Cooperative Inverse Reinforcement Learning
Improving PAC Exploration Using the Median Of Means
Dynamic Filter Networks
Learning Infinite RBMs with Frank-Wolfe
Unsupervised Learning for Physical Interaction through Video Prediction
Sorting out typicality with the inverse moment matrix SOS polynomial
Generating Images with Perceptual Similarity Metrics based on Deep Networks
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction
Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in the Blanks
Threshold Learning for Optimal Decision Making
Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning
Composing graphical models with neural networks for structured representations and fast inference
Finding significant combinations of features in the presence of categorical covariates
Algorithms and matching lower bounds for approximately-convex optimization
Tensor Switching Networks
PAC Reinforcement Learning with Rich Observations
Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization
Active Learning with Oracle Epiphany
Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
A Non-generative Framework and Convex Relaxations for Unsupervised Learning
Estimating Nonlinear Neural Response Functions using GP Priors and Kronecker Methods
Binarized Neural Networks
Reward Augmented Maximum Likelihood for Neural Structured Prediction
Equality of Opportunity in Supervised Learning
Learning Tree Structured Potential Games
PAC-Bayesian Theory Meets Bayesian Inference
Graph Clustering: Block-models and model free results
Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers
A Probabilistic Model of Social Decision Making based on Reward Maximization
k*-Nearest Neighbors: From Global to Local
Learning Transferrable Representations for Unsupervised Domain Adaptation
Exponential Family Embeddings
Interaction Networks for Learning about Objects, Relations and Physics
Measuring Neural Net Robustness with Constraints
A forward model at Purkinje cell synapses facilitates cerebellar anticipatory control
Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Nearly Isometric Embedding by Relaxation
A Bayesian method for reducing bias in neural representational similarity analysis
Adaptive optimal training of animal behavior
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