NIPS 2015 Accepted Papers





Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing

Learning with Symmetric Label Noise: The Importance of Being Unhinged

Algorithmic Stability and Uniform Generalization

Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models

Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling

Robust Portfolio Optimization

Logarithmic Time Online Multiclass prediction

Planar Ultrametric Rounding for Image Segmentation

Expressing an Image Stream with a Sequence of Natural Sentences

Parallel Correlation Clustering on Big Graphs

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Space-Time Local Embeddings

A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements

Smooth Interactive Submodular Set Cover

Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning

On the Pseudo-Dimension of Nearly Optimal Auctions

Unlocking neural population non-stationarities using hierarchical dynamics models

Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM)

Color Constancy by Learning to Predict Chromaticity from Luminance

Fast and Accurate Inference of Plackett–Luce Models

Probabilistic Line Searches for Stochastic Optimization

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

Where are they looking?

Minimax Regret for Unfair Bandits 004

On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors

Measuring Sample Quality with Stein's Method

Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution

Bounding errors of Expectation-Propagation

A fast, universal algorithm to learn parametric nonlinear embeddings

Texture synthesis and the controlled generation of natural stimuli using convolutional neural networks

Extending Gossip Algorithms to Distributed Estimation of U-statistics

Streaming, Distributed Variational Inference for Bayesian Nonparametrics

Learning visual biases from human imagination

Smooth and Strong: MAP Inference with Linear Convergence

Copeland Dueling Bandits

Optimal Ridge Detection using Coverage Risk

Top-$k$ Multiclass SVM

Policy Evaluation Using the Ω-Return

Orthogonal NMF through Subspace Exploration

Stochastic Online Greedy Learning with Semi-bandit Feedbacks

Deeply Learning the Messages in Message Passing Inference

Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring

Accelerated Proximal Gradient Methods for Nonconvex Programming

Approximating Sparse PCA from Incomplete Data

Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations

Column Selection via Adaptive Sampling

HONOR: Hybrid Optimization for NOn-convex Regularized problems

3D Object Proposals for Accurate Object Class Detection

Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits

Tensorizing Neural Networks

Parallelizing MCMC with Random Partition Trees

A Reduced-Dimension fMRI Shared Response Model

Spectral Learning of Large Structured HMMs for Comparative Epigenomics

Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability

Estimating Mixture Models via Mixtures of Polynomials

On the Global Linear Convergence of Frank-Wolfe Optimization Variants

Deep Knowledge Tracing

Moment matching for LDA and discrete ICA

Efficient Compressive Phase Retrieval with Constrained Sensing Vectors

Barrier Frank-Wolfe for Marginal Inference

Learning Theory and Algorithms for Forecasting Non-stationary Time Series

Compressive spectral embedding: sidestepping the SVD

A Nonconvex Optimization Framework for Low Rank Matrix Estimation

Automatic Variational Inference in Stan

Attention-Based Models for Speech Recognition

Closed-form Estimators for High-dimensional Generalized Linear Models

Online F-Measure Optimization

Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach

On Submodularity of M-Best-Diverse-Labelings

Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number

Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring

Training Restricted Boltzmann Machine via the ￼Thouless-Anderson-Palmer free energy

Character-level Convolutional Networks for Text Classification

Semi-Supervised Robust Feature-Sample Linear Discriminant Analysis for Neurodegenerative Brain Disorders Diagnosis

Black-box optimization of noisy functions with unknown smoothness

Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters

Deep learning with Elastic Averaging SGD

Monotone k-Submodular Function Maximization with Size Constraints

Active Learning from Weak and Strong Labelers

On the Optimality of Classifier Chain for Multi-label Classification

Robust Regression via Hard Thresholding

Locally Non-linear Embeddings for Extreme Multi-label Learning

Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems

A Hierarchical Approach to Individualized Disease Trajectory Predictions in Heterogeneous Populations

Subspace Clustering with Irrelevant Features via Robust Dantzig Selector

Sparse PCA via Bipartite Matchings

Fast Randomized Kernel Methods with Statistical Guarantees

Online Learning for Adversaries with Memory: Price of Past Mistakes

Convolutional spike-triggered covariance analysis for neural subunit models

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

GAP Safe screening rules for sparse multi-task and multi-class models

Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces

Statistical Model Criticism using Kernel Two Sample Tests

Precision-Recall-Gain Curves: PR Analysis Done Right

A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice

Bidirectional Recurrent Neural Networks as Generative Models

Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling

Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets

Hessian-Free Optimization For Learning Deep Multidimensional Recurrent Neural Networks

Large-scale probabilistic predictors with and without guarantees of validity

Shepard Convolutional Neural Networks

Manifold Optimization for Gaussian Mixture Models

Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding

Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models

Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling

Bounding the Cost of Search-Based Lifted Inference

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families

Linear Multi-Resource Allocation with Semi-Bandit Feedback

Unsupervised Learning by Program Synthesis

Enforcing balance allows local supervised learning in spiking recurrent networks

Fast and Guaranteed Tensor Decomposition via Sketching

Differentially private subspace clustering

Predtron: A Family of Online Algorithms for General Prediction Problems

Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization

SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk

On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs

The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions

Fast Classification Rates for High-dimensional Conditional Gaussian Models

Fast Distributed k-Center Clustering with Outliers on Massive Data

Human Memory Search as Initial-Visit Emitting Random Walk

Non-convex Statistical Optimization for Sparse Tensor Graphical Model

Convergence Rates of Active Learning for Maximum Likelihood Estimation

Learning to Rotate 3D Objects with Recurrent Convolutional Encoder-Decoder Networks

Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

Backpropagation for Energy-Efficient Neuromorphic Computing

Alternating Minimization for Regression Problems with Vector-valued Outputs

Learning both Weights and Connections for Efficient Neural Network

Optimal Rates for Random Fourier Features

The Population Posterior and Bayesian Inference on Streams

Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Unified View of Matrix Completion under General Structural Constraints

Efficient Output Kernel Learning for Multiple Tasks

Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models

Variational Consensus Monte Carlo

Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma

Practical and Optimal LSH for Angular Distance

Learning to Linearize Under Uncertainty

Finite-Time Analysis of Projected Langevin Monte Carlo

Deep Visual Analogy-Making

Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation

Online Learning with Adversarial Delays

Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection

Minimum Weight Perfect Matching via Blossom Belief Propagation

Efficient Thompson Sampling for Online ￼Matrix-Factorization Recommendation

Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems

Lifted Symmetry Detection and Breaking for MAP Inference

Evaluating the statistical significance of biclusters

Discriminative Robust Transformation Learning

Bandits with Unobserved Confounders: A Causal Approach

Scalable Semi-Supervised Aggregation of Classifiers

Online Learning with Gaussian Payoffs and Side Observations

Private Graphon Estimation for Sparse Graphs

SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals

Fast Second Order Stochastic Backpropagation for Variational Inference

Stronger and Faster Approximate Singular Value Decomposition via the Block Lanczos Method

Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions

Scalable Automated Inference for Gaussian Process Models

Fast Bidirectional Probability Estimation in Markov Models

Probabilistic Variational Bounds for Graphical Models

Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes

Combinatorial Cascading Bandits

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path

Policy Gradient for Coherent Risk Measures

Fast Rates for Exp-concave Empirical Risk Minimization

Deep Generative Image Models using a ￼Laplacian Pyramid of Adversarial Networks

Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation

Equilibrated adaptive learning rates for non-convex optimization

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions

Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach

Asynchronous stochastic approximation: the noise is in the noise and SGD don't care

Lifelong Learning with Non-i.i.d. Tasks

Optimal Linear Estimation under Unknown Nonlinear Transform

Learning with Group Invariant Features: A Kernel Perspective.

Regularized EM Algorithms: A Unified Framework and Statistical Guarantees

Distributionally Robust Logistic Regression

Adaptive Stochastic Optimization: From Sets to Paths

Beyond Convexity: Stochastic Quasi-Convex Optimization

An Analytically Tractable Bayesian Approximation to Optimal Point Process Filtering

Sum-of-Squares Lower Bounds for Sparse PCA

Max-Margin Majority Voting for Learning from Crowds

Learning with Incremental Iterative Regularization

Halting in graph kernels

MCMC for Variationally Sparse Gaussian Processes

Less is More: Nystr\"om Computational Regularization

Infinite Factorial Dynamical Model

Regularization Path of Cross-Validation Error Lower Bounds

Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze–like Environments

Teaching Machines to Read and Comprehend

Principal Differences Analysis: Interpretable Characterization of Differences between Distributions

When are Kalman-Filter Restless Bandits Indexable?

Segregated Graphs and Marginals of Chain Graph Models

Efficient Non-greedy Optimization of Decision Trees and Forests

Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process

Inverse Reinforcement Learning with Locally Consistent Reward Functions

Communication Complexity of Distributed Convex Learning and Optimization

End-to-end Learning of Latent Dirichlet Allocation by Mirror-Descent Back Propagation

Subset Selection by Pareto Optimization

On the accuracy of self-normalized linear models

Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring

Is Approval Voting Optimal Given Approval Votes?

Regressive Virtual Metric Learning

Analysis of Robust PCA via Local Incoherence

Learning to Transduce with Unbounded Memory

Max-Margin Deep Generative Models

Spherical Random Features for Polynomial Kernels

Rectified Factor Networks

Learning Bayesian Networks with Thousands of Variables

Matrix Completion Under Monotonic Single Index Models

Visalogy: Answering Visual Analogy Questions

Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models

Streaming Min-max Hypergraph Partitioning

Collaboratively Learning Preferences from Ordinal Data

Biologically Inspired Dynamic Textures for Probing Motion Perception

Generative Image Modeling Using Spatial LSTMs

Robust PCA with compressed data

Sampling from Probabilistic Submodular Models

COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

On Predictive Belief Methods for Dynamical System Learning

Regret-Based Pruning in Extensive-Form Games

Fast Two-Sample Testing with Analytic Representations of Probability Measures

Learning to Segment Object Candidates

GP Kernels for Cross-Spectrum Analysis

Secure Multi-party Differential Privacy

Spatial Transformer Networks

Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks

Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms

High-dimensional neural spike train analysis with generalized count linear dynamical systems

Learning with a Wasserstein Loss

b-bit Marginal Regression

Natural Neural Networks

Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference

Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing

On some provably correct cases of variational inference for topic models

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Combinatorial Bandits Revisited

Stochastic Variational Information Maximisation

A Structural Smoothing Framework For Robust Graph Comparison

Competitive Distribution Estimation: Why is Good-Turing Good

Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction

A hybrid sampler for Poisson-Kingman mixture models

An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching

Local Smoothness in Variance Reduced Optimization

Saliency, Scale and Information: Towards a Unifying Theory

Fighting Bandits with a New Kind of Smoothness

Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs

Spectral Norm Regularization of Orthonormal Representations for Graph Transduction

Neural Molecular Fingerprints

Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications

Tractable Learning for Complex Probability Queries

StopWasting My Gradients: Practical SVRG

Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction

A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks

On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators

Sparsistent Estimation of Nonparametric Graphical Models

Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question

Neighborhood Watch: Stochastic Gradient Descent with Neighbors

Sample Efficient Path Integral Control under Uncertainty

Stochastic Expectation Propagation

Approximate MAP Inference in Continuous MRFs

Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients

Generalization in Adaptive Data Analysis and Holdout Reuse

Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents

Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent

Training Very Deep Networks

Bayesian Active Model Selection with an Application to Automated Audiometry

A Linear-Time Particle Gibbs Sampler for Infinite Hidden Markov Models

Learning spatiotemporal trajectories from manifold-valued longitudinal data

A Bayesian Framework for Modeling Confidence in Perceptual Decision Making

Path-SGD: Path-Normalized Optimization in Deep Neural Networks

On the consistency theory of high dimensional variable screening

End-To-End Memory Networks

Spectral Representations for Convolutional Neural Networks

Online Gradient Boosting

Deep Temporal Sigmoid Belief Networks for Sequence Modeling

Recognizing retinal ganglion cells in the dark

A Theory of Decision Making Under Dynamic Context

A Gaussian Process Model of Quasar Spectral Energy Distributions

Hidden Technical Debt in Machine Learning Systems

Local Causal Discovery

High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality

Revenue Optimization against Strategic Buyers

Deep Convolutional Inverse Graphics Network

Sparse and Low-Rank Tensor Decomposition

Minimax Time Series Prediction

Differentially Private Learning of Structured Discrete Distributions

Variational Dropout and the Local Reparameterization Trick

Sample Complexity of Learning Mahalanobis Distance Metrics

Learning Wake-Sleep Recurrent Attention Models

Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso

Testing Closeness With Unequal Sized Samples

Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach

Neural Adaptive Sequential Monte Carlo

Local Expectation Gradients for Doubly Stochastic Variational Inference

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants

NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning

Super-Resolution Off the Grid

Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms

The Return of the Gating Network: combining generative models and discriminative training in natural image priors.

Pointer Networks

Associative Memory via a Sparse Recovery Model

Robust Spectral Inference for Joint Stochastic Matrix Factorization

Fast, Provable Algorithms for Isotonic Regression in all l_p-norms

Structured Prediction Games for Multivariate Losses

Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images

Efficient and Parsimonious Agnostic Active Learning

Softstar: Softened Heuristic-based Inference

Grammar as a Foreign Language

Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices

Winner-Take-All Autoencoders

Deep Poisson Factor Modeling

Bayesian Optimization with Exponential Convergence

Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning

Learning with Relaxed Supervision

Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's

Accelerated Mirror Descent in Continuous and Discrete Time

The Human Kernel

Action-Conditional Video Prediction using Deep Networks in Atari Games

A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA

Distributed Submodular Cover: Succinctly Summarizing Massive Data

Community Detection via Measure Space Embedding

Basis refinement strategies for linear value function approximation in MDPs

Structured Estimation with Atomic Norms: General Bounds and Applications

A Complete Recipe for Stochastic Gradient MCMC

Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff

Online Prediction at the Limit of Zero Temperature

Learning Continuous Control Policies by Stochastic Value Gradients

Exploring Models and Data for Image Question Answering

Efficient and Robust Automated Machine Learning

Preconditioned Spectral Descent for Deep Learning

A Recurrent Latent Variable Model for Sequential Data

Fast Convergence of Regularized Learning in Games

Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation

IDSIA

Reflection, Refraction, and Hamiltonian Monte Carlo

The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels

Nearly Optimal Private LASSO

Convergence Analysis of Prediction Markets via Randomized Subspace Descent

The Poisson Gamma Belief Network

Convergence rates of sub-sampled Newton methods

No-Regret Learning in Repeated Bayesian Games

Statistical Topological Data Analysis - A Kernel Perspective

Unsupervised Sequence Learning

Structured Transforms for Small-Footprint Deep Learning

Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width

Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm

Sample Complexity Bounds for Iterative Stochastic Policy Optimization

BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Interactive Control of Diverse Complex Characters with Neural Networks

Submodular Hamming Metrics

A universal primal-dual convex optimization framework

Learning-curve analysis of simple decision heuristics

Explore no more: improved high-probability regret bounds for non-stochastic bandits

Fast and Memory Optimal Low-Rank Matrix Approximation

Learnability of Influence in Networks

Learning Causal Graphs with Small Interventions

Information-theoretic lower bounds for convex optimization with erroneous oracles

Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial

Large-Scale Bayesian Multi-Label Learning via Positive Labels Only

The Self-Normalized Estimator for Counterfactual Learning

Fast Lifted MAP Inference via Partitioning

Data Generation as Sequential Decision Making

On Elicitation Complexity and Conditional Elicitation

Decomposition Bounds for Marginal MAP

Inference and Feature Selection via Maximal Correlation

A class of network models recoverable by spectral clustering

Skip-Thought Vectors

Rate-Agnostic (Causal) Structure Learning

Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric

Consistent Multilabel Classification

Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions

Cornering Stationary and Restless Mixing Bandits with Remix-UCB

Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data

Gaussian Process Random Fields

M-Statistic for Kernel Change-Point Detection

Adaptive Online Learning

A Universal Catalyst for First-Order Optimization

Inference for determinantal point processes without spectral knowledge

Kullback-Leibler Proximal Variational Inference

Semi-proximal Mirror-Prox for Nonsmooth Composite Minimization

LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements

From random walks to distances on unweighted graphs

Bayesian dark knowledge

Matrix Completion with Noisy Side Information

Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation

On-the-Job Learning with Bayesian Decision Theory

Calibrated Structured Prediction

Learning Structured Output Representation using Deep Conditional Generative Models

Time-Sensitive Recommendation From Recurrent User Activities

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels

Eliciting and Aggregating Private Information

Lifted Inference Rules With Constraints

Gradient Estimation Using Stochastic Computation Graphs

Model-Based Relative Entropy Stochastic Search

Semi-supervised Learning with Ladder Network

Embedding Inference for Structured Multilabel Prediction

Variational inference with copula augmentation

Recursive 2D-3D Convolutional Networks for Neuronal Boundary Prediction

A Dual-Augmented Block Minimization Framework for Learning with Limited Memory

Optimal Testing for Families of Distributions

Efficient Continuous-Time Hidden Markov Model for Disease Modeling

Expectation Particle Belief Propagation

Latent Bayesian melding for integrating individual and population models