NeurIPS(NIPS) 2015 论文列表
Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada.
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Latent Bayesian melding for integrating individual and population models.
Expectation Particle Belief Propagation.
Efficient Learning of Continuous-Time Hidden Markov Models for Disease Progression.
Optimal Testing for Properties of Distributions.
A Dual Augmented Block Minimization Framework for Learning with Limited Memory.
Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Prediction.
Copula variational inference.
Embedding Inference for Structured Multilabel Prediction.
Semi-supervised Learning with Ladder Networks.
Model-Based Relative Entropy Stochastic Search.
Gradient Estimation Using Stochastic Computation Graphs.
Lifted Inference Rules With Constraints.
A Market Framework for Eliciting Private Data.
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels.
Time-Sensitive Recommendation From Recurrent User Activities.
Learning Structured Output Representation using Deep Conditional Generative Models.
Calibrated Structured Prediction.
On-the-Job Learning with Bayesian Decision Theory.
Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation.
Matrix Completion with Noisy Side Information.
Bayesian dark knowledge.
From random walks to distances on unweighted graphs.
LASSO with Non-linear Measurements is Equivalent to One With Linear Measurements.
Semi-Proximal Mirror-Prox for Nonsmooth Composite Minimization.
Kullback-Leibler Proximal Variational Inference.
Inference for determinantal point processes without spectral knowledge.
A Universal Catalyst for First-Order Optimization.
Adaptive Online Learning.
M-Statistic for Kernel Change-Point Detection.
Gaussian Process Random Fields.
Semi-Supervised Factored Logistic Regression for High-Dimensional Neuroimaging Data.
Cornering Stationary and Restless Mixing Bandits with Remix-UCB.
Parallel Predictive Entropy Search for Batch Global Optimization of Expensive Objective Functions.
Consistent Multilabel Classification.
Principal Geodesic Analysis for Probability Measures under the Optimal Transport Metric.
Rate-Agnostic (Causal) Structure Learning.
Skip-Thought Vectors.
A class of network models recoverable by spectral clustering.
Discrete Rényi Classifiers.
Decomposition Bounds for Marginal MAP.
On Elicitation Complexity.
Data Generation as Sequential Decision Making.
Fast Lifted MAP Inference via Partitioning.
The Self-Normalized Estimator for Counterfactual Learning.
Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings.
Fixed-Length Poisson MRF: Adding Dependencies to the Multinomial.
Information-theoretic lower bounds for convex optimization with erroneous oracles.
Learning Causal Graphs with Small Interventions.
Learnability of Influence in Networks.
Fast and Memory Optimal Low-Rank Matrix Approximation.
Explore no more: Improved high-probability regret bounds for non-stochastic bandits.
Learning From Small Samples: An Analysis of Simple Decision Heuristics.
A Universal Primal-Dual Convex Optimization Framework.
Submodular Hamming Metrics.
Interactive Control of Diverse Complex Characters with Neural Networks.
BinaryConnect: Training Deep Neural Networks with binary weights during propagations.
Sample Complexity Bounds for Iterative Stochastic Policy Optimization.
Interpolating Convex and Non-Convex Tensor Decompositions via the Subspace Norm.
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width.
Structured Transforms for Small-Footprint Deep Learning.
Semi-supervised Sequence Learning.
Statistical Topological Data Analysis - A Kernel Perspective.
No-Regret Learning in Bayesian Games.
Convergence rates of sub-sampled Newton methods.
The Poisson Gamma Belief Network.
Convergence Analysis of Prediction Markets via Randomized Subspace Descent.
Nearly Optimal Private LASSO.
The Consistency of Common Neighbors for Link Prediction in Stochastic Blockmodels.
Reflection, Refraction, and Hamiltonian Monte Carlo.
Parallel Multi-Dimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation.
Fast Convergence of Regularized Learning in Games.
A Recurrent Latent Variable Model for Sequential Data.
Preconditioned Spectral Descent for Deep Learning.
Efficient and Robust Automated Machine Learning.
Exploring Models and Data for Image Question Answering.
Learning Continuous Control Policies by Stochastic Value Gradients.
Online Prediction at the Limit of Zero Temperature.
Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff.
A Complete Recipe for Stochastic Gradient MCMC.
Structured Estimation with Atomic Norms: General Bounds and Applications.
Basis refinement strategies for linear value function approximation in MDPs.
Community Detection via Measure Space Embedding.
Distributed Submodular Cover: Succinctly Summarizing Massive Data.
A Pseudo-Euclidean Iteration for Optimal Recovery in Noisy ICA.
Action-Conditional Video Prediction using Deep Networks in Atari Games.
The Human Kernel.
Accelerated Mirror Descent in Continuous and Discrete Time.
Subsampled Power Iteration: a Unified Algorithm for Block Models and Planted CSP's.
Learning with Relaxed Supervision.
Sample Complexity of Episodic Fixed-Horizon Reinforcement Learning.
Bayesian Optimization with Exponential Convergence.
Deep Poisson Factor Modeling.
Winner-Take-All Autoencoders.
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices.
Grammar as a Foreign Language.
Softstar: Heuristic-Guided Probabilistic Inference.
Efficient and Parsimonious Agnostic Active Learning.
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images.
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization.
Adversarial Prediction Games for Multivariate Losses.
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms.
Robust Spectral Inference for Joint Stochastic Matrix Factorization.
Associative Memory via a Sparse Recovery Model.
Pointer Networks.
The Return of the Gating Network: Combining Generative Models and Discriminative Training in Natural Image Priors.
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms.
Super-Resolution Off the Grid.
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning.
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants.
Local Expectation Gradients for Black Box Variational Inference.
Neural Adaptive Sequential Monte Carlo.
Estimating Jaccard Index with Missing Observations: A Matrix Calibration Approach.
Testing Closeness With Unequal Sized Samples.
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso.
Learning Wake-Sleep Recurrent Attention Models.
Sample Complexity of Learning Mahalanobis Distance Metrics.
Variational Dropout and the Local Reparameterization Trick.
Differentially Private Learning of Structured Discrete Distributions.
Minimax Time Series Prediction.
Sparse and Low-Rank Tensor Decomposition.
Deep Convolutional Inverse Graphics Network.
Revenue Optimization against Strategic Buyers.
High Dimensional EM Algorithm: Statistical Optimization and Asymptotic Normality.
Local Causal Discovery of Direct Causes and Effects.
Hidden Technical Debt in Machine Learning Systems.
A Gaussian Process Model of Quasar Spectral Energy Distributions.
A Theory of Decision Making Under Dynamic Context.
Recognizing retinal ganglion cells in the dark.
Deep Temporal Sigmoid Belief Networks for Sequence Modeling.
Online Gradient Boosting.
Spectral Representations for Convolutional Neural Networks.
End-To-End Memory Networks.
On the consistency theory of high dimensional variable screening.
Path-SGD: Path-Normalized Optimization in Deep Neural Networks.
A Bayesian Framework for Modeling Confidence in Perceptual Decision Making.
Learning spatiotemporal trajectories from manifold-valued longitudinal data.
Particle Gibbs for Infinite Hidden Markov Models.
Bayesian Active Model Selection with an Application to Automated Audiometry.
Training Very Deep Networks.
Sparse Linear Programming via Primal and Dual Augmented Coordinate Descent.
Market Scoring Rules Act As Opinion Pools For Risk-Averse Agents.
Generalization in Adaptive Data Analysis and Holdout Reuse.
Scale Up Nonlinear Component Analysis with Doubly Stochastic Gradients.
Exactness of Approximate MAP Inference in Continuous MRFs.
Stochastic Expectation Propagation.
Sample Efficient Path Integral Control under Uncertainty.
Variance Reduced Stochastic Gradient Descent with Neighbors.
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question.
Learning structured densities via infinite dimensional exponential families.
On the Convergence of Stochastic Gradient MCMC Algorithms with High-Order Integrators.
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks.
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction.
StopWasting My Gradients: Practical SVRG.
Tractable Learning for Complex Probability Queries.
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications.
Convolutional Networks on Graphs for Learning Molecular Fingerprints.
Spectral Norm Regularization of Orthonormal Representations for Graph Transduction.
Beyond Sub-Gaussian Measurements: High-Dimensional Structured Estimation with Sub-Exponential Designs.
Fighting Bandits with a New Kind of Smoothness.
Saliency, Scale and Information: Towards a Unifying Theory.
Local Smoothness in Variance Reduced Optimization.
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching.
A hybrid sampler for Poisson-Kingman mixture models.
Efficient Learning by Directed Acyclic Graph For Resource Constrained Prediction.
Competitive Distribution Estimation: Why is Good-Turing Good.
A Structural Smoothing Framework For Robust Graph Comparison.
Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning.
Combinatorial Bandits Revisited.
Collaborative Filtering with Graph Information: Consistency and Scalable Methods.
On some provably correct cases of variational inference for topic models.
Adaptive Primal-Dual Splitting Methods for Statistical Learning and Image Processing.
Optimization Monte Carlo: Efficient and Embarrassingly Parallel Likelihood-Free Inference.
Natural Neural Networks.
b-bit Marginal Regression.
Learning with a Wasserstein Loss.
High-dimensional neural spike train analysis with generalized count linear dynamical systems.
Multi-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms.
Anytime Influence Bounds and the Explosive Behavior of Continuous-Time Diffusion Networks.
Spatial Transformer Networks.
Secure Multi-party Differential Privacy.
GP Kernels for Cross-Spectrum Analysis.
Learning to Segment Object Candidates.
Fast Two-Sample Testing with Analytic Representations of Probability Measures.
Regret-Based Pruning in Extensive-Form Games.
Supervised Learning for Dynamical System Learning.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution.
Sampling from Probabilistic Submodular Models.
Robust PCA with compressed data.
Generative Image Modeling Using Spatial LSTMs.
Biologically Inspired Dynamic Textures for Probing Motion Perception.
Collaboratively Learning Preferences from Ordinal Data.
Streaming Min-max Hypergraph Partitioning.
Tree-Guided MCMC Inference for Normalized Random Measure Mixture Models.
Visalogy: Answering Visual Analogy Questions.
Matrix Completion Under Monotonic Single Index Models.
Learning Bayesian Networks with Thousands of Variables.
Rectified Factor Networks.
Spherical Random Features for Polynomial Kernels.
Max-Margin Deep Generative Models.
Learning to Transduce with Unbounded Memory.
Analysis of Robust PCA via Local Incoherence.
Regressive Virtual Metric Learning.
Is Approval Voting Optimal Given Approval Votes?
Regret Lower Bound and Optimal Algorithm in Finite Stochastic Partial Monitoring.
On the Accuracy of Self-Normalized Log-Linear Models.
Subset Selection by Pareto Optimization.
End-to-end Learning of LDA by Mirror-Descent Back Propagation over a Deep Architecture.
Communication Complexity of Distributed Convex Learning and Optimization.
Inverse Reinforcement Learning with Locally Consistent Reward Functions.
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian Process.
Efficient Non-greedy Optimization of Decision Trees.
Segregated Graphs and Marginals of Chain Graph Models.
When are Kalman-Filter Restless Bandits Indexable?
Principal Differences Analysis: Interpretable Characterization of Differences between Distributions.
Teaching Machines to Read and Comprehend.
Attractor Network Dynamics Enable Preplay and Rapid Path Planning in Maze-like Environments.
Regularization Path of Cross-Validation Error Lower Bounds.
Infinite Factorial Dynamical Model.
Less is More: Nyström Computational Regularization.
MCMC for Variationally Sparse Gaussian Processes.
Halting in Random Walk Kernels.
Learning with Incremental Iterative Regularization.
Max-Margin Majority Voting for Learning from Crowds.
Sum-of-Squares Lower Bounds for Sparse PCA.
A Tractable Approximation to Optimal Point Process Filtering: Application to Neural Encoding.
Beyond Convexity: Stochastic Quasi-Convex Optimization.
Adaptive Stochastic Optimization: From Sets to Paths.
Distributionally Robust Logistic Regression.
Regularized EM Algorithms: A Unified Framework and Statistical Guarantees.
Learning with Group Invariant Features: A Kernel Perspective.
Optimal Linear Estimation under Unknown Nonlinear Transform.
Lifelong Learning with Non-i.i.d. Tasks.
Asynchronous stochastic convex optimization: the noise is in the noise and SGD don't care.
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach.
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions.
Equilibrated adaptive learning rates for non-convex optimization.
Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation.
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks.
Fast Rates for Exp-concave Empirical Risk Minimization.
Policy Gradient for Coherent Risk Measures.
Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path.
Combinatorial Cascading Bandits.
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes.
Probabilistic Variational Bounds for Graphical Models.
Fast Bidirectional Probability Estimation in Markov Models.
Scalable Inference for Gaussian Process Models with Black-Box Likelihoods.
Cross-Domain Matching for Bag-of-Words Data via Kernel Embeddings of Latent Distributions.
Randomized Block Krylov Methods for Stronger and Faster Approximate Singular Value Decomposition.
Fast Second Order Stochastic Backpropagation for Variational Inference.
SubmodBoxes: Near-Optimal Search for a Set of Diverse Object Proposals.
Private Graphon Estimation for Sparse Graphs.
Online Learning with Gaussian Payoffs and Side Observations.
Scalable Semi-Supervised Aggregation of Classifiers.
Bandits with Unobserved Confounders: A Causal Approach.
Discriminative Robust Transformation Learning.
Evaluating the statistical significance of biclusters.
Lifted Symmetry Detection and Breaking for MAP Inference.
Improved Iteration Complexity Bounds of Cyclic Block Coordinate Descent for Convex Problems.
Efficient Thompson Sampling for Online Matrix-Factorization Recommendation.
Minimum Weight Perfect Matching via Blossom Belief Propagation.
Multi-Layer Feature Reduction for Tree Structured Group Lasso via Hierarchical Projection.
Online Learning with Adversarial Delays.
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation.
Deep Visual Analogy-Making.
Finite-Time Analysis of Projected Langevin Monte Carlo.
Learning to Linearize Under Uncertainty.
Practical and Optimal LSH for Angular Distance.
Newton-Stein Method: A Second Order Method for GLMs via Stein's Lemma.
Variational Consensus Monte Carlo.
Scalable Adaptation of State Complexity for Nonparametric Hidden Markov Models.
Efficient Output Kernel Learning for Multiple Tasks.
Unified View of Matrix Completion under General Structural Constraints.
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks.
Frank-Wolfe Bayesian Quadrature: Probabilistic Integration with Theoretical Guarantees.
The Population Posterior and Bayesian Modeling on Streams.
Optimal Rates for Random Fourier Features.
Learning both Weights and Connections for Efficient Neural Network.
Alternating Minimization for Regression Problems with Vector-valued Outputs.
Backpropagation for Energy-Efficient Neuromorphic Computing.
Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets.
Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis.
Convergence Rates of Active Learning for Maximum Likelihood Estimation.
Non-convex Statistical Optimization for Sparse Tensor Graphical Model.
Human Memory Search as Initial-Visit Emitting Random Walk.
Fast Distributed k-Center Clustering with Outliers on Massive Data.
Fast Classification Rates for High-dimensional Gaussian Generative Models.
The Brain Uses Reliability of Stimulus Information when Making Perceptual Decisions.
On Top-k Selection in Multi-Armed Bandits and Hidden Bipartite Graphs.
SGD Algorithms based on Incomplete U-statistics: Large-Scale Minimization of Empirical Risk.
Weighted Theta Functions and Embeddings with Applications to Max-Cut, Clustering and Summarization.
Predtron: A Family of Online Algorithms for General Prediction Problems.
Differentially private subspace clustering.
Fast and Guaranteed Tensor Decomposition via Sketching.
Enforcing balance allows local supervised learning in spiking recurrent networks.
Unsupervised Learning by Program Synthesis.
Linear Multi-Resource Allocation with Semi-Bandit Feedback.
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.
Bounding the Cost of Search-Based Lifted Inference.
Convolutional Neural Networks with Intra-Layer Recurrent Connections for Scene Labeling.
Parallel Recursive Best-First AND/OR Search for Exact MAP Inference in Graphical Models.
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding.
Matrix Manifold Optimization for Gaussian Mixtures.
Shepard Convolutional Neural Networks.
Large-scale probabilistic predictors with and without guarantees of validity.
Hessian-free Optimization for Learning Deep Multidimensional Recurrent Neural Networks.
Maximum Likelihood Learning With Arbitrary Treewidth via Fast-Mixing Parameter Sets.
Quartz: Randomized Dual Coordinate Ascent with Arbitrary Sampling.
Bidirectional Recurrent Neural Networks as Generative Models.
A Generalization of Submodular Cover via the Diminishing Return Property on the Integer Lattice.
Precision-Recall-Gain Curves: PR Analysis Done Right.
Statistical Model Criticism using Kernel Two Sample Tests.
Empirical Localization of Homogeneous Divergences on Discrete Sample Spaces.
GAP Safe screening rules for sparse multi-task and multi-class models.
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting.
Convolutional spike-triggered covariance analysis for neural subunit models.
Online Learning for Adversaries with Memory: Price of Past Mistakes.
Fast Randomized Kernel Ridge Regression with Statistical Guarantees.
Sparse PCA via Bipartite Matchings.
Subspace Clustering with Irrelevant Features via Robust Dantzig Selector.
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure.
Solving Random Quadratic Systems of Equations Is Nearly as Easy as Solving Linear Systems.
Sparse Local Embeddings for Extreme Multi-label Classification.
Robust Regression via Hard Thresholding.
On the Optimality of Classifier Chain for Multi-label Classification.
Active Learning from Weak and Strong Labelers.
Monotone k-Submodular Function Maximization with Size Constraints.
Deep learning with Elastic Averaging SGD.
Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters.
Black-box optimization of noisy functions with unknown smoothness.
Robust Feature-Sample Linear Discriminant Analysis for Brain Disorders Diagnosis.
Character-level Convolutional Networks for Text Classification.
Training Restricted Boltzmann Machine via the Thouless-Anderson-Palmer free energy.
Learning Large-Scale Poisson DAG Models based on OverDispersion Scoring.
Tractable Bayesian Network Structure Learning with Bounded Vertex Cover Number.
M-Best-Diverse Labelings for Submodular Energies and Beyond.
Online Rank Elicitation for Plackett-Luce: A Dueling Bandits Approach.
Online F-Measure Optimization.
Closed-form Estimators for High-dimensional Generalized Linear Models.
Attention-Based Models for Speech Recognition.
Automatic Variational Inference in Stan.
A Nonconvex Optimization Framework for Low Rank Matrix Estimation.
Compressive spectral embedding: sidestepping the SVD.
Learning Theory and Algorithms for Forecasting Non-stationary Time Series.
Barrier Frank-Wolfe for Marginal Inference.
Efficient Compressive Phase Retrieval with Constrained Sensing Vectors.
Rethinking LDA: Moment Matching for Discrete ICA.
Deep Knowledge Tracing.
On the Global Linear Convergence of Frank-Wolfe Optimization Variants.
Estimating Mixture Models via Mixtures of Polynomials.
Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability.
Spectral Learning of Large Structured HMMs for Comparative Epigenomics.
A Reduced-Dimension fMRI Shared Response Model.
Parallelizing MCMC with Random Partition Trees.
Tensorizing Neural Networks.
Algorithms with Logarithmic or Sublinear Regret for Constrained Contextual Bandits.
3D Object Proposals for Accurate Object Class Detection.
HONOR: Hybrid Optimization for NOn-convex Regularized problems.
Column Selection via Adaptive Sampling.
Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations.
Approximating Sparse PCA from Incomplete Data.
Accelerated Proximal Gradient Methods for Nonconvex Programming.
Synaptic Sampling: A Bayesian Approach to Neural Network Plasticity and Rewiring.
Deeply Learning the Messages in Message Passing Inference.
Stochastic Online Greedy Learning with Semi-bandit Feedbacks.
Orthogonal NMF through Subspace Exploration.
Policy Evaluation Using the Ω-Return.
Top-k Multiclass SVM.
Optimal Ridge Detection using Coverage Risk.
Copeland Dueling Bandits.
Smooth and Strong: MAP Inference with Linear Convergence.
Learning visual biases from human imagination.
Streaming, Distributed Variational Inference for Bayesian Nonparametrics.
Extending Gossip Algorithms to Distributed Estimation of U-statistics.
Texture Synthesis Using Convolutional Neural Networks.
A fast, universal algorithm to learn parametric nonlinear embeddings.
Bounding errors of Expectation-Propagation.
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution.
Measuring Sample Quality with Stein's Method.
On the Limitation of Spectral Methods: From the Gaussian Hidden Clique Problem to Rank-One Perturbations of Gaussian Tensors.
The Pareto Regret Frontier for Bandits.
Where are they looking?
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets.
Probabilistic Line Searches for Stochastic Optimization.
Fast and Accurate Inference of Plackett-Luce Models.
Color Constancy by Learning to Predict Chromaticity from Luminance.
Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM).
Unlocking neural population non-stationarities using hierarchical dynamics models.
On the Pseudo-Dimension of Nearly Optimal Auctions.
Galileo: Perceiving Physical Object Properties by Integrating a Physics Engine with Deep Learning.
Smooth Interactive Submodular Set Cover.
A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements.
Space-Time Local Embeddings.
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.
Parallel Correlation Clustering on Big Graphs.
Expressing an Image Stream with a Sequence of Natural Sentences.
Planar Ultrametrics for Image Segmentation.
Logarithmic Time Online Multiclass prediction.
Robust Portfolio Optimization.
Covariance-Controlled Adaptive Langevin Thermostat for Large-Scale Bayesian Sampling.
Adaptive Low-Complexity Sequential Inference for Dirichlet Process Mixture Models.
Algorithmic Stability and Uniform Generalization.
Learning with Symmetric Label Noise: The Importance of Being Unhinged.
Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing.
