NeurIPS(NIPS) 2014 论文列表Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. |
Fast Prediction for Large-Scale Kernel Machines.
Variational Gaussian Process State-Space Models.
Learning a Concept Hierarchy from Multi-labeled Documents.
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls.
Unsupervised learning of an efficient short-term memory network.
Spectral k-Support Norm Regularization.
Learning on graphs using Orthonormal Representation is Statistically Consistent.
Fast Kernel Learning for Multidimensional Pattern Extrapolation.
Biclustering Usinig Message Passing.
A Wild Bootstrap for Degenerate Kernel Tests.
Stochastic variational inference for hidden Markov models.
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation.
Semi-supervised Learning with Deep Generative Models.
Pre-training of Recurrent Neural Networks via Linear Autoencoders.
Low-Rank Time-Frequency Synthesis.
Parallel Feature Selection Inspired by Group Testing.
Deep Networks with Internal Selective Attention through Feedback Connections.
LSDA: Large Scale Detection through Adaptation.
Judy Hoffman Sergio Guadarrama Eric Tzeng Ronghang Hu Jeff Donahue Ross B. Girshick Trevor Darrell Kate Saenko
Partition-wise Linear Models.
Factoring Variations in Natural Images with Deep Gaussian Mixture Models.
Algorithms for CVaR Optimization in MDPs.
Clustered factor analysis of multineuronal spike data.
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning.
Online Decision-Making in General Combinatorial Spaces.
Concavity of reweighted Kikuchi approximation.
Zero-shot recognition with unreliable attributes.
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling.
Consistency of weighted majority votes.
Rates of Convergence for Nearest Neighbor Classification.
Feedback Detection for Live Predictors.
Discrete Graph Hashing.
Asynchronous Anytime Sequential Monte Carlo.
Finding a sparse vector in a subspace: Linear sparsity using alternating directions.
Discriminative Metric Learning by Neighborhood Gerrymandering.
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time.
Learning Time-Varying Coverage Functions.
Learning with Pseudo-Ensembles.
Distributed Bayesian Posterior Sampling via Moment Sharing.
A Latent Source Model for Online Collaborative Filtering.
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning.
Accelerated Mini-batch Randomized Block Coordinate Descent Method.
How transferable are features in deep neural networks?
Conditional Random Field Autoencoders for Unsupervised Structured Prediction.
An Integer Polynomial Programming Based Framework for Lifted MAP Inference.
Learning to Search in Branch and Bound Algorithms.
Tight convex relaxations for sparse matrix factorization.
Convex Deep Learning via Normalized Kernels.
Learning Distributed Representations for Structured Output Prediction.
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models.
Large-Margin Convex Polytope Machine.
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs.
Efficient Minimax Strategies for Square Loss Games.
Orbit Regularization.
On Sparse Gaussian Chain Graph Models.
Bayesian Sampling Using Stochastic Gradient Thermostats.
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices.
A Statistical Decision-Theoretic Framework for Social Choice.
Content-based recommendations with Poisson factorization.
Streaming, Memory Limited Algorithms for Community Detection.
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs.
Expectation-Maximization for Learning Determinantal Point Processes.
Mondrian Forests: Efficient Online Random Forests.
Tight Continuous Relaxation of the Balanced k-Cut Problem.
Sparse Polynomial Learning and Graph Sketching.
Improved Distributed Principal Component Analysis.
Sequence to Sequence Learning with Neural Networks.
A Bayesian model for identifying hierarchically organised states in neural population activity.
A* Sampling.
Simple MAP Inference via Low-Rank Relaxations.
Communication-Efficient Distributed Dual Coordinate Ascent.
Martin Jaggi Virginia Smith Martin Takác Jonathan Terhorst Sanjay Krishnan Thomas Hofmann Michael I. Jordan
An Accelerated Proximal Coordinate Gradient Method.
Fast Training of Pose Detectors in the Fourier Domain.
Scalable Kernel Methods via Doubly Stochastic Gradients.
Exponential Concentration of a Density Functional Estimator.
Information-based learning by agents in unbounded state spaces.
Weighted importance sampling for off-policy learning with linear function approximation.
Scale Adaptive Blind Deblurring.
Graph Clustering With Missing Data: Convex Algorithms and Analysis.
Optimal Neural Codes for Control and Estimation.
Scaling-up Importance Sampling for Markov Logic Networks.
Optimizing Energy Production Using Policy Search and Predictive State Representations.
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models.
Learning with Fredholm Kernels.
Extracting Latent Structure From Multiple Interacting Neural Populations.
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization.
On the Number of Linear Regions of Deep Neural Networks.
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning.
Real-Time Decoding of an Integrate and Fire Encoder.
A statistical model for tensor PCA.
Global Belief Recursive Neural Networks.
Extremal Mechanisms for Local Differential Privacy.
Algorithm selection by rational metareasoning as a model of human strategy selection.
The Noisy Power Method: A Meta Algorithm with Applications.
Structure learning of antiferromagnetic Ising models.
Scalable Inference for Neuronal Connectivity from Calcium Imaging.
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning.
Multitask learning meets tensor factorization: task imputation via convex optimization.
Bregman Alternating Direction Method of Multipliers.
The limits of squared Euclidean distance regularization.
Multi-Scale Spectral Decomposition of Massive Graphs.
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature.
Online combinatorial optimization with stochastic decision sets and adversarial losses.
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition.
Deterministic Symmetric Positive Semidefinite Matrix Completion.
Greedy Subspace Clustering.
Consistent Binary Classification with Generalized Performance Metrics.
Computing Nash Equilibria in Generalized Interdependent Security Games.
On Communication Cost of Distributed Statistical Estimation and Dimensionality.
Cone-Constrained Principal Component Analysis.
Efficient Minimax Signal Detection on Graphs.
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain.
Global Sensitivity Analysis for MAP Inference in Graphical Models.
Testing Unfaithful Gaussian Graphical Models.
Generative Adversarial Nets.
Ian J. Goodfellow Jean Pouget-Abadie Mehdi Mirza Bing Xu David Warde-Farley Sherjil Ozair Aaron C. Courville Yoshua Bengio
Dynamic Rank Factor Model for Text Streams.
Do Deep Nets Really Need to be Deep?
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets.
Fairness in Multi-Agent Sequential Decision-Making.
Convolutional Kernel Networks.
Controlling privacy in recommender systems.
Learning Mixtures of Ranking Models.
Augur: Data-Parallel Probabilistic Modeling.
Jean-Baptiste Tristan Daniel Huang Joseph Tassarotti Adam Craig Pocock Stephen J. Green Guy L. Steele Jr.
Distributed Balanced Clustering via Mapping Coresets.
Ranking via Robust Binary Classification.
Diverse Randomized Agents Vote to Win.
Albert Xin Jiang Leandro Soriano Marcolino Ariel D. Procaccia Tuomas Sandholm Nisarg Shah Milind Tambe
Feedforward Learning of Mixture Models.
Optimal rates for k-NN density and mode estimation.
Nonparametric Bayesian inference on multivariate exponential families.
Deep Symmetry Networks.
Design Principles of the Hippocampal Cognitive Map.
Difference of Convex Functions Programming for Reinforcement Learning.
A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights.
Multi-Class Deep Boosting.
Reputation-based Worker Filtering in Crowdsourcing.
Analysis of Brain States from Multi-Region LFP Time-Series.
Shaping Social Activity by Incentivizing Users.
Optimal Teaching for Limited-Capacity Human Learners.
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space.
Recursive Context Propagation Network for Semantic Scene Labeling.
Smoothed Gradients for Stochastic Variational Inference.
Generalized Unsupervised Manifold Alignment.
Multivariate f-divergence Estimation With Confidence.
On Multiplicative Multitask Feature Learning.
Structure Regularization for Structured Prediction.
Searching for Higgs Boson Decay Modes with Deep Learning.
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process.
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators.
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network.
Learning Chordal Markov Networks by Dynamic Programming.
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations.
Randomized Experimental Design for Causal Graph Discovery.
A framework for studying synaptic plasticity with neural spike train data.
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS).
Efficient Optimization for Average Precision SVM.
On the Information Theoretic Limits of Learning Ising Models.
Learning the Learning Rate for Prediction with Expert Advice.
Advances in Learning Bayesian Networks of Bounded Treewidth.
A Dual Algorithm for Olfactory Computation in the Locust Brain.
A Boosting Framework on Grounds of Online Learning.
Subspace Embeddings for the Polynomial Kernel.
Spectral Learning of Mixture of Hidden Markov Models.
Fast Sampling-Based Inference in Balanced Neuronal Networks.
Analog Memories in a Balanced Rate-Based Network of E-I Neurons.
Active Learning and Best-Response Dynamics.
Tree-structured Gaussian Process Approximations.
Recurrent Models of Visual Attention.
Median Selection Subset Aggregation for Parallel Inference.
Multi-Resolution Cascades for Multiclass Object Detection.
Neural Word Embedding as Implicit Matrix Factorization.
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems.
Elementary Estimators for Graphical Models.
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising.
Improved Multimodal Deep Learning with Variation of Information.
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets.
Optimizing F-Measures by Cost-Sensitive Classification.
A Filtering Approach to Stochastic Variational Inference.
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers.
Deep Recursive Neural Networks for Compositionality in Language.
Feature Cross-Substitution in Adversarial Classification.
Self-Paced Learning with Diversity.
Diverse Sequential Subset Selection for Supervised Video Summarization.
On the relations of LFPs & Neural Spike Trains.
Scalable Non-linear Learning with Adaptive Polynomial Expansions.
Convolutional Neural Network Architectures for Matching Natural Language Sentences.
Attentional Neural Network: Feature Selection Using Cognitive Feedback.
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks.
General Stochastic Networks for Classification.
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models.
A provable SVD-based algorithm for learning topics in dominant admixture corpus.
Deep Learning Face Representation by Joint Identification-Verification.
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning.
Local Linear Convergence of Forward-Backward under Partial Smoothness.
Latent Support Measure Machines for Bag-of-Words Data Classification.
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification.
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks.
Generalized Dantzig Selector: Application to the k-support norm.
Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells".
Predicting Useful Neighborhoods for Lazy Local Learning.
Probabilistic Differential Dynamic Programming.
Flexible Transfer Learning under Support and Model Shift.
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping.
Optimal prior-dependent neural population codes under shared input noise.
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers.
Sequential Monte Carlo for Graphical Models.
An Autoencoder Approach to Learning Bilingual Word Representations.
A. P. Sarath Chandar Stanislas Lauly Hugo Larochelle Mitesh M. Khapra Balaraman Ravindran Vikas C. Raykar Amrita Saha
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms.
How hard is my MDP?" The distribution-norm to the rescue".
Learning Optimal Commitment to Overcome Insecurity.
Metric Learning for Temporal Sequence Alignment.
Learning Generative Models with Visual Attention.
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation.
Deep Convolutional Neural Network for Image Deconvolution.
Low Rank Approximation Lower Bounds in Row-Update Streams.
Making Pairwise Binary Graphical Models Attractive.
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion.
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling.
Bayesian Inference for Structured Spike and Slab Priors.
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations.
Probabilistic low-rank matrix completion on finite alphabets.
Online Optimization for Max-Norm Regularization.
Unsupervised Deep Haar Scattering on Graphs.
Distributed Parameter Estimation in Probabilistic Graphical Models.
Efficient Partial Monitoring with Prior Information.
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input.
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis.
Time-Data Tradeoffs by Aggressive Smoothing.
Exclusive Feature Learning on Arbitrary Structures via \ell_{1, 2}-norm.
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives.
Weakly-supervised Discovery of Visual Pattern Configurations.
Sparse Bayesian structure learning with dependent relevance determination priors.
Convex Optimization Procedure for Clustering: Theoretical Revisit.
The Blinded Bandit: Learning with Adaptive Feedback.
Do Convnets Learn Correspondence?
Covariance shrinkage for autocorrelated data.
Learning to Optimize via Information-Directed Sampling.
Stochastic Proximal Gradient Descent with Acceleration Techniques.
Decomposing Parameter Estimation Problems.
Estimation with Norm Regularization.
Decoupled Variational Gaussian Inference.
Unsupervised Transcription of Piano Music.
Sparse PCA with Oracle Property.
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data.
Spectral Methods for Supervised Topic Models.
Top Rank Optimization in Linear Time.
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures.
Spectral Methods for Indian Buffet Process Inference.
Minimax-optimal Inference from Partial Rankings.
Model-based Reinforcement Learning and the Eluder Dimension.
Blossom Tree Graphical Models.
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings.
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization.
Provable Tensor Factorization with Missing Data.
Robust Tensor Decomposition with Gross Corruption.
Learning Mixtures of Submodular Functions for Image Collection Summarization.
Automated Variational Inference for Gaussian Process Models.
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures.
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning.
Projecting Markov Random Field Parameters for Fast Mixing.
A Drifting-Games Analysis for Online Learning and Applications to Boosting.
On Integrated Clustering and Outlier Detection.
Efficient Structured Matrix Rank Minimization.
Submodular Attribute Selection for Action Recognition in Video.
Large-scale L-BFGS using MapReduce.
Mode Estimation for High Dimensional Discrete Tree Graphical Models.
Conditional Swap Regret and Conditional Correlated Equilibrium.
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology.
DFacTo: Distributed Factorization of Tensors.
The Large Margin Mechanism for Differentially Private Maximization.
Learning to Discover Efficient Mathematical Identities.
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation.
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing.
Extended and Unscented Gaussian Processes.
Divide-and-Conquer Learning by Anchoring a Conical Hull.
Discovering, Learning and Exploiting Relevance.
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP.
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit.
Recovery of Coherent Data via Low-Rank Dictionary Pursuit.
From Stochastic Mixability to Fast Rates.
Clustering from Labels and Time-Varying Graphs.
Reducing the Rank in Relational Factorization Models by Including Observable Patterns.
Learning Multiple Tasks in Parallel with a Shared Annotator.
Causal Strategic Inference in Networked Microfinance Economies.
Dependent nonparametric trees for dynamic hierarchical clustering.
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors.
Message Passing Inference for Large Scale Graphical Models with High Order Potentials.
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm.
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning.
Non-convex Robust PCA.
Distributed Estimation, Information Loss and Exponential Families.
Extreme bandits.
Magnitude-sensitive preference formation.
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics.
Hardness of parameter estimation in graphical models.
A Safe Screening Rule for Sparse Logistic Regression.
Gaussian Process Volatility Model.
Optimistic Planning in Markov Decision Processes Using a Generative Model.
A Framework for Testing Identifiability of Bayesian Models of Perception.
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm.
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings.
Approximating Hierarchical MV-sets for Hierarchical Clustering.
Universal Option Models.
General Table Completion using a Bayesian Nonparametric Model.
Incremental Local Gaussian Regression.
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights.
Inferring synaptic conductances from spike trains with a biophysically inspired point process model.
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices.
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials.
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation.
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions.
Clamping Variables and Approximate Inference.
SerialRank: Spectral Ranking using Seriation.
Learning convolution filters for inverse covariance estimation of neural network connectivity.
Stochastic Network Design in Bidirected Trees.
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks.
Self-Adaptable Templates for Feature Coding.
On the Computational Efficiency of Training Neural Networks.
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology.
Mind the Nuisance: Gaussian Process Classification using Privileged Noise.
Best-Arm Identification in Linear Bandits.
Sparse Multi-Task Reinforcement Learning.
Exploiting easy data in online optimization.
Provable Submodular Minimization using Wolfe's Algorithm.
Projective dictionary pair learning for pattern classification.
Bandit Convex Optimization: Towards Tight Bounds.
Distance-Based Network Recovery under Feature Correlation.
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks.
Learning Shuffle Ideals Under Restricted Distributions.
Optimal decision-making with time-varying evidence reliability.
Probabilistic ODE Solvers with Runge-Kutta Means.
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data.
Constrained convex minimization via model-based excessive gap.
Dimensionality Reduction with Subspace Structure Preservation.
Analysis of Learning from Positive and Unlabeled Data.
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions.
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation.
On Prior Distributions and Approximate Inference for Structured Variables.
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection.
PAC-Bayesian AUC classification and scoring.
New Rules for Domain Independent Lifted MAP Inference.
On the Convergence Rate of Decomposable Submodular Function Minimization.
Recursive Inversion Models for Permutations.
Repeated Contextual Auctions with Strategic Buyers.
Efficient learning by implicit exploration in bandit problems with side observations.
Near-optimal Reinforcement Learning in Factored MDPs.
Learning Mixed Multinomial Logit Model from Ordinal Data.
Positive Curvature and Hamiltonian Monte Carlo.
Discovering Structure in High-Dimensional Data Through Correlation Explanation.
Two-Stream Convolutional Networks for Action Recognition in Videos.
Coresets for k-Segmentation of Streaming Data.
Bounded Regret for Finite-Armed Structured Bandits.
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision.
Robust Bayesian Max-Margin Clustering.
Deep Joint Task Learning for Generic Object Extraction.
Efficient Sampling for Learning Sparse Additive Models in High Dimensions.
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks.
A Complete Variational Tracker.
Learning Deep Features for Scene Recognition using Places Database.
Sensory Integration and Density Estimation.
Active Regression by Stratification.
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment.
Bayes-Adaptive Simulation-based Search with Value Function Approximation.
Beyond Disagreement-Based Agnostic Active Learning.
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space.
Local Decorrelation For Improved Pedestrian Detection.
Fast and Robust Least Squares Estimation in Corrupted Linear Models.
Spectral Clustering of graphs with the Bethe Hessian.
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model.
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces.
Combinatorial Pure Exploration of Multi-Armed Bandits.
Near-optimal sample compression for nearest neighbors.
A Representation Theory for Ranking Functions.
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System.
Low-dimensional models of neural population activity in sensory cortical circuits.
Sparse PCA via Covariance Thresholding.
Iterative Neural Autoregressive Distribution Estimator NADE-k.
Multi-scale Graphical Models for Spatio-Temporal Processes.
Incremental Clustering: The Case for Extra Clusters.
Causal Inference through a Witness Protection Program.
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning.
Transportability from Multiple Environments with Limited Experiments: Completeness Results.
A Unified Semantic Embedding: Relating Taxonomies and Attributes.
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities.
Robust Logistic Regression and Classification.
From MAP to Marginals: Variational Inference in Bayesian Submodular Models.
Parallel Sampling of HDPs using Sub-Cluster Splits.
Shape and Illumination from Shading using the Generic Viewpoint Assumption.
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations.
Object Localization based on Structural SVM using Privileged Information.
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards.
(Almost) No Label No Cry.
Parallel Direction Method of Multipliers.
Quantized Kernel Learning for Feature Matching.
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation.
Just-In-Time Learning for Fast and Flexible Inference.
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification.
Exact Post Model Selection Inference for Marginal Screening.
Multivariate Regression with Calibration.
Parallel Double Greedy Submodular Maximization.
Rounding-based Moves for Metric Labeling.
Altitude Training: Strong Bounds for Single-Layer Dropout.
large scale canonical correlation analysis with iterative least squares.
Multiscale Fields of Patterns.
Restricted Boltzmann machines modeling human choice.
Sparse Space-Time Deconvolution for Calcium Image Analysis.
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction.
Zeta Hull Pursuits: Learning Nonconvex Data Hulls.
Robust Classification Under Sample Selection Bias.
The Infinite Mixture of Infinite Gaussian Mixtures.
Communication Efficient Distributed Machine Learning with the Parameter Server.
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models.
Kernel Mean Estimation via Spectral Filtering.