Downloads 2014

Format:
Posters:
Tutorials:
Invited talks:
Workshops:
Demonstrations:

Number of events: 473

3rd NIPS Workshop on Probabilistic Programming
4th Workshop on Automated Knowledge Base Construction (AKBC)
A 3D Simulator for Evaluating Reinforcement and Imitation Learning Algorithms on Complex Tasks
A Bayesian model for identifying hierarchically organised states in neural population activity
ABC in Montreal
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation
A Boosting Framework on Grounds of Online Learning
Accelerated Mini-batch Randomized Block Coordinate Descent Method
A Complete Variational Tracker
Active Learning and Best-Response Dynamics
Active Regression by Stratification
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights
A Drifting-Games Analysis for Online Learning and Applications to Boosting
A Dual Algorithm for Olfactory Computation in the Locust Brain
Advances in Learning Bayesian Networks of Bounded Treewidth
Advances in Variational Inference
A Filtering Approach to Stochastic Variational Inference
A framework for studying synaptic plasticity with neural spike train data
A Framework for Testing Identifiability of Bayesian Models of Perception
A Latent Source Model for Online Collaborative Filtering
Algorithm selection by rational metareasoning as a model of human strategy selection
Algorithms for CVaR Optimization in MDPs
(Almost) No Label No Cry
Altitude Training: Strong Bounds for Single-Layer Dropout
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
An Accelerated Proximal Coordinate Gradient Method
Analog Memories in a Balanced Rate-Based Network of E-I Neurons
Analysis of Brain States from Multi-Region LFP Time-Series
Analysis of Learning from Positive and Unlabeled Data
Analysis of Rank Data: Confluence of Social Choice, Operations Research, and Machine Learning
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP
Analyzing the omics of the brain
An Autoencoder Approach to Learning Bilingual Word Representations
An Integer Polynomial Programming Based Framework for Lifted MAP Inference
Approximating Hierarchical MV-sets for Hierarchical Clustering
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process
A provable SVD-based algorithm for learning topics in dominant admixture corpus
A Representation Theory for Ranking Functions
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
A Safe Screening Rule for Sparse Logistic Regression
A* Sampling
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment
A Statistical Decision-Theoretic Framework for Social Choice
A statistical model for tensor PCA
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System
Asynchronous Anytime Sequential Monte Carlo
Attentional Neural Network: Feature Selection Using Cognitive Feedback
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning
Augur: Data-Parallel Probabilistic Modeling
A Unified Semantic Embedding: Relating Taxonomies and Attributes
Automated Variational Inference for Gaussian Process Models
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning
Autonomously Learning Robots
A Visual and Interactive IDE for Probabilistic Programming
A Wild Bootstrap for Degenerate Kernel Tests
Bandit Convex Optimization: Towards Tight Bounds
Bayes-Adaptive Simulation-based Search with Value Function Approximation
Bayesian Inference for Structured Spike and Slab Priors
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
Bayesian Optimization in Academia and Industry
Bayesian Sampling Using Stochastic Gradient Thermostats
Best-Arm Identification in Linear Bandits
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling
Beyond Disagreement-Based Agnostic Active Learning
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems
Biclustering Using Message Passing
BIDMach: High Performance Machine Learning through Codesign and Rooflining
Blossom Tree Graphical Models
Bounded Regret for Finite-Armed Structured Bandits
Bregman Alternating Direction Method of Multipliers
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs
Causal Inference through a Witness Protection Program
Causal Strategic Inference in Networked Microfinance Economies
Challenges in Machine Learning workshop (CiML 2014)
Clamping Variables and Approximate Inference
Climate Change: Challenges for Machine Learning
Clustered factor analysis of multineuronal spike data
Clustering from Labels and Time-Varying Graphs
Combinatorial Pure Exploration of Multi-Armed Bandits
Communication-Efficient Distributed Dual Coordinate Ascent
Communication Efficient Distributed Machine Learning with the Parameter Server
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
Computing Game-Theoretic Solutions
Computing Nash Equilibria in Generalized Interdependent Security Games
Concavity of reweighted Kikuchi approximation
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
Conditional Swap Regret and Conditional Correlated Equilibrium
Cone-Constrained Principal Component Analysis
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
Consistency of weighted majority votes
Consistent Binary Classification with Generalized Performance Metrics
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
Constrained convex minimization via model-based excessive gap
Content-based recommendations with Poisson factorization
Controlling privacy in recommender systems
Convex Deep Learning via Normalized Kernels
Convex Optimization Procedure for Clustering: Theoretical Revisit
Convolutional Kernel Networks
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Coresets for k-Segmentation of Streaming Data
Covariance shrinkage for autocorrelated data
Decomposing Parameter Estimation Problems
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain
Decoupled Variational Gaussian Inference
Deep Convolutional Neural Network for Image Deconvolution
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
Deep Joint Task Learning for Generic Object Extraction
Deep Learning and Representation Learning
Deep Learning Face Representation by Joint Identification-Verification
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
Deep Networks with Internal Selective Attention through Feedback Connections
Deep Recursive Neural Networks for Compositionality in Language
Deep Symmetry Networks
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
Dependent nonparametric trees for dynamic hierarchical clustering
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Design Principles of the Hippocampal Cognitive Map
Deterministic Symmetric Positive Semidefinite Matrix Completion
DFacTo: Distributed Factorization of Tensors
Difference of Convex Functions Programming for Reinforcement Learning
Differential Privacy and Learning: The Tools, The Results, and The Frontier
Dimensionality Reduction with Subspace Structure Preservation
Discovering, Learning and Exploiting Relevance
Discovering Structure in High-Dimensional Data Through Correlation Explanation
Discrete Graph Hashing
Discrete Optimization in Machine Learning
Discriminative Metric Learning by Neighborhood Gerrymandering
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Distance-Based Network Recovery under Feature Correlation
Distributed Balanced Clustering via Mapping Coresets
Distributed Bayesian Posterior Sampling via Moment Sharing
Distributed Estimation, Information Loss and Exponential Families
Distributed Machine Learning and Matrix Computations
Distributed Parameter Estimation in Probabilistic Graphical Models
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Diverse Randomized Agents Vote to Win
Diverse Sequential Subset Selection for Supervised Video Summarization
Divide-and-Conquer Learning by Anchoring a Conical Hull
Do Convnets Learn Correspondence?
Do Deep Nets Really Need to be Deep?
Dynamic Rank Factor Model for Text Streams
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
Efficient learning by implicit exploration in bandit problems with side observations
Efficient Minimax Signal Detection on Graphs
Efficient Minimax Strategies for Square Loss Games
Efficient Optimization for Average Precision SVM
Efficient Partial Monitoring with Prior Information
Efficient Sampling for Learning Sparse Additive Models in High Dimensions
Efficient Structured Matrix Rank Minimization
Elementary Estimators for Graphical Models
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
Estimation with Norm Regularization
Exact Post Model Selection Inference for Marginal Screening
Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights
Expectation-Maximization for Learning Determinantal Point Processes
Exploiting easy data in online optimization
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Exponential Concentration of a Density Functional Estimator
Extended and Unscented Gaussian Processes
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities
Extracting Latent Structure From Multiple Interacting Neural Populations
Extremal Mechanisms for Local Differential Privacy
Extreme bandits
Factoring Variations in Natural Images with Deep Gaussian Mixture Models
Fairness, Accountability, and Transparency in Machine Learning
Fairness in Multi-Agent Sequential Decision-Making
Fast and Robust Least Squares Estimation in Corrupted Linear Models
Fast Kernel Learning for Multidimensional Pattern Extrapolation
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Fast Prediction for Large-Scale Kernel Machines
Fast Sampling-Based Inference in Balanced Neuronal Networks
Fast Training of Pose Detectors in the Fourier Domain
Feature Cross-Substitution in Adversarial Classification
Feedback Detection for Live Predictors
Feedforward Learning of Mixture Models
Finding a sparse vector in a subspace: Linear sparsity using alternating directions
Flexible Transfer Learning under Support and Model Shift
From Bad Models to Good Policies (Sequential Decision Making under Uncertainty)
From MAP to Marginals: Variational Inference in Bayesian Submodular Models
From Stochastic Mixability to Fast Rates
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
Games, Networks, and People
Gaussian Process Volatility Model
Generalized Dantzig Selector: Application to the k-support norm
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion
Generalized Unsupervised Manifold Alignment
General Stochastic Networks for Classification
General Table Completion using a Bayesian Nonparametric Model
Generative Adversarial Nets
Gibbs-type Indian Buffet Processes
Global Belief Recursive Neural Networks
Global Sensitivity Analysis for MAP Inference in Graphical Models
Graph Clustering With Missing Data: Convex Algorithms and Analysis
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data
Greedy Subspace Clustering
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models
Hardness of parameter estimation in graphical models
High-energy particle physics, machine learning, and the HiggsML data challenge (HEPML)
"How hard is my MDP?" The distribution-norm to the rescue
How transferable are features in deep neural networks?
Human Propelled Machine Learning
ICE: Interactive Classification and Entity Extraction
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Improved Distributed Principal Component Analysis
Improved Multimodal Deep Learning with Variation of Information
Incremental Clustering: The Case for Extra Clusters
Incremental Local Gaussian Regression
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit
Inferring synaptic conductances from spike trains with a biophysically inspired point process model
Information-based learning by agents in unbounded state spaces
Integrated Information Theory of Consciousness - Conceptual Formulation & Adventures in Simulated Evolution
Iterative Neural Autoregressive Distribution Estimator NADE-k
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Just-In-Time Learning for Fast and Flexible Inference
Kernel Mean Estimation via Spectral Filtering
Large-Margin Convex Polytope Machine
large scale canonical correlation analysis with iterative least squares
Large-scale L-BFGS using MapReduce
Large scale optical physiology: From data-acquisition to models of neural coding
Large-scale reinforcement learning and Markov decision problems
Latent Support Measure Machines for Bag-of-Words Data Classification
Learning a Concept Hierarchy from Multi-labeled Documents
Learning Chordal Markov Networks by Dynamic Programming
Learning convolution filters for inverse covariance estimation of neural network connectivity
Learning Deep Features for Scene Recognition using Places Database
Learning Distributed Representations for Structured Output Prediction
Learning for Tactile Manipulation
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm
Learning Generative Models with Visual Attention
Learning Mixed Multinomial Logit Model from Ordinal Data
Learning Mixtures of Ranking Models
Learning Mixtures of Submodular Functions for Image Collection Summarization
Learning Multiple Tasks in Parallel with a Shared Annotator
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics
Learning on graphs using Orthonormal Representation is Statistically Consistent
Learning Optimal Commitment to Overcome Insecurity
Learning Semantics
Learning Shuffle Ideals Under Restricted Distributions
Learning the Learning Rate for Prediction with Expert Advice
Learning Time-Varying Coverage Functions
Learning to Discover Efficient Mathematical Identities
Learning to Optimize via Information-Directed Sampling
Learning to Search in Branch and Bound Algorithms
Learning with Fredholm Kernels
Learning with Pseudo-Ensembles
Local Decorrelation For Improved Pedestrian Detection
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
Local Linear Convergence of Forward--Backward under Partial Smoothness
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
Low-dimensional models of neural population activity in sensory cortical circuits
Low Rank Approximation Lower Bounds in Row-Update Streams
Low-Rank Time-Frequency Synthesis
LSDA: Large Scale Detection through Adaptation
Machine Learning for Clinical Data Analysis, Healthcare and Genomics
Machine Learning in Computational Biology
Machine Learning in the Browser
Magnitude-sensitive preference formation`
Making Pairwise Binary Graphical Models Attractive
Median Selection Subset Aggregation for Parallel Inference
Message Passing Inference for Large Scale Graphical Models with High Order Potentials
Metric Learning for Temporal Sequence Alignment
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Minimax-optimal Inference from Partial Rankings
MLINI 2014 - 4th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner
Mode Estimation for High Dimensional Discrete Tree Graphical Models
Model-based Reinforcement Learning and the Eluder Dimension
Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells"
Modern Machine Learning and Natural Language Processing
Modern Nonparametrics 3: Automating the Learning Pipeline
Mondrian Forests: Efficient Online Random Forests
Multi-Class Deep Boosting
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
Multi-Resolution Cascades for Multiclass Object Detection
Multiscale Fields of Patterns
Multi-scale Graphical Models for Spatio-Temporal Processes
Multi-Scale Spectral Decomposition of Massive Graphs
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition
Multitask learning meets tensor factorization: task imputation via convex optimization
Multivariate f-divergence Estimation With Confidence
Multivariate Regression with Calibration
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms
Near-optimal Reinforcement Learning in Factored MDPs
Near-optimal sample compression for nearest neighbors
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
Networks: From Graphs to Rich Data
Networks in Climate Science
Neural Machine Translation
Neural Word Embedding as Implicit Matrix Factorization
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks
New Rules for Domain Independent Lifted MAP Inference
NIPS’14 Workshop on Crowdsourcing and Machine Learning
NIPS Workshop on Transactional Machine Learning and E-Commerce
Non-convex Robust PCA
Nonparametric Bayesian inference on multivariate exponential families
Non-Parametric Causal Models
Novel Trends and Applications in Reinforcement Learning
Object Localization based on Structural SVM using Privileged Information
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification
On Communication Cost of Distributed Statistical Estimation and Dimensionality
On Integrated Clustering and Outlier Detection
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
Online combinatorial optimization with stochastic decision sets and adversarial losses
Online Decision-Making in General Combinatorial Spaces
Online Optimization for Max-Norm Regularization
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
On Multiplicative Multitask Feature Learning
On Prior Distributions and Approximate Inference for Structured Variables
On Sparse Gaussian Chain Graph Models
On the Computational Efficiency of Training Neural Networks
On the Convergence Rate of Decomposable Submodular Function Minimization
On the Information Theoretic Limits of Learning Ising Models
On the Number of Linear Regions of Deep Neural Networks
On the relations of LFPs & Neural Spike Trains
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures
OPT2014: Optimization for Machine Learning
Optimal decision-making with time-varying evidence reliability
Optimal Neural Codes for Control and Estimation
Optimal prior-dependent neural population codes under shared input noise
Optimal rates for k-NN density and mode estimation
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers
Optimal Teaching for Limited-Capacity Human Learners
Optimal Transport and Machine Learning
Optimistic Planning in Markov Decision Processes Using a Generative Model
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
Optimizing Energy Production Using Policy Search and Predictive State Representations
Optimizing F-Measures by Cost-Sensitive Classification
Orbit Regularization
Out of the Box: Robustness in High Dimension
PAC-Bayesian AUC classification and scoring
Parallel Direction Method of Multipliers
Parallel Double Greedy Submodular Maximization
Parallel Feature Selection Inspired by Group Testing
Parallel Sampling of HDPs using Sub-Cluster Splits
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization
Partition-wise Linear Models
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision
Personalization: Methods and Applications
Perturbations, Optimization, and Statistics
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising
Playing with Convnets
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Positive Curvature and Hamiltonian Monte Carlo
Predicting Useful Neighborhoods for Lazy Local Learning
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
Pre-training of Recurrent Neural Networks via Linear Autoencoders
Privacy in the Land of Plenty
Probabilistic Differential Dynamic Programming
Probabilistic low-rank matrix completion on finite alphabets
Probabilistic ODE Solvers with Runge-Kutta Means
Projecting Markov Random Field Parameters for Fast Mixing
Projective dictionary pair learning for pattern classification
Provable Submodular Minimization using Wolfe's Algorithm
Provable Tensor Factorization with Missing Data
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
Quantized Kernel Learning for Feature Matching
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
Randomized Experimental Design for Causal Graph Discovery
Ranking via Robust Binary Classification
Rates of Convergence for Nearest Neighbor Classification
Real-Time Decoding of an Integrate and Fire Encoder
Real-time machine-vision applications on a million-spiking-neuron chip
Recent Progress in the Structure of Large-Treewidth Graphs and Some Applications
Recovery of Coherent Data via Low-Rank Dictionary Pursuit
Recurrent Models of Visual Attention
Recursive Context Propagation Network for Semantic Scene Labeling
Recursive Inversion Models for Permutations
Reducing the Rank in Relational Factorization Models by Including Observable Patterns
Repeated Contextual Auctions with Strategic Buyers
Representation and Learning Methods for Complex Outputs
Reputation-based Worker Filtering in Crowdsourcing
Restricted Boltzmann machines modeling human choice
Riemannian geometry in machine learning, statistics and computer vision
Robust Bayesian Max-Margin Clustering
Robust Classification Under Sample Selection Bias
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
Robust Logistic Regression and Classification
Robust Tensor Decomposition with Gross Corruption
Role of Coupled Networks in 21st Century Energy Infrastructure
Rounding-based Moves for Metric Labeling
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Scalable Inference for Neuronal Connectivity from Calcium Imaging
Scalable Kernel Methods via Doubly Stochastic Gradients
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Scale Adaptive Blind Deblurring
Scaling-up Importance Sampling for Markov Logic Networks
Searching for Higgs Boson Decay Modes with Deep Learning
Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice
Self-Adaptable Templates for Feature Coding
Self-Paced Learning with Diversity
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Semi-supervised Learning with Deep Generative Models
Sensory Integration and Density Estimation
Sequence to Sequence Learning with Neural Networks
Sequential Monte Carlo for Graphical Models
SerialRank: Spectral Ranking using Seriation
Shape and Illumination from Shading using the Generic Viewpoint Assumption
Shaping Social Activity by Incentivizing Users
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Simple MAP Inference via Low-Rank Relaxations
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
SmartWheeler – A smart robotic wheelchair platform
Smoothed Gradients for Stochastic Variational Inference
Software Engineering for Machine Learning
Sparse Bayesian structure learning with dependent relevance determination prior
Sparse Multi-Task Reinforcement Learning
Sparse PCA via Covariance Thresholding
Sparse PCA with Oracle Property
Sparse Polynomial Learning and Graph Sketching
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space
Sparse Space-Time Deconvolution for Calcium Image Analysis
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
Spectral Clustering of graphs with the Bethe Hessian
Spectral k-Support Norm Regularization
Spectral Learning of Mixture of Hidden Markov Models
Spectral Methods for Indian Buffet Process Inference
Spectral Methods for Supervised Topic Models
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards
Stochastic Network Design in Bidirected Trees
Stochastic Proximal Gradient Descent with Acceleration Techniques
Stochastic variational inference for hidden Markov models
Streaming, Memory Limited Algorithms for Community Detection
Structure learning of antiferromagnetic Ising models
Structure Regularization for Structured Prediction
Subgradient Methods for Huge-Scale Optimization Problems
Submodular Attribute Selection for Action Recognition in Video
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
Subspace Embeddings for the Polynomial Kernel
Testing Unfaithful Gaussian Graphical Models
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
The Blinded Bandit: Learning with Adaptive Feedback
The Infinite Mixture of Infinite Gaussian Mixtures
The Large Margin Mechanism for Differentially Private Maximization
The limits of squared Euclidean distance regularization
The Noisy Power Method: A Meta Algorithm with Applications
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
Tight Continuous Relaxation of the Balanced k-Cut Problem
Tight convex relaxations for sparse matrix factorization
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
Time--Data Tradeoffs by Aggressive Smoothing
Top Rank Optimization in Linear Time
Toronto Deep Learning
Transportability from Multiple Environments with Limited Experiments: Completeness Results
Tree-structured Gaussian Process Approximations
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Two-Stream Convolutional Networks for Action Recognition in Videos
Universal Option Models
Unsupervised Deep Haar Scattering on Graphs
Unsupervised learning of an efficient short-term memory network
Unsupervised Transcription of Piano Music
Unsupervised Transcription of Piano Music
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings
Using the Emergent Dynamics of Attractor Networks for Computation
Variational Gaussian Process State-Space Models
Weakly-supervised Discovery of Visual Pattern Configurations
Weighted importance sampling for off-policy learning with linear function approximation
Zero-shot recognition with unreliable attributes
Zeta Hull Pursuits: Learning Nonconvex Data Hulls