Downloads 2013

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

Number of events: 424

Accelerated Mini-Batch Stochastic Dual Coordinate Ascent
Accelerating Deep Neural Networks on Mobile Processor with Embedded Programmable Logic
Accelerating Stochastic Gradient Descent using Predictive Variance Reduction
A Comparative Framework for Preconditioned Lasso Algorithms
Acquiring and Analyzing the Activity of Large Neural Ensembles
Action from Still Image Dataset and Inverse Optimal Control to Learn Task Specific Visual Scanpaths
Action is in the Eye of the Beholder: Eye-gaze Driven Model for Spatio-Temporal Action Localization
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion
Actor-Critic Algorithms for Risk-Sensitive MDPs
Adaptive Anonymity via $b$-Matching
Adaptive dropout for training deep neural networks
Adaptive Market Making via Online Learning
Adaptive Step-Size for Policy Gradient Methods
Adaptive Submodular Maximization in Bandit Setting
Adaptivity to Local Smoothness and Dimension in Kernel Regression
A Deep Architecture for Matching Short Texts
A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data
Advances in Machine Learning for Sensorimotor Control
A Gang of Bandits
Aggregating Optimistic Planning Trees for Solving Markov Decision Processes
A Graphical Transformation for Belief Propagation: Maximum Weight Matchings and Odd-Sized Cycles
A Kernel Test for Three-Variable Interactions
A* Lasso for Learning a Sparse Bayesian Network Structure for Continuous Variables
A Latent Source Model for Nonparametric Time Series Classification
A memory frontier for complex synapses
A message-passing algorithm for multi-agent trajectory planning
A Mobile Development Platform for Adaptive Machine Learning and Neuromorphic Computing in Robotics
A multi-agent control framework for co-adaptation in brain-computer interfaces
Analyzing Hogwild Parallel Gaussian Gibbs Sampling
Analyzing the Harmonic Structure in Graph-Based Learning
An Approximate, Efficient LP Solver for LP Rounding
A New Convex Relaxation for Tensor Completion
Annealing between distributions by averaging moments
A Novel Two-Step Method for Cross Language Representation Learning
Approximate Bayesian Computation (ABC)
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
Approximate Dynamic Programming Finally Performs Well in the Game of Tetris
Approximate Gaussian process inference for the drift function in stochastic differential equations
Approximate Inference in Continuous Determinantal Processes
Approximate inference in latent Gaussian-Markov models from continuous time observations
A Scalable Approach to Probabilistic Latent Space Inference of Large-Scale Networks
A simple example of Dirichlet process mixture inconsistency for the number of components
A Stability-based Validation Procedure for Differentially Private Machine Learning
Auditing: Active Learning with Outcome-Dependent Query Costs
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
Bayesian entropy estimation for binary spike train data using parametric prior knowledge
Bayesian Estimation of Latently-grouped Parameters in Undirected Graphical Models
Bayesian Hierarchical Community Discovery
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits
Bayesian inference as iterated random functions with applications to sequential inference in graphical models
Bayesian inference for low rank spatiotemporal neural receptive fields
Bayesian Mixture Modelling and Inference based Thompson Sampling in Monte-Carlo Tree Search
Bayesian optimization explains human active search
Bayesian Optimization in Theory and Practice
Belief Propagation Algorithms: From Matching Problems to Network Discovery in Cancer Genomics
Bellman Error Based Feature Generation using Random Projections on Sparse Spaces
Better Approximation and Faster Algorithm Using the Proximal Average
Beyond Pairwise: Provably Fast Algorithms for Approximate $k$-Way Similarity Search
Big Learning : Advances in Algorithms and Data Management
BIG & QUIC: Sparse Inverse Covariance Estimation for a Million Variables
Binary to Bushy: Bayesian Hierarchical Clustering with the Beta Coalescent
Blind Calibration in Compressed Sensing using Message Passing Algorithms
B-test: A Non-parametric, Low Variance Kernel Two-sample Test
Buy-in-Bulk Active Learning
Capacity of strong attractor patterns to model behavioural and cognitive prototypes
Causal Inference on Time Series using Restricted Structural Equation Models
Causes and Counterfactuals: Concepts, Principles and Tools.
Cluster Trees on Manifolds
Codewebs: a Pedagogical Search Engine for Code Submissions to a MOOC
Compete to Compute
Compressive Feature Learning
Computing the Stationary Distribution Locally
Conditional Random Fields via Univariate Exponential Families
Confidence Intervals and Hypothesis Testing for High-Dimensional Statistical Models
Constructive Machine Learning
Context-sensitive active sensing in humans
Contrastive Learning Using Spectral Methods
Controlling Robot Dynamics With Spiking Neurons
Convergence of Monte Carlo Tree Search in Simultaneous Move Games
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
Convex Relaxations for Permutation Problems
Convex Tensor Decomposition via Structured Schatten Norm Regularization
Convex Two-Layer Modeling
Correlated random features for fast semi-supervised learning
Correlations strike back (again): the case of associative memory retrieval
Cross-Lingual Technologies: Text to Logic Mapping, Search and Classification over 100 Languages
Crowdsourcing: Theory, Algorithms and Applications
Curvature and Optimal Algorithms for Learning and Minimizing Submodular Functions
Data-driven Distributionally Robust Polynomial Optimization
Data Driven Education
Decision Jungles: Compact and Rich Models for Classification
Deep content-based music recommendation
Deep Content-Based Music Recommendation
Deep Fisher Networks for Large-Scale Image Classification
Deep Learning
Deep Learning for Computer Vision
Deep Mathematical Properties of Submodularity with Applications to Machine Learning
Deep Neural Networks for Object Detection
Demixing odors - fast inference in olfaction
Demos of Deep Learning Technologies at Baidu IDL
Density estimation from unweighted k-nearest neighbor graphs: a roadmap
Designed Measurements for Vector Count Data
DESPOT: Online POMDP Planning with Regularization
DeViSE: A Deep Visual-Semantic Embedding Model
DeViSE: A Deep Visual-Semantic Embedding Model
Di-BOSS™: Digital Building Operating System Solution
Dimension-Free Exponentiated Gradient
Direct 0-1 Loss Minimization and Margin Maximization with Boosting
Dirty Statistical Models
Discovering Hidden Variables in Noisy-Or Networks using Quartet Tests
Discrete Optimization in Machine Learning: Connecting Theory and Practice
Discriminative Transfer Learning with Tree-based Priors
Distributed Exploration in Multi-Armed Bandits
Distributed k-means and k-median clustering on general communication topologies
Distributed Representations of Words and Phrases and their Compositionality
Distributed Representations of Words and Phrases and their Compositionality
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data
Documents as multiple overlapping windows into grids of counts
Dropout Training as Adaptive Regularization
Dynamic Clustering via Asymptotics of the Dependent Dirichlet Process Mixture
Easy Text Classification with Machine Learning
EDML for Learning Parameters in Directed and Undirected Graphical Models
Efficient Algorithm for Privately Releasing Smooth Queries
Efficient Exploration and Value Function Generalization in Deterministic Systems
Efficient Online Inference for Bayesian Nonparametric Relational Models
Efficient Optimization for Sparse Gaussian Process Regression
Eluder Dimension and the Sample Complexity of Optimistic Exploration
Embed and Project: Discrete Sampling with Universal Hashing
Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion
Estimating LASSO Risk and Noise Level
Estimating the Unseen: Improved Estimators for Entropy and other Properties
Estimation Bias in Multi-Armed Bandit Algorithms for Search Advertising
Estimation, Optimization, and Parallelism when Data is Sparse
Exact and Stable Recovery of Pairwise Interaction Tensors
Extracting regions of interest from biological images with convolutional sparse block coding
Extreme Classification: Multi-Class & Multi-Label Learning with Millions of Categories
Factorized Asymptotic Bayesian Inference for Latent Feature Models
Fantope Projection and Selection: A near-optimal convex relaxation of sparse PCA
Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis
Fast Determinantal Point Process Sampling with Application to Clustering
Faster Ridge Regression via the Subsampled Randomized Hadamard Transform
Fast Template Evaluation with Vector Quantization
Firing rate predictions in optimal balanced networks
First-order Decomposition Trees
Flexible sampling of discrete data correlations without the marginal distributions
Forgetful Bayes and myopic planning: Human learning and decision-making in a bandit setting
From Bandits to Experts: A Tale of Domination and Independence
Frontiers of Network Analysis: Methods, Models, and Applications
Gaussian Process Conditional Copulas with Applications to Financial Time Series
Generalized Denoising Auto-Encoders as Generative Models
Generalized Method-of-Moments for Rank Aggregation
Generalized Random Utility Models with Multiple Types
Generalizing Analytic Shrinkage for Arbitrary Covariance Structures
Geometric optimisation on positive definite matrices for elliptically contoured distributions
Global MAP-Optimality by Shrinking the Combinatorial Search Area with Convex Relaxation
Global Solver and Its Efficient Approximation for Variational Bayesian Low-rank Subspace Clustering
Graphical Models for Inference with Missing Data
Greedy Algorithms, Frank-Wolfe and Friends - A modern perspective
Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms
Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream
High-Dimensional Gaussian Process Bandits
High-dimensional Statistical Inference in the Brain
Higher Order Priors for Joint Intrinsic Image, Objects, and Attributes Estimation
How to Hedge an Option Against an Adversary: Black-Scholes Pricing is Minimax Optimal
Improved and Generalized Upper Bounds on the Complexity of Policy Iteration
Inferring neural population dynamics from multiple partial recordings of the same neural circuit
Information-theoretic lower bounds for distributed statistical estimation with communication constraints
Integrated Non-Factorized Variational Inference
Inverse Density as an Inverse Problem: the Fredholm Equation Approach
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
Knowledge Extraction from Text (KET)
k-Prototype Learning for 3D Rigid Structures
Large Scale Distributed Sparse Precision Estimation
Large Scale Matrix Analysis and Inference
Lasso Screening Rules via Dual Polytope Projection
Latent Maximum Margin Clustering
Latent Structured Active Learning
Learning Adaptive Value of Information for Structured Prediction
Learning a Deep Compact Image Representation for Visual Tracking
Learning and using language via recursive pragmatic reasoning about other agents
Learning Chordal Markov Networks by Constraint Satisfaction
Learning Efficient Random Maximum A-Posteriori Predictors with Non-Decomposable Loss Functions
Learning Faster From Easy Data
Learning Feature Selection Dependencies in Multi-task Learning
Learning from Limited Demonstrations
Learning Gaussian Graphical Models with Observed or Latent FVSs
Learning Hidden Markov Models from Non-sequence Data via Tensor Decomposition
Learning invariant representations and applications to face verification
Learning Kernels Using Local Rademacher Complexity
Learning Multi-level Sparse Representations
Learning Multiple Models via Regularized Weighting
Learning Prices for Repeated Auctions with Strategic Buyers
Learning Stochastic Feedforward Neural Networks
Learning Stochastic Inverses
Learning the Local Statistics of Optical Flow
Learning to Interact
Learning to Pass Expectation Propagation Messages
Learning to Prune in Metric and Non-Metric Spaces
Learning Trajectory Preferences for Manipulators via Iterative Improvement
Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space
Learning with Noisy Labels
Learning word embeddings efficiently with noise-contrastive estimation
Least Informative Dimensions
Lexical and Hierarchical Topic Regression
Linear Convergence with Condition Number Independent Access of Full Gradients
Linear decision rule as aspiration for simple decision heuristics
Locally Adaptive Bayesian Multivariate Time Series
Local Privacy and Minimax Bounds: Sharp Rates for Probability Estimation
Low-Rank Matrix and Tensor Completion via Adaptive Sampling
Low-rank matrix reconstruction and clustering via approximate message passing
Machine Learning for Clinical Data Analysis and Healthcare
Machine Learning for Sustainability
Machine Learning in Computational Biology
Machine Learning Open Source Software: Towards Open Workflows
Machine Teaching for Bayesian Learners in the Exponential Family
Making Smooth Topical Connections on Touch Devices
Manifold-based Similarity Adaptation for Label Propagation
Mapping paradigm ontologies to and from the brain
Marginals-to-Models Reducibility
Matrix Completion From any Given Set of Observations
Matrix factorization with binary components
Mechanisms Underlying Visual Object Recognition: Humans vs. Neurons vs. Machines
Memoized Online Variational Inference for Dirichlet Process Mixture Models
Memory Limited, Streaming PCA
Memory Reactivation in Awake and Sleep States
Message Passing Inference with Chemical Reaction Networks
Mid-level Visual Element Discovery as Discriminative Mode Seeking
Minimax Optimal Algorithms for Unconstrained Linear Optimization
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
Mixed Optimization for Smooth Functions
MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 1)
MLINI-13: Machine Learning and Interpretation in Neuroimaging (Day 2)
Modeling Clutter Perception using Parametric Proto-object Partitioning
Modeling Overlapping Communities with Node Popularities
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Modern Nonparametric Methods in Machine Learning
Moment-based Uniform Deviation Bounds for $k$-means and Friends
More data speeds up training time in learning halfspaces over sparse vectors
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
(More) Efficient Reinforcement Learning via Posterior Sampling
Multiclass Total Variation Clustering
Multilinear Dynamical Systems for Tensor Time Series
Multi-Prediction Deep Boltzmann Machines
Multiscale Dictionary Learning for Estimating Conditional Distributions
Multisensory Encoding, Decoding, and Identification
Multi-Task Bayesian Optimization
NCS: A Novel CPU/GPU Simulation Environment for Large-Scale Biologically-Realistic Neural Modeling
(Nearly) Optimal Algorithms for Private Online Learning in Full-information and Bandit Settings
Near-optimal Anomaly Detection in Graphs using Lovasz Extended Scan Statistic
Near-Optimal Entrywise Sampling for Data Matrices
Neural Information Processing Scaled for Bioacoustics : NIPS4B
Neural Reinforcement Learning
Neural representation of action sequences: how far can a simple snippet-matching model take us?
New Directions in Transfer and Multi-Task: Learning Across Domains and Tasks
New Methods for the Analysis of Genome Variation Data
New Subsampling Algorithms for Fast Least Squares Regression
NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms
Noise-Enhanced Associative Memories
Non-Linear Domain Adaptation with Boosting
Nonparametric Multi-group Membership Model for Dynamic Networks
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty
On Algorithms for Sparse Multi-factor NMF
On Decomposing the Proximal Map
One-shot learning and big data with n=2
One-shot learning by inverting a compositional causal process
On Flat versus Hierarchical Classification in Large-Scale Taxonomies
Online learning in episodic Markovian decision processes by relative entropy policy search
Online Learning in Markov Decision Processes with Adversarially Chosen Transition Probability Distributions
Online Learning of Dynamic Parameters in Social Networks
Online Learning of Nonparametric Mixture Models via Sequential Variational Approximation
Online Learning with Costly Features and Labels
Online Learning with Switching Costs and Other Adaptive Adversaries
Online PCA for Contaminated Data
Online Robust PCA via Stochastic Optimization
Online Variational Approximations to non-Exponential Family Change Point Models: With Application to Radar Tracking
On model selection consistency of penalized M-estimators: a geometric theory
On Poisson Graphical Models
On Sampling from the Gibbs Distribution with Random Maximum A-Posteriori Perturbations
On the Complexity and Approximation of Binary Evidence in Lifted Inference
On the Expressive Power of Restricted Boltzmann Machines
On the Linear Convergence of the Proximal Gradient Method for Trace Norm Regularization
On the Relationship Between Binary Classification, Bipartite Ranking, and Binary Class Probability Estimation
On the Sample Complexity of Subspace Learning
OPT2013: Optimization for Machine Learning
Optimal integration of visual speed across different spatiotemporal frequency channels
Optimal Neural Population Codes for High-dimensional Stimulus Variables
Optimistic Concurrency Control for Distributed Unsupervised Learning
Optimistic policy iteration and natural actor-critic: A unifying view and a non-optimality result
Optimization, Learning, and Games with Predictable Sequences
Optimizing Instructional Policies
Output Representation Learning
PAC-Bayes-Empirical-Bernstein Inequality
Parallel Sampling of DP Mixture Models using Sub-Cluster Splits
Parametric Task Learning
Pass-efficient unsupervised feature selection
Perfect Associative Learning with Spike-Timing-Dependent Plasticity
Perturbations, Optimization, and Statistics
Phase Retrieval using Alternating Minimization
Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games.
Point Based Value Iteration with Optimal Belief Compression for Dec-POMDPs
Polar Operators for Structured Sparse Estimation
Policy Shaping: Integrating Human Feedback with Reinforcement Learning
Predicting Parameters in Deep Learning
Predictive PAC Learning and Process Decompositions
Prior-free and prior-dependent regret bounds for Thompson Sampling
Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization Algorithms
Probabilistic Models for Big Data
Probabilistic Movement Primitives
Probabilistic Principal Geodesic Analysis
Projected Natural Actor-Critic
Projecting Ising Model Parameters for Fast Mixing
Provable Subspace Clustering: When LRR meets SSC
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions
Randomized Methods for Machine Learning
Rapid Distance-Based Outlier Detection via Sampling
Real-Time Inference for a Gamma Process Model of Neural Spiking
Reasoning With Neural Tensor Networks for Knowledge Base Completion
Reciprocally Coupled Local Estimators Implement Bayesian Information Integration Distributively
Reconciling "priors'' & "priors" without prejudice?
Recurrent linear models of simultaneously-recorded neural populations
Recurrent networks of coupled Winner-Take-All oscillators for solving constraint satisfaction problems
Reflection methods for user-friendly submodular optimization
Regression-tree Tuning in a Streaming Setting
Regret based Robust Solutions for Uncertain Markov Decision Processes
Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
Regularized Spectral Clustering under the Degree-Corrected Stochastic Blockmodel
Reinforcement Learning in Robust Markov Decision Processes
Relevance Topic Model for Unstructured Social Group Activity Recognition
Reservoir Boosting : Between Online and Offline Ensemble Learning
Reshaping Visual Datasets for Domain Adaptation
Resource-Efficient Machine Learning
Restricting exchangeable nonparametric distributions
Reward Mapping for Transfer in Long-Lived Agents
RNADE: The real-valued neural autoregressive density-estimator
Robust Bloom Filters for Large MultiLabel Classification Tasks
Robust Data-Driven Dynamic Programming
Robust Image Denoising with Multi-Column Deep Neural Networks
Robust learning of low-dimensional dynamics from large neural ensembles
Robust Low Rank Kernel Embeddings of Multivariate Distributions
Robust Multimodal Graph Matching: Sparse Coding Meets Graph Matching
Robust Sparse Principal Component Regression under the High Dimensional Elliptical Model
Robust Spatial Filtering with Beta Divergence
Robust Transfer Principal Component Analysis with Rank Constraints
Scalable Inference for Logistic-Normal Topic Models
Scalable Influence Estimation in Continuous-Time Diffusion Networks
Scalable kernels for graphs with continuous attributes
Scoring Workers in Crowdsourcing: How Many Control Questions are Enough?
Semi-supervised learning for multilingual text to logic mapping
Sensor Selection in High-Dimensional Gaussian Trees with Nuisances
Sequential Transfer in Multi-armed Bandit with Finite Set of Models
Sign Cauchy Projections and Chi-Square Kernel
Similarity Component Analysis
Simultaneous Rectification and Alignment via Robust Recovery of Low-rank Tensors
Sinkhorn Distances: Lightspeed Computation of Optimal Transport
Sketching Structured Matrices for Faster Nonlinear Regression
Small, n=me, Data
Small-Variance Asymptotics for Hidden Markov Models
Solving inverse problem of Markov chain with partial observations
Solving the multi-way matching problem by permutation synchronization
Sparse Additive Text Models with Low Rank Background
Sparse Inverse Covariance Estimation with Calibration
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions
Sparse Overlapping Sets Lasso for Multitask Learning and its Application to fMRI Analysis
Spectral methods for neural characterization using generalized quadratic models
Speeding up Permutation Testing in Neuroimaging
Speedup Matrix Completion with Side Information: Application to Multi-Label Learning
Spike train entropy-rate estimation using hierarchical Dirichlet process priors
Statistical Active Learning Algorithms
Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
Stochastic Convex Optimization with Multiple Objectives
Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex
Stochastic Majorization-Minimization Algorithms for Large-Scale Optimization
Stochastic Optimization of PCA with Capped MSG
Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs
Streaming Variational Bayes
Structured Learning via Logistic Regression
Submodular Optimization with Submodular Cover and Submodular Knapsack Constraints
Summary Statistics for Partitionings and Feature Allocations
Supervised Sparse Analysis and Synthesis Operators
Symbolic Opportunistic Policy Iteration for Factored-Action MDPs
Synthesizing Robust Plans under Incomplete Domain Models
The Fast Convergence of Incremental PCA
The Online Revolution: Learning without Limits
The Pareto Regret Frontier
The Power of Asymmetry in Binary Hashing
The Randomized Dependence Coefficient
The Three-Weight Algorithm: Enhancing ADMM for Large-Scale Distributed Optimization
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Third-Order Edge Statistics: Contour Continuation, Curvature, and Cortical Connections
Thompson Sampling for 1-Dimensional Exponential Family Bandits
Top-Down Regularization of Deep Belief Networks
Topic Modeling for Robots
Topic Models: Computation, Application, and Evaluation
Tracking Time-varying Graphical Structure
Trading Computation for Communication: Distributed Stochastic Dual Coordinate Ascent
Training and Analysing Deep Recurrent Neural Networks
Transfer Learning in a Transductive Setting
Translating Embeddings for Modeling Multi-relational Data
Transportability from Multiple Environments with Limited Experiments
Two-Target Algorithms for Infinite-Armed Bandits with Bernoulli Rewards
Understanding Dropout
Understanding variable importances in forests of randomized trees
Universal models for binary spike patterns using centered Dirichlet processes
Unsupervised Spectral Learning of Finite State Transducers
Unsupervised Structure Learning of Stochastic And-Or Grammars
Using multiple samples to learn mixture models
Variance Reduction for Stochastic Gradient Optimization
Variational Inference for Mahalanobis Distance Metrics in Gaussian Process Regression
Variational Planning for Graph-based MDPs
Variational Policy Search via Trajectory Optimization
Visual Concept Learning: Combining Machine Vision and Bayesian Generalization on Concept Hierarchies
Wavelets on Graphs via Deep Learning
What Are the Invariant Occlusive Components of Image Patches? A Probabilistic Generative Approach
What Difference Does Personalization Make?
What do row and column marginals reveal about your dataset?
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
When in Doubt, SWAP: High-Dimensional Sparse Recovery from Correlated Measurements
Which Space Partitioning Tree to Use for Search?
Workshop on Spectral Learning
Zero-Shot Learning Through Cross-Modal Transfer
Σ-Optimality for Active Learning on Gaussian Random Fields