# Downloads

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