# Downloads

Number of events: 356

- $\theta$-MRF: Capturing Spatial and Semantic Structure in the Parameters for Scene Understanding
- 2nd Workshop on Computational Social Science and the Wisdom of Crowds
- A blind sparse deconvolution method for neural spike identification
- A Brain-Machine Interface Operating with a Real-Time Spiking Neural Network Control Algorithm
- Accelerated Adaptive Markov Chain for Partition Function Computation
- A Collaborative Mechanism for Crowdsourcing Prediction Problems
- A concave regularization technique for sparse mixture models
- A Convergence Analysis of Log-Linear Training
- Action-Gap Phenomenon in Reinforcement Learning
- Active Classification based on Value of Classifier
- Active dendrites: adaptation to spike-based communication
- Active learning of neural response functions with Gaussian processes
- Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity
- Active Learning with a Drifting Distribution
- Active Ranking using Pairwise Comparisons
- Adaptive Hedge
- Additive Gaussian Processes
- A Denoising View of Matrix Completion
- Advice Refinement in Knowledge-Based SVMs
- A Global Structural EM Algorithm for a Model of Cancer Progression
- Agnostic Selective Classification
- AISoy1, a Robot that Perceives, Feels and Makes Decisions
- Algorithms and hardness results for parallel large margin learning
- Algorithms for Hyper-Parameter Optimization
- A Machine Learning Approach to Predict Chemical Reactions
- A Maximum Margin Multi-Instance Learning Framework for Image Categorization
- A Model for Temporal Dependencies in Event Streams
- A More Powerful Two-Sample Test in High Dimensions using Random Projection
- A Multilinear Subspace Regression Method Using Orthogonal Tensors Decompositions
- Analysis and Improvement of Policy Gradient Estimation
- Analytical Results for the Error in Filtering of Gaussian Processes
- An Application of Tree-Structured Expectation Propagation for Channel Decoding
- An Empirical Evaluation of Thompson Sampling
- An Exact Algorithm for F-Measure Maximization
- An ideal observer model for identifying the reference frame of objects
- A Non-Parametric Approach to Dynamic Programming
- An Unsupervised Decontamination Procedure For Improving The Reliability Of Human Judgments
- Approximating Semidefinite Programs in Sublinear Time
- A rational model of causal inference with continuous causes
- A Reinforcement Learning Theory for Homeostatic Regulation
- A reinterpretation of the policy oscillation phenomenon in approximate policy iteration
- A smartphone 3D functional brain scanner
- A Two-Stage Weighting Framework for Multi-Source Domain Adaptation
- Automated Refinement of Bayes Networks' Parameters based on Test Ordering Constraints
- Autonomous Learning of Action Models for Planning
- Bayesian Bias Mitigation for Crowdsourcing
- Bayesian Nonparametric Methods: Hope or Hype?
- Bayesian optimization, experimental design and bandits: Theory and applications
- Bayesian Partitioning of Large-Scale Distance Data
- Bayesian Spike-Triggered Covariance Analysis
- Beating SGD: Learning SVMs in Sublinear Time
- Better Mini-Batch Algorithms via Accelerated Gradient Methods
- Beyond Mahalanobis: Supervised Large-Scale Learning of Similarity
- Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts
- Big Learning: Algorithms, Systems, and Tools for Learning at Scale
- Blending Autonomous Exploration and Apprenticeship Learning
- Boosting with Maximum Adaptive Sampling
- Budgeted Optimization with Concurrent Stochastic-Duration Experiments
- Causal Discovery with Cyclic Additive Noise Models
- Challenges in Learning Hierarchical Models: Transfer Learning and Optimization
- Choice Models and Preference Learning
- Clustered Multi-Task Learning Via Alternating Structure Optimization
- Clustering via Dirichlet Process Mixture Models for Portable Skill Discovery
- Collective Graphical Models
- Committing Bandits
- Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
- Complexity of Inference in Latent Dirichlet Allocation
- Composite Multiclass Losses
- Computational Trade-offs in Statistical Learning
- Confidence Sets for Network Structure
- Contextual Gaussian Process Bandit Optimization
- Continuous-Time Regression Models for Longitudinal Networks
- Contour-Based Large Scale Image Retrieval Platform
- Convergence Rates of Inexact Proximal-Gradient Methods for Convex Optimization
- Convergent Bounds on the Euclidean Distance
- Convergent Fitted Value Iteration with Linear Function Approximation
- Copulas in Machine Learning
- Co-regularized Multi-view Spectral Clustering
- Cosmology meets Machine Learning
- Co-Training for Domain Adaptation
- Crowdclustering
- Data Skeletonization via Reeb Graphs
- Decision Making with Multiple Imperfect Decision Makers
- Decoding of Finger Flexion from Electrocorticographic Signals Using Switching Non-Parametric Dynamic Systems
- Deep Learning and Unsupervised Feature Learning
- Demixed Principal Component Analysis
- Detecting Quakes with Clouds and Phones: the Community Seismic Network
- Differentially Private M-Estimators
- Dimensionality Reduction Using the Sparse Linear Model
- Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators
- Discrete Optimization in Machine Learning (DISCML): Uncertainty, Generalization and Feedback
- Distributed Delayed Stochastic Optimization
- Diversity in an Olfactory Network
- Divide-and-Conquer Matrix Factorization
- Domain Adaptation Workshop: Theory and Application
- Dynamical segmentation of single trials from population neural data
- Efficient anomaly detection using bipartite k-NN graphs
- Efficient coding with a population of Linear-Nonlinear neurons
- Efficient Inference in Fully Connected CRFs with Gaussian Edge Potentials
- Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
- Efficient Methods for Overlapping Group Lasso
- Efficient Offline Communication Policies for Factored Multiagent POMDPs
- Efficient Online Learning via Randomized Rounding
- EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning
- Emergence of Multiplication in a Biophysical Model of a Wide-Field Visual Neuron for Computing Object Approaches: Dynamics, Peaks, & Fits
- Empirical models of spiking in neural populations
- Energetically Optimal Action Potentials
- Environmental statistics and the trade-off between model-based and TD learning in humans
- Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron
- Evaluating computational models of preference learning
- Exploiting spatial overlap to efficiently compute appearance distances between image windows
- Expressive Power and Approximation Errors of Restricted Boltzmann Machines
- Extracting Speaker-Specific Information with a Regularized Siamese Deep Network
- Facial Expression Transfer with Input-Output Temporal Restricted Boltzmann Machines
- Fast and Accurate k-means For Large Datasets
- Fast and Balanced: Efficient Label Tree Learning for Large Scale Object Recognition
- Fast approximate submodular minimization
- Finite Time Analysis of Stratified Sampling for Monte Carlo
- Flexible, Multivariate Point Process Models for Unlocking the Neural Code
- From Bandits to Experts: On the Value of Side-Observations
- From kernels to causal inference
- From statistical genetics to predictive models in personalized medicine
- From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models
- Gaussian process modulated renewal processes
- Gaussian Process Training with Input Noise
- Generalised Coupled Tensor Factorisation
- Generalization Bounds and Consistency for Latent Structural Probit and Ramp Loss
- Generalized Beta Mixtures of Gaussians
- Generalized Lasso based Approximation of Sparse Coding for Visual Recognition
- Generalizing from Several Related Classification Tasks to a New Unlabeled Sample
- Global Solution of Fully-Observed Variational Bayesian Matrix Factorization is Column-Wise Independent
- Graphical Models for the Internet
- Greedy Algorithms for Structurally Constrained High Dimensional Problems
- Greedy Model Averaging
- Group Anomaly Detection using Flexible Genre Models
- Haptic Belt with Pedestrian Detection
- Hashing Algorithms for Large-Scale Learning
- Heavy-tailed Distances for Gradient Based Image Descriptors
- Hierarchically Supervised Latent Dirichlet Allocation
- Hierarchical Matching Pursuit for Recognition: Architecture and Fast Algorithms
- Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation
- Hierarchical Topic Modeling for Analysis of Time-Evolving Personal Choices
- High-Dimensional Graphical Model Selection: Tractable Graph Families and Necessary Conditions
- High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity
- Higher-Order Correlation Clustering for Image Segmentation
- History distribution matching method for predicting effectiveness of HIV combination therapies
- Hogwild!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
- How biased are maximum entropy models?
- How Do Humans Teach: On Curriculum Learning and Teaching Dimension
- ICA with Reconstruction Cost for Efficient Overcomplete Feature Learning
- Identifying Alzheimer's Disease-Related Brain Regions from Multi-Modality Neuroimaging Data using Sparse Composite Linear Discrimination Analysis
- Im2Text: Describing Images Using 1 Million Captioned Photographs
- Image Parsing with Stochastic Scene Grammar
- Improved Algorithms for Linear Stochastic Bandits
- Improving Topic Coherence with Regularized Topic Models
- Inductive reasoning about chimeric creatures
- Inference in continuous time changepoint point models
- Inferring Interaction Networks using the IBP applied to microRNA Target Prediction
- Inferring spike-timing-dependent plasticity from spike train data
- Infinite Latent SVM for Classification and Multi-task Learning
- Information Rates and Optimal Decoding in Large Neural Populations
- Information Theory in Learning and Control
- Integrating Language and Vision
- Interpretable Decoding of Higher Cognitive States from Neural Data
- Inverting Grice's Maxims to Learn Rules from Natural Language Extractions
- Iterative Learning for Reliable Crowdsourcing Systems
- Joint 3D Estimation of Objects and Scene Layout
- Kernel Bayes' Rule
- Kernel Embeddings of Latent Tree Graphical Models
- k-NN Regression Adapts to Local Intrinsic Dimension
- Lagrangian Relaxation Algorithms for Inference in Natural Language Processing
- Large-Scale Category Structure Aware Image Categorization
- Large-Scale Sparse Principal Component Analysis with Application to Text Data
- Learning About Sensorimotor Data
- Learning a Distance Metric from a Network
- Learning Anchor Planes for Classification
- Learning a Tree of Metrics with Disjoint Visual Features
- Learning Auto-regressive Models from Sequence and Non-sequence Data
- Learning Eigenvectors for Free
- Learning Higher-Order Graph Structure with Features by Structure Penalty
- Learning in Hilbert vs. Banach Spaces: A Measure Embedding Viewpoint
- Learning large-margin halfspaces with more malicious noise
- Learning Patient-Specific Cancer Survival Distributions as a Sequence of Dependent Regressors
- Learning person-object interactions for action recognition in still images
- Learning Probabilistic Non-Linear Latent Variable Models for Tracking Complex Activities
- Learning Semantics
- Learning sparse inverse covariance matrices in the presence of confounders
- Learning Sparse Representations of High Dimensional Data on Large Scale Dictionaries
- Learning to Agglomerate Superpixel Hierarchies
- Learning to Learn with Compound HD Models
- Learning to Search Efficiently in High Dimensions
- Learning unbelievable marginal probabilities
- Learning with the weighted trace-norm under arbitrary sampling distributions
- Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation
- Linear Programming Relaxations for Graphical Models
- Linear Submodular Bandits and their Application to Diversified Retrieval
- Lower Bounds for Passive and Active Learning
- Machine Learning and Interpretation in Neuroimaging (MLINI-2011)
- Machine Learning for Sustainability
- Machine Learning in Computational Biology
- Machine Learning meets Computational Photography
- Manifold Precis: An Annealing Technique for Diverse Sampling of Manifolds
- MAP Inference for Bayesian Inverse Reinforcement Learning
- Matrix Completion for Image Classification
- Maximal Cliques that Satisfy Hard Constraints with Application to Deformable Object Model Learning
- Maximum Covariance Unfolding : Manifold Learning for Bimodal Data
- Maximum Margin Multi-Label Structured Prediction
- Message-Passing for Approximate MAP Inference with Latent Variables
- Metric Learning with Multiple Kernels
- Minimax Localization of Structural Information in Large Noisy Matrices
- Modelling Genetic Variations using Fragmentation-Coagulation Processes
- Modern Bayesian Nonparametrics
- Monte Carlo Value Iteration with Macro-Actions
- Multi-armed bandits on implicit metric spaces
- Multi-Bandit Best Arm Identification
- Multiclass Boosting: Theory and Algorithms
- Multiple Instance Filtering
- Multiple Instance Learning on Structured Data
- Multi-View Learning of Word Embeddings via CCA
- Natural Algorithms
- Nearest Neighbor based Greedy Coordinate Descent
- Neural Reconstruction with Approximate Message Passing (NeuRAMP)
- Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability
- New Frontiers in Model Order Selection
- Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction
- Noise Thresholds for Spectral Clustering
- Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning
- Non-conjugate Variational Message Passing for Multinomial and Binary Regression
- Nonlinear Inverse Reinforcement Learning with Gaussian Processes
- Nonnegative dictionary learning in the exponential noise model for adaptive music signal representation
- Non-parametric Group Orthogonal Matching Pursuit for Sparse Learning with Multiple Kernels
- Nonstandard Interpretations of Probabilistic Programs for Efficient Inference
- Object Detection with Grammar Models
- On Learning Discrete Graphical Models using Greedy Methods
- Online Learning: Stochastic, Constrained, and Smoothed Adversaries
- Online structured-output learning for real-time object tracking and detection
- Online Submodular Set Cover, Ranking, and Repeated Active Learning
- On Strategy Stitching in Large Extensive Form Multiplayer Games
- On the accuracy of l1-filtering of signals with block-sparse structure
- On the Analysis of Multi-Channel Neural Spike Data
- On the Completeness of First-Order Knowledge Compilation for Lifted Probabilistic Inference
- On the Universality of Online Mirror Descent
- On Tracking The Partition Function
- On U-processes and clustering performance
- Optimal learning rates for least squares SVMs using Gaussian kernels
- Optimal Reinforcement Learning for Gaussian Systems
- Optimistic Optimization of Deterministic Functions
- Optimization for Machine Learning
- Orthogonal Matching Pursuit with Replacement
- PAC-Bayesian Analysis of Contextual Bandits
- Penalty Decomposition Methods for Rank Minimization
- Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning
- Phase transition in the family of p-resistances
- Philosophy and Machine Learning
- PiCoDes: Learning a Compact Code for Novel-Category Recognition
- Policy Gradient Coagent Networks
- Portmanteau Vocabularies for Multi-Cue Image Representation
- Practical Variational Inference for Neural Networks
- Predicting Dynamic Difficulty
- Predicting response time and error rates in visual search
- Prediction strategies without loss
- Priors over Recurrent Continuous Time Processes
- Prismatic Algorithm for Discrete D.C. Programming Problem
- Probabilistic amplitude and frequency demodulation
- Probabilistic Joint Image Segmentation and Labeling
- Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations
- Projection onto A Nonnegative Max-Heap
- Pylon Model for Semantic Segmentation
- Quasi-Newton Methods for Markov Chain Monte Carlo
- Query-Aware MCMC
- Randomized Algorithms for Comparison-based Search
- Ranking annotators for crowdsourced labeling tasks
- Rapid Deformable Object Detection using Dual-Tree Branch-and-Bound
- Real-time Multi-class Segmentation using Depth Cues
- Real-time social media analysis with TWIMPACT
- Reconstructing Patterns of Information Diffusion from Incomplete Observations
- Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance
- Regularized Laplacian Estimation and Fast Eigenvector Approximation
- Reinforcement Learning using Kernel-Based Stochastic Factorization
- Relations between machine learning problems - an approach to unify the field
- Relative Density-Ratio Estimation for Robust Distribution Comparison
- Reproducing biologically realistic firing patterns on a highly-accelerated neuromorphic hardware system
- Robust Lasso with missing and grossly corrupted observations
- Robust Multi-Class Gaussian Process Classification
- RTRMC: A Riemannian trust-region method for low-rank matrix completion
- Scalable Training of Mixture Models via Coresets
- See the Tree Through the Lines: The Shazoo Algorithm
- Select and Sample: A Model of Efficient Neural Inference and Learning
- Selecting Receptive Fields in Deep Networks
- Selecting the State-Representation in Reinforcement Learning
- Selective Prediction of Financial Trends with Hidden Markov Models
- Semantic Labeling of 3D Point Clouds for Indoor Scenes
- Semi-supervised Regression via Parallel Field Regularization
- SENNA Natural Language Processing Demo
- Sequence learning with hidden units in spiking neural networks
- Shallow vs. Deep Sum-Product Networks
- Shaping Level Sets with Submodular Functions
- ShareBoost: Efficient multiclass learning with feature sharing
- Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment
- Similarity-based Learning via Data Driven Embeddings
- Simultaneous Sampling and Multi-Structure Fitting with Adaptive Reversible Jump MCMC
- Solving Decision Problems with Limited Information
- SpaRCS: Recovering low-rank and sparse matrices from compressive measurements
- Sparse Bayesian Multi-Task Learning
- Sparse Estimation with Structured Dictionaries
- Sparse Features for PCA-Like Linear Regression
- Sparse Filtering
- Sparse Inverse Covariance Matrix Estimation Using Quadratic Approximation
- Sparse Manifold Clustering and Embedding
- Sparse recovery by thresholded non-negative least squares
- Sparse Recovery with Brownian Sensing
- Sparse Representation and Low-rank Approximation
- Sparsity: algorithms, approximations, and analysis
- Spatial distance dependent Chinese Restaurant Process for image segmentation
- Spectral Methods for Learning Multivariate Latent Tree Structure
- Speedy Q-Learning
- Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning
- Statistical Performance of Convex Tensor Decomposition
- Statistical Tests for Optimization Efficiency
- Stochastic convex optimization with bandit feedback
- Structural equations and divisive normalization for energy-dependent component analysis
- Structured Learning for Cell Tracking
- Structured sparse coding via lateral inhibition
- Structure Learning for Optimization
- Submodular Multi-Label Learning
- Target Neighbor Consistent Feature Weighting for Nearest Neighbor Classification
- TD_gamma: Re-evaluating Complex Backups in Temporal Difference Learning
- t-divergence Based Approximate Inference
- Testing a Bayesian Measure of Representativeness Using a Large Image Database
- The 4th International Workshop on Music and Machine Learning: Learning from Musical Structure
- The Doubly Correlated Nonparametric Topic Model
- The Fast Convergence of Boosting
- The Fixed Points of Off-Policy TD
- The Impact of Unlabeled Patterns in Rademacher Complexity Theory for Kernel Classifiers
- The Kernel Beta Process
- The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
- The Manifold Tangent Classifier
- The Neuronal Replicator Hypothesis: Novel Mechanisms for Information Transfer and Search in the Brain
- Thinning Measurement Models and Questionnaire Design
- Trace Lasso: a trace norm regularization for correlated designs
- Transfer from Multiple MDPs
- Transfer Learning by Borrowing Examples
- Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories
- Understanding the Intrinsic Memorability of Images
- Unfolding Recursive Autoencoders for Paraphrase Detection
- Unifying Framework for Fast Learning Rate of Non-Sparse Multiple Kernel Learning
- Unifying Non-Maximum Likelihood Learning Objectives with Minimum KL Contraction
- Uniqueness of Belief Propagation on Signed Graphs
- Universal low-rank matrix recovery from Pauli measurements
- Unsupervised learning models of primary cortical receptive fields and receptive field plasticity
- Variance Penalizing AdaBoost
- Variance Reduction in Monte-Carlo Tree Search
- Variational Gaussian Process Dynamical Systems
- Variational Learning for Recurrent Spiking Networks
- Video Annotation and Tracking with Active Learning
- Why The Brain Separates Face Recognition From Object Recognition