Downloads 2011

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

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