Downloads 2010
Number of events: 346
- 2-D Cursor Movement using EEG
- A Bayesian Approach to Concept Drift
- A Bayesian Framework for Figure-Ground Interpretation
- A biologically plausible network for the computation of orientation dominance
- Accounting for network effects in neuronal responses using L1 regularized point process models
- Accounting for network effects in neuronal responses using L1 regularized point process models
- A Computational Decision Theory for Interactive Assistants
- Active Estimation of F-Measures
- Active Instance Sampling via Matrix Partition
- Active Learning Applied to Patient-Adaptive Heartbeat Classification
- Active Learning by Querying Informative and Representative Examples
- Adaptive Multi-Task Lasso: with Application to eQTL Detection
- A Dirty Model for Multi-task Learning
- A Discriminative Latent Model of Image Region and Object Tag Correspondence
- Advances in Activity-Dependent Synaptic Plasticity
- A Family of Penalty Functions for Structured Sparsity
- A framework for evaluating and designing 'attention mechanism' implementations based on tracking of human eye movements
- Agnostic Active Learning Without Constraints
- A Log-Domain Implementation of the Diffusion Network in Very Large Scale Integration
- An Alternative to Low-level-Sychrony-Based Methods for Speech Detection
- An analysis on negative curvature induced by singularity in multi-layer neural-network learning
- An Approximate Inference Approach to Temporal Optimization in Optimal Control
- A New Probabilistic Model for Rank Aggregation
- An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
- A novel family of non-parametric cumulative based divergences for point processes
- A Novel Kernel for Learning a Neuron Model from Spike Train Data
- A POMDP Extension with Belief-dependent Rewards
- Approximate Inference by Compilation to Arithmetic Circuits
- Approximate inference in continuous time Gaussian-Jump processes
- A Primal-Dual Algorithm for Group Sparse Regularization with Overlapping Groups
- A Primal-Dual Message-Passing Algorithm for Approximated Large Scale Structured Prediction
- A rational decision making framework for inhibitory control
- A Reduction from Apprenticeship Learning to Classification
- A Theory of Multiclass Boosting
- Attractor Dynamics with Synaptic Depression
- A unified model of short-range and long-range motion perception
- Auto-Regressive HMM Inference with Incomplete Data for Short-Horizon Wind Forecasting
- A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model
- Avoiding False Positive in Multi-Instance Learning
- Basis Construction from Power Series Expansions of Value Functions
- Batch Bayesian Optimization via Simulation Matching
- Bayesian Action-Graph Games
- b-Bit Minwise Hashing for Estimating Three-Way Similarities
- BCI Demonstration using a Dry-Electrode
- Beyond Actions: Discriminative Models for Contextual Group Activities
- Beyond classification: Machine Learning for next generation Computer Vision challenges
- Block Variable Selection in Multivariate Regression and High-dimensional Causal Inference
- Boosting Classifier Cascades
- Bootstrapping Apprenticeship Learning
- Brain covariance selection: better individual functional connectivity models using population prior
- Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations
- Causal discovery in multiple models from different experiments
- Challenges of Data Visualization
- Charting Chemical Space: Challenges and Opportunities for AI and Machine Learning
- Coarse-to-Fine Learning and Inference
- Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
- Computational Social Science and the Wisdom of Crowds
- Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning
- Constructing Skill Trees for Reinforcement Learning Agents from Demonstration Trajectories
- Construction of Dependent Dirichlet Processes based on Poisson Processes
- Convex Multiple-Instance Learning by Estimating Likelihood Ratio
- Copula Bayesian Networks
- Copula Processes
- Co-regularization Based Semi-supervised Domain Adaptation
- Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings
- CUR from a Sparse Optimization Viewpoint
- Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
- Decision Making with Multiple Imperfect Decision Makers
- Decoding Ipsilateral Finger Movements from ECoG Signals in Humans
- Decomposing Isotonic Regression for Efficiently Solving Large Problems
- Deep Coding Network
- Deep Learning and Unsupervised Feature Learning
- Deterministic Single-Pass Algorithm for LDA
- Direct Loss Minimization for Structured Prediction
- Discrete Optimization in Machine Learning: Structures, Algorithms and Applications
- Discriminative Clustering by Regularized Information Maximization
- Distributed Dual Averaging In Networks
- Distributionally Robust Markov Decision Processes
- Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform
- Double Q-learning
- Dynamic Infinite Relational Model for Time-varying Relational Data Analysis
- Effects of Synaptic Weight Diffusion on Learning in Decision Making Networks
- Efficient algorithms for learning kernels from multiple similarity matrices with general convex loss functions
- Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
- Efficient Minimization of Decomposable Submodular Functions
- Efficient Optimization for Discriminative Latent Class Models
- Efficient Relational Learning with Hidden Variable Detection
- Empirical Bernstein Inequalities for U-Statistics
- Empirical Risk Minimization with Approximations of Probabilistic Grammars
- Energy Disaggregation via Discriminative Sparse Coding
- Epitome driven 3-D Diffusion Tensor image segmentation: on extracting specific structures
- Error Propagation for Approximate Policy and Value Iteration
- Estimating Spatial Layout of Rooms using Volumetric Reasoning about Objects and Surfaces
- Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs
- Evaluating neuronal codes for inference using Fisher information
- Evaluation of Rarity of Fingerprints in Forensics
- Evidence-Specific Structures for Rich Tractable CRFs
- Exact inference and learning for cumulative distribution functions on loopy graphs
- Exact learning curves for Gaussian process regression on large random graphs
- Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach
- Extended Bayesian Information Criteria for Gaussian Graphical Models
- Extensions of Generalized Binary Search to Group Identification and Exponential Costs
- Factorized Latent Spaces with Structured Sparsity
- Fast detection of multiple change-points shared by many signals using group LARS
- Fast global convergence rates of gradient methods for high-dimensional statistical recovery
- Fast Large-scale Mixture Modeling with Component-specific Data Partitions
- Feature Construction for Inverse Reinforcement Learning
- Feature Set Embedding for Incomplete Data
- Feature Transitions with Saccadic Search: Size, Color, and Orientation Are Not Alike
- Fractionally Predictive Spiking Neurons
- Functional form of motion priors in human motion perception
- Functional Geometry Alignment and Localization of Brain Areas
- Gated Softmax Classification
- Gaussian Process Preference Elicitation
- Gaussian sampling by local perturbations
- Generalized roof duality and bisubmodular functions
- Generating more realistic images using gated MRF's
- Generative Local Metric Learning for Nearest Neighbor Classification
- Getting lost in space: Large sample analysis of the resistance distance
- Global Analytic Solution for Variational Bayesian Matrix Factorization
- Global seismic monitoring as probabilistic inference
- Globby: It's a Search Engine with a Sorting View
- Graph-Valued Regression
- Group Sparse Coding with a Laplacian Scale Mixture Prior
- Guaranteed Rank Minimization via Singular Value Projection
- Hallucinations in Charles Bonnet Syndrome Induced by Homeostasis: a Deep Boltzmann Machine Model
- Haptic Information Presentation Through Vibro Tactile
- Hashing Hyperplane Queries to Near Points with Applications to Large-Scale Active Learning
- Heavy-Tailed Process Priors for Selective Shrinkage
- High-dimensional Statistics: Prediction, Association and Causal Inference
- How Does the Brain Compute and Compare Values at the Time of Decision-Making?
- How to Grow a Mind: Statistics, Structure and Abstraction
- Humans Learn Using Manifolds, Reluctantly
- Identifying Dendritic Processing
- Identifying graph-structured activation patterns in networks
- Identifying Patients at Risk of Major Adverse Cardiovascular Events Using Symbolic Mismatch
- Implicit Differentiation by Perturbation
- Implicit encoding of prior probabilities in optimal neural populations
- Implicitly Constrained Gaussian Process Regression for Monocular Non-Rigid Pose Estimation
- Improvements to the Sequence Memoizer
- Improving Human Judgments by Decontaminating Sequential Dependencies
- Improving the Asymptotic Performance of Markov Chain Monte-Carlo by Inserting Vortices
- Individualized ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles
- Inductive Regularized Learning of Kernel Functions
- Inference and communication in the game of Password
- Inference with Multivariate Heavy-Tails in Linear Models
- Inferring Stimulus Selectivity from the Spatial Structure of Neural Network Dynamics
- Infinite Relational Modeling of Functional Connectivity in Resting State fMRI
- Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks
- Interval Estimation for Reinforcement-Learning Algorithms in Continuous-State Domains
- Joint Analysis of Time-Evolving Binary Matrices and Associated Documents
- Joint Cascade Optimization Using A Product Of Boosted Classifiers
- Kernel Descriptors for Visual Recognition
- Label Embedding Trees for Large Multi-Class Tasks
- Large Margin Learning of Upstream Scene Understanding Models
- Large Margin Multi-Task Metric Learning
- Large-Scale Matrix Factorization with Missing Data under Additional Constraints
- Latent Factor Models for Relational Arrays and Network Data
- Latent Variable Models for Predicting File Dependencies in Large-Scale Software Development
- Layered image motion with explicit occlusions, temporal consistency, and depth ordering
- Layer-wise analysis of deep networks with Gaussian kernels
- Learning and Planning from Batch Time Series Data
- Learning Bounds for Importance Weighting
- Learning concept graphs from text with stick-breaking priors
- Learning Convolutional Feature Hierarchies for Visual Recognition
- Learning Efficient Markov Networks
- Learning from Candidate Labeling Sets
- Learning from Logged Implicit Exploration Data
- Learning invariant features using the Transformed Indian Buffet Process
- Learning Kernels with Radiuses of Minimum Enclosing Balls
- Learning Multiple Tasks using Manifold Regularization
- Learning Multiple Tasks with a Sparse Matrix-Normal Penalty
- Learning Networks of Stochastic Differential Equations
- Learning on Cores, Clusters, and Clouds
- Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors
- Learning the context of a category
- Learning to combine foveal glimpses with a third-order Boltzmann machine
- Learning To Count Objects in Images
- Learning to localise sounds with spiking neural networks
- Learning via Gaussian Herding
- Lifted Inference Seen from the Other Side : The Tractable Features
- Linear Complementarity for Regularized Policy Evaluation and Improvement
- Linear readout from a neural population with partial correlation data
- Link Discovery using Graph Feature Tracking
- Lower Bounds on Rate of Convergence of Cutting Plane Methods
- Low-rank Methods for Large-scale Machine Learning
- LSTD with Random Projections
- Machine Learning for Assistive Technologies
- Machine Learning for Social Computing
- Machine Learning in Computational Biology
- Machine Learning in Online Advertising
- Machine Learning meets Computational Photography
- Machine Learning with Human Intelligence: Principled Corner Cutting (PC2)
- MAP Estimation for Graphical Models by Likelihood Maximization
- MAP estimation in Binary MRFs via Bipartite Multi-cuts
- MetaOptimize: A Q+A site for machine learning
- Minimum Average Cost Clustering
- Mixture of time-warped trajectory models for movement decoding
- mldata.org - machine learning data and benchmark
- Modeling Human Communication Dynamics
- Monte Carlo Methods for Bayesian Inference in Modern Day Applications
- Monte-Carlo Planning in Large POMDPs
- Moreau-Yosida Regularization for Grouped Tree Structure Learning
- More data means less inference: A pseudo-max approach to structured learning
- Movement extraction by detecting dynamics switches and repetitions
- Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition
- Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers
- Multiple Kernel Learning and the SMO Algorithm
- Multi-Stage Dantzig Selector
- Multitask Learning without Label Correspondences
- Multivariate Dyadic Regression Trees for Sparse Learning Problems
- Multi-View Active Learning in the Non-Realizable Case
- Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks
- Near-Optimal Bayesian Active Learning with Noisy Observations
- Network Flow Algorithms for Structured Sparsity
- Networks Across Disciplines: Theory and Applications
- NeuFlow: a dataflow processor for convolutional nets and other real-time algorithms
- New Adaptive Algorithms for Online Classification
- New Directions in Multiple Kernel Learning
- Nonparametric Bayesian Policy Priors for Reinforcement Learning
- Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable
- Non-Stochastic Bandit Slate Problems
- Numerical Mathematics Challenges in Machine Learning
- Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
- Occlusion Detection and Motion Estimation with Convex Optimization
- On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient
- On Herding and the Perceptron Cycling Theorem
- Online Classification with Specificity Constraints
- Online Learning for Latent Dirichlet Allocation
- Online Learning in The Manifold of Low-Rank Matrices
- Online Learning: Random Averages, Combinatorial Parameters, and Learnability
- Online Markov Decision Processes under Bandit Feedback
- On the Convexity of Latent Social Network Inference
- On the Theory of Learnining with Privileged Information
- Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets
- Optimal learning rates for Kernel Conjugate Gradient regression
- Optimal Web-Scale Tiering as a Flow Problem
- Optimization Algorithms in Machine Learning
- Optimization for Machine Learning
- Over-complete representations on recurrent neural networks can support persistent percepts
- PAC-Bayesian Model Selection for Reinforcement Learning
- Parallelized Stochastic Gradient Descent
- Parametric Bandits: The Generalized Linear Case
- Penalized Principal Component Regression on Graphs for Analysis of Subnetworks
- Perceptual Bases for Rules of Thumb in Photography
- Permutation Complexity Bound on Out-Sample Error
- Phoneme Recognition with Large Hierarchical Reservoirs
- Phone Recognition with the Mean-Covariance Restricted Boltzmann Machine
- Platform to Share Feature Extraction Methods
- Policy gradients in linearly-solvable MDPs
- Pose-Sensitive Embedding by Nonlinear NCA Regression
- Practical Application of Sparse Modeling: Open Issues and New Directions
- Practical Large-Scale Optimization for Max-norm Regularization
- Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
- Predictive Models in Personalized Medicine
- Predictive State Temporal Difference Learning
- Predictive Subspace Learning for Multi-view Data: a Large Margin Approach
- Probabilistic Belief Revision with Structural Constraints
- Probabilistic Deterministic Infinite Automata
- Probabilistic Inference and Differential Privacy
- Probabilistic latent variable models for distinguishing between cause and effect
- Probabilistic Multi-Task Feature Selection
- Project Emporia: News Recommendation using Graphical Models
- Random Conic Pursuit for Semidefinite Programming
- Random Projections for $k$-means Clustering
- Random Projection Trees Revisited
- Random Walk Approach to Regret Minimization
- Rates of convergence for the cluster tree
- Regularized estimation of image statistics by Score Matching
- Reinforcement Learning for Embodied Cognition
- Reinforcement Learning in Humans and Other Animals
- Relaxed Clipping: A Global Training Method for Robust Regression and Classification
- Repeated Games against Budgeted Adversaries
- Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models
- Reverse Multi-Label Learning
- Reward Design via Online Gradient Ascent
- (RF)^2 -- Random Forest Random Field
- Robust Clustering as Ensembles of Affinity Relations
- Robust PCA via Outlier Pursuit
- Robust Statistical Learning
- Sample Complexity of Testing the Manifold Hypothesis
- Scrambled Objects for Least-Squares Regression
- Segmentation as Maximum-Weight Independent Set
- Self-Paced Learning for Latent Variable Models
- Semi-Supervised Learning with Adversarially Missing Label Information
- Shadow Dirichlet for Restricted Probability Modeling
- Short-term memory in neuronal networks through dynamical compressed sensing
- Sidestepping Intractable Inference with Structured Ensemble Cascades
- Simultaneous Object Detection and Ranking with Weak Supervision
- Size Matters: Metric Visual Search Constraints from Monocular Metadata
- Slice sampling covariance hyperparameters of latent Gaussian models
- Smoothness, Low Noise and Fast Rates
- Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models
- Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake
- Sparse Coding for Learning Interpretable Spatio-Temporal Primitives
- Sparse Instrumental Variables (SPIV) for Genome-Wide Studies
- Sparse Inverse Covariance Selection via Alternating Linearization Methods
- Spatial and anatomical regularization of SVM for brain image analysis
- Spectral Regularization for Support Estimation
- Sphere Embedding: An Application to Part-of-Speech Induction
- SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system
- Spike timing-dependent plasticity as dynamic filter
- Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models
- Static Analysis of Binary Executables Using Structural SVMs
- Statistical Inference of Protein Structure and Function
- Stochastic Matlab
- Structural epitome: a way to summarize one’s visual experience
- Structured Determinantal Point Processes
- Structured sparsity-inducing norms through submodular functions
- Subgraph Detection Using Eigenvector L1 Norms
- Sufficient Conditions for Generating Group Level Sparsity in a Robust Minimax Framework
- Supervised Clustering
- Switched Latent Force Models for Movement Segmentation
- Switching state space model for simultaneously estimating state transitions and nonstationary firing rates
- Synergies in learning words and their referents
- Tensors, Kernels, and Machine Learning
- The Interplay of Machine Learning and Mechanism Design
- The LASSO risk: asymptotic results and real world examples
- The Maximal Causes of Natural Scenes are Edge Filters
- The Multidimensional Wisdom of Crowds
- The Neural Costs of Optimal Control
- The SHOGUN Machine Learning Toolbox
- Throttling Poisson Processes
- Tight Sample Complexity of Large-Margin Learning
- Tiled convolutional neural networks
- t-logistic regression
- Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models
- Towards Property-Based Classification of Clustering Paradigms
- Trading off Mistakes and Don't-Know Predictions
- Transduction with Matrix Completion: Three Birds with One Stone
- Transfer Learning Via Rich Generative Models.
- Tree-Structured Stick Breaking for Hierarchical Data
- Two-Layer Generalization Analysis for Ranking Using Rademacher Average
- Universal Consistency of Multi-Class Support Vector Classification
- Universal Kernels on Non-Standard Input Spaces
- Unsupervised Kernel Dimension Reduction
- Using body-anchored priors for identifying actions in single images
- Variable margin losses for classifier design
- Variational bounds for mixed-data factor analysis
- Variational Inference over Combinatorial Spaces
- Vision-Based Control, Control-Based Vision, and the Information Knot That Ties Them
- Visual Object Recognition with NN Convolutional & Spiking; Test on Traffic Signs
- Why are some word orders more common than others? A uniform information density account
- Word Features for Latent Dirichlet Allocation
- Worst-case bounds on the quality of max-product fixed-points
- Worst-Case Linear Discriminant Analysis