# Downloads 2008

Number of events: 303

- A Bayesian Approach for Extracting State Transition Dynamics from Multiple Spike Trains
- Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes
- A computational model of hippocampal function in trace conditioning
- A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approxi
- A Convex Upper Bound on the Log-Partition Function
- Adapting to a Market Shock: Optimal Sequential Market-Making
- Adaptive Forward-Backward Greedy Algorithm for Sparse Learning with Linear Models
- Adaptive Martingale Boosting
- Adaptive Template Matching with Shift-Invariant Semi-NMF
- A general framework for investigating how far the decoding process in the brain can be simplified
- Agnostic Learning: Algorithms and Theory
- A Hierarchical Image Model for Polynomial-Time 2D Parsing
- Algebraic and combinatorial methods in machine learning
- Algorithms for Infinitely Many-Armed Bandits
- A Massively Parallel Digital Learning Processor
- A mixture model for the evolution of gene expression in non-homogeneous datasets
- An Algorithm for Microchip Spike Sorting
- Analyzing Graphs: Theory and Applications
- Analyzing human feature learning as nonparametric Bayesian inference
- Analyzing the Monotonic Feature Abstraction for Text Classification
- An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering
- An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis
- An Extended Level Method for Efficient Multiple Kernel Learning
- An Homotopy Algorithm for the Lasso with Online Observations
- An ideal observer model of infant object perception
- An improved estimator of Variance Explained in the presence of noise
- An interior-point stochastic approximation method and an L1-regularized delta rule
- An Online Algorithm for Maximizing Submodular Functions
- A Pencil Balancing Robot Using Only Spike-based Visual Input
- Approximate inference - how far have we come?
- A rational model of preference learning and choice prediction by children
- Artificial Olfactory Brain for Mixture Identification
- A Scalable Hierarchical Distributed Language Model
- A "Shape Aware" Model for semi-supervised Learning of Objects and its Context
- A spatially varying two-sample recombinant coalescent, with applications to HIV escape response
- Asynchronous Distributed Learning of Topic Models
- A Transductive Bound for the Voted Classifier with an Application to Semi-supervised Learning
- Automatic online tuning for fast Gaussian summation
- Bayesian Experimental Design of Magnetic Resonance Imaging Sequences
- Bayesian Exponential Family PCA
- Bayesian Kernel Shaping for Learning Control
- Bayesian Model of Behaviour in Economic Games
- Bayesian Network Score Approximation using a Metagraph Kernel
- Bayesian Synchronous Grammar Induction
- Beyond Novelty Detection: Incongruent Events, when General and Specific Classifiers Disagree
- Beyond Search: Computational Intelligence for the Web (day 1)
- Beyond Search: Computational Intelligence for the Web (day 2)
- Biasing Approximate Dynamic Programming with a Lower Discount Factor
- Bio-inspired Real Time Sensory Map Realignment in a Robotic Barn Owl
- Bounding Performance Loss in Approximate MDP Homomorphisms
- Bounds on marginal probability distributions
- Breaking Audio CAPTCHAs with Machine Learning Techniques
- Cascaded Classification Models: Combining Models for Holistic Scene Understanding
- Causality: objectives and assessment
- Cell Assemblies in Large Sparse Inhibitory Networks of Biologically Realistic Spiking Neurons
- Characteristic Kernels on Groups and Semigroups
- Characterizing neural dependencies with Poisson copula models
- Clustered Multi-Task Learning: A Convex Formulation
- Clustering via LP-based Stabilities
- Clusters and Coarse Partitions in LP Relaxations
- Comparing model predictions of response bias and variance in cue combination
- Competing RBM density models for classification of fMRI images
- Compressed Sensing
- Computations in Human Sensorimotor Control
- Connectome: the quest to deconstruct the brain
- Continuously-adaptive discretization for message-passing algorithms
- Convergence and Rate of Convergence of A Manifold-Based Dimension Reduction
- Correlated Bigram LSA for Unsupervised Language Model Adaptation
- Cortical Microcircuits and their Computational Functions
- Cost Sensitive Learning
- Counting Solution Clusters Using Belief Propagation
- Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform
- Cyclizing Clusters via Zeta Function of a Graph
- Deep Learning with Kernel Regularization for Visual Recognition
- Deflation Methods for Sparse PCA
- Demonstrations
- Demonstrations
- Dependence of Orientation Tuning on Recurrent Excitation and Inhibition in a Network Model of V1
- Dependent Dirichlet Process Spike Sorting
- Depression: an RL formulation and a behavioural test
- Designing neurophysiology experiments to optimally constrain receptive field models along parametric
- Diffeomorphic Dimensionality Reduction
- Dimensionality Reduction for Data in Multiple Feature Representations
- DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification
- Domain Adaptation with Multiple Sources
- Dynamic Visual Attention: Searching for coding length increments
- Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance
- Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection
- Efficient Exact Inference in Planar Ising Models
- Efficient Inference in Phylogenetic InDel Trees
- Efficient Sampling for Gaussian Process Inference using Control Variables
- Empirical performance maximization for linear rank statistics
- Estimating Robust Query Models with Convex Optimization
- Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG
- Estimating vector fields using sparse basis field expansions
- Estimation of Information Theoretic Measures for Continuous Random Variables
- Evaluating probabilities under high-dimensional latent variable models
- Exact Convex Confidence-Weighted Learning
- Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning
- Extended Grassmann Kernels for Subspace-Based Learning
- Fast Computation of Posterior Mode in Multi-Level Hierarchical Models
- Fast High-dimensional Kernel Summations Using the Monte Carlo Multipole Method
- Fast Prediction on a Tree
- Fast Rates for Regularized Objectives
- Finding Latent Causes in Causal Networks: an Efficient Approach Based on Markov Blankets
- Fitted Q-iteration by Advantage Weighted Regression
- From Online to Batch Learning with Cutoff-Averaging
- Gates
- Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity
- Generative and Discriminative Learning with Unknown Labeling Bias
- Global Ranking Using Continuous Conditional Random Fields
- Goal-directed decision making in prefrontal cortex: a computational framework
- Grouping Contours Via a Related Image
- Hebbian Learning of Bayes Optimal Decisions
- Hierarchical Conditional Random Fields for Recursive Sequential Data
- Hierarchical Fisher Kernels for Longitudinal Data
- High-Accuracy 3D Sensing for Mobile Manipulators
- High Dimensional Motion Planning for Dynamic Robot Locomotion
- High-dimensional union support recovery in multivariate regression
- How memory biases affect information transmission: A rational analysis of serial reproduction
- Human Active Learning
- ICA based on a Smooth Estimation of the Differential Entropy
- Implicit Mixtures of Restricted Boltzmann Machines
- Improved Moves for Truncated Convex Models
- Improving on Expectation Propagation
- Infer.NET: Software for Graphical Models
- Inferring rankings under constrained sensing
- Influence of graph construction on graph-based clustering measures
- Integrating Locally Learned Causal Structures with Overlapping Variables
- Interpreting the neural code with Formal Concept Analysis
- Kernel-ARMA for Hand Tracking and Brain-Machine interfacing During 3D Motor Control
- Kernel Change-point Analysis
- Kernelized Sorting
- Kernel Learning: Automatic Selection of Optimal Kernels
- Kernel Measures of Independence for non-iid Data
- Large Margin Taxonomy Embedding for Document Categorization
- Learning a discriminative hidden part model for human action recognition
- Learning Bounded Treewidth Bayesian Networks
- Learning from Multiple Sources
- Learning Hybrid Models for Image Annotation with Partially Labeled Data
- Learning Taxonomies by Dependence Maximization
- Learning the Semantic Correlation: An Alternative Way to Gain from Unlabeled Text
- Learning to Use Working Memory in Partially Observable Environments through Dopaminergic Reinforcement
- Learning Transformational Invariants from Time-Varying Natural Images
- Learning with Consistency between Inductive Functions and Kernels
- Linear Classification and Selective Sampling Under Low Noise Conditions
- Load and Attentional Bayes
- Local Gaussian Process Regression for Real Time Online Model Learning
- Localized Sliced Inverse Regression
- LOOPS: Localizing Object Outlines using Probabilistic Shape
- Machine Learning in Computational Biology
- Machine Learning in High Energy Physics
- Machine learning meets human learning
- Machine Learning Open Source Software
- Machine Perception for Human Machine Interaction
- MAS: a multiplicative approximation scheme for probabilistic inference
- MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features
- MDPs with Non-Deterministic Policies
- Measures of Clustering Quality: A Working Set of Axioms for Clustering
- Mind the Duality Gap: Logarithmic regret algorithms for online optimization
- Mixed Membership Stochastic Blockmodels
- MLPACK: Scalable Machine Learning Software
- Modeling human function learning with Gaussian processes
- Modeling Short-term Noise Dependence of Spike Counts in Macaque Prefrontal Cortex
- Modeling the effects of memory on human online sentence processing with particle filters
- Model selection and velocity estimation using novel priors for motion patterns
- Model Selection in Gaussian Graphical Models: High-Dimensional Consistency of \ell_1-regularizedMLE
- Model Uncertainty and Risk in Reinforcement Learning
- Mortal Multi-Armed Bandits
- Multi-Agent Filtering with Infinitely Nested Beliefs
- Multi-label Multiple Kernel Learning
- Multi-Level Active Prediction of Useful Image Annotations for Recognition
- Multi-resolution Exploration in Continuous Spaces
- Multiscale Random Fields with Application to Contour Grouping
- Multi-stage Convex Relaxation for Learning with Sparse Regularization
- Multi-task Gaussian Process Learning of Robot Inverse Dynamics
- Natural Image Denoising with Convolutional Networks
- Near-minimax recursive density estimation on the binary hypercube
- Near-optimal Regret Bounds for Reinforcement Learning
- New Challanges in Theoretical Machine Learning: Data Dependent Concept Spaces
- New Directions in Statistical Learning for Meaningful and Reproducible fMRI Analysis
- Nonlinear causal discovery with additive noise models
- Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
- Nonparametric Bayesian Sparse Hierarchical Factor Modeling and Regression
- Nonparametric regression and classification with joint sparsity constraints
- Non-parametric Regression Between Manifolds
- Nonparametric sparse hierarchical models describe V1 fMRI responses to natural images
- Nonrigid Structure from Motion in Trajectory Space
- Non-stationary dynamic Bayesian networks
- (Not) Sparse Coding
- Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks
- On Bootstrapping the ROC Curve
- On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing
- One sketch for all: Theory and Application of Conditional Random Sampling
- Online Metric Learning and Fast Similarity Search
- Online Models for Content Optimization
- Online Optimization in X-Armed Bandits
- On-Line Prediction on Large Diameter Graphs
- Online Stochastic Combinatorial Optimization
- On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
- On the Design of Loss Functions for Classification: theory, robustness to outliers, and SavageBoost
- On the Efficient Minimization of Classification Calibrated Surrogates
- On the equivalence between TD learning and differential Hebbian learning using a local third factor
- On the Generalization Ability of Online Strongly Convex Programming Algorithms
- On the Reliability of Clustering Stability in the Large Sample Regime
- Optimal Response Initiation: Why Recent Experience Matters
- Optimization for Machine Learning
- Optimization on a Budget: A Reinforcement Learning Approach
- Overlaying classifiers: a practical approach for optimal ranking
- Parallel Implementations of Learning Algorithms: What have you done for me lately?
- Parallel Implementations of Learning Algorithms: What have you done for me lately?
- Partially Observed Maximum Entropy Discrimination Markov Networks
- Particle Filter-based Policy Gradient in POMDPs
- Performance analysis for L_2 kernel classification
- Phase transitions for high-dimensional joint support recovery
- Play Brain-Pong in 10 Minutes
- Playing Pinball with non-invasive BCI
- Policy Search for Motor Primitives in Robotics
- Posterior Consistency of the Silverman g-prior in Bayesian Model Choice
- Predicting the Geometry of Metal Binding Sites from Protein Sequence
- Predictive Indexing for Fast Search
- Prequential Analysis
- Principled Theoretical Frameworks for the Perception-Action Cycle
- Privacy-preserving logistic regression
- Probabilistic detection of short events, with application to critical care monitoring
- Probabilistic Programming: Universal Languages, Systems and Applications
- PSDBoost: Matrix-Generation Linear Programming for Positive Semidefinite Matrices Learning
- QUIC-SVD: Fast SVD Using Cosine Trees
- Rademacher Complexity Bounds for Non-I.I.D. Processes
- Real-time Topology Learning
- Reconciling Real Scores with Binary Comparisons: A Unified Logistic Model for Ranking
- Reducing statistical dependencies in natural signals using radial Gaussianization
- Regularized Co-Clustering with Dual Supervision
- Regularized Learning with Networks of Features
- Regularized Policy Iteration
- Relative Margin Machines
- Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation
- Resolution Limits of Sparse Coding in High Dimensions
- Risk Bounds for Randomized Sample Compressed Classifiers
- RL-Glue: From Grid Worlds to Sensor Rich Robots
- Robust Kernel Principal Component Analysis
- Robust Near-Isometric Matching via Structured Learning of Graphical Models
- Robust Regression and Lasso
- Scalable Algorithms for String Kernels with Inexact Matching
- SDL: Supervised Dictionary Learning
- Self-organization using dynamical synapses
- Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization
- Sequential effects: Superstition or rational behavior?
- Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
- Short-Term Depression in VLSI Stochastic Synapse
- Signal-to-Noise Ratio Analysis of Policy Gradient Algorithms
- Simple Local Models for Complex Dynamical Systems
- Skill Characterization Based on Betweenness
- Sparse Convolved Gaussian Processes for Multi-ouptut Regression
- Sparse Online Learning via Truncated Gradient
- Sparse probabilistic projections
- Sparse Signal Recovery Using Markov Random Fields
- Sparsity of SVMs that use the epsilon-insensitive loss
- Spectral Clustering with Perturbed Data
- Spectral Hashing
- Speech and Language: Learning-based Methods and Systems
- Speech and Language: Unsupervised Latent-Variable Models
- Statistical Analysis and Modeling of Response Dependencies in Neural Populations (day 1)
- Statistical Analysis and Modeling of Response Dependencies in Neural Populations (day 2)
- Statistical Models of Visual Images
- Stochastic Models of Behaviour
- Stochastic Relational Models for Large-scale Dyadic Data using MCMC
- Stress, noradrenaline, and realistic prediction of mouse behaviour using reinforcement learning
- Structured Input - Structured Output
- Structured ranking learning using cumulative distribution networks
- Structure Learning in Human Sequential Decision-Making
- Supervised Bipartite Graph Inference
- Supervised Exponential Family Principal Component Analysis via Convex Optimizatio
- Suppport Vector Machines with a Reject Option
- Syntactic Topic Models
- Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation
- Temporal Dynamics of Cognitive Control
- The Chiara: A New Robotic Platform for Education and Research
- The Conjoint Effect of Divisive Normalization and Orientation Selectivity on Redundancy Reduction
- The Gaussian Process Density Sampler
- The Infinite Factorial Hidden Markov Model
- The Mondrian Process
- The Neurobiology of Decision Making
- Theory of matching pursuit
- Theory of Mind with fMRI
- The Recurrent Temporal Restricted Boltzmann Machine
- The Smoothed Analysis of Algorithms
- Tighter Bounds for Structured Estimation
- Tracking Changing Stimuli in Continuous Attractor Neural Networks
- Transfer Learning by Distribution Matching for Targeted Advertising
- Translated Learning
- Understanding Brain Connectivity Patterns during Motor Imagery for Brain-Computer Interfacing
- Unifying the Sensory and Motor Components of Sensorimotor Adaptation
- Unlabeled data: Now it helps, now it doesn't
- Unsupervised Bayesian Parameter Estimation for Probabilistic Grammars
- Unsupervised Learning of Visual Sense Models for Polysemous Words
- Using Bayesian Dynamical Systems for Motion Template Libraries
- Using matrices to model symbolic relationship
- Variational Mixture of Gaussian Process Experts
- Vision Toolkit Based on Hierarchical Temporal Memory
- Visualizing NIPS Cooperations using Multiple Maps t-SNE
- Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning
- Worio: A Web-Scale Machine Learning System