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Learning Label Embeddings for Nearest-Neighbor Multi-class Classification with an Application to Speech Recognition
Heavy-Tailed Symmetric Stochastic Neighbor Embedding
A Fast, Consistent Kernel Two-Sample Test
On Invariance in Hierarchical Models
Maximin affinity learning of image segmentation
Semi-supervised Learning using Sparse Eigenfunction Bases
On the Convergence of the Concave-Convex Procedure
Bilinear classifiers for visual recognition
Factor Modeling for Advertisement Targeting
AUC optimization and the two-sample problem
Bayesian estimation of orientation preference maps
Kernels and learning curves for Gaussian process regression on random graphs
Distribution-Calibrated Hierarchical Classification
Fast, smooth and adaptive regression in metric spaces
A Smoothed Approximate Linear Program
Modeling the spacing effect in sequential category learning
Accelerated Gradient Methods for Stochastic Optimization and Online Learning
Perceptual Multistability as Markov Chain Monte Carlo Inference
Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model
Label Selection on Graphs
A Neural Implementation of the Kalman Filter
Heterogeneous multitask learning with joint sparsity constraints
Adapting to the Shifting Intent of Search Queries
Sparse Estimation Using General Likelihoods and Non-Factorial Priors
Slow Learners are Fast
Orthogonal Matching Pursuit From Noisy Random Measurements: A New Analysis
Complexity of Decentralized Control: Special Cases
Slow, Decorrelated Features for Pretraining Complex Cell-like Networks
Efficient Match Kernel between Sets of Features for Visual Recognition
Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity
Rethinking LDA: Why Priors Matter
Conditional Neural Fields
Nonparametric Latent Feature Models for Link Prediction
Robust Nonparametric Regression with Metric-Space Valued Output
Riffled Independence for Ranked Data
Segmenting Scenes by Matching Image Composites
A Generalized Natural Actor-Critic Algorithm
Learning to Hash with Binary Reconstructive Embeddings
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations
Learning models of object structure
Lattice Regression
Compressed Least-Squares Regression
Modelling Relational Data using Bayesian Clustered Tensor Factorization
Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference
Replicated Softmax: an Undirected Topic Model
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation
Convex Relaxation of Mixture Regression with Efficient Algorithms
Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions
Efficient and Accurate Lp-Norm Multiple Kernel Learning
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction
Learning a Small Mixture of Trees
3D Object Recognition with Deep Belief Nets
A Game-Theoretic Approach to Hypergraph Clustering
Strategy Grafting in Extensive Games
Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction
Polynomial Semantic Indexing
Group Sparse Coding
Approximating MAP by Compensating for Structural Relaxations
Reconstruction of Sparse Circuits Using Multi-neuronal Excitation (RESCUME)
Time-Varying Dynamic Bayesian Networks
Learning in Markov Random Fields using Tempered Transitions
Matrix Completion from Power-Law Distributed Samples
Maximum likelihood trajectories for continuous-time Markov chains
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process
Which graphical models are difficult to learn?
FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs
Canonical Time Warping for Alignment of Human Behavior
Particle-based Variational Inference for Continuous Systems
Nonlinear Learning using Local Coordinate Coding
Kernel Methods for Deep Learning
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
Skill Discovery in Continuous Reinforcement Learning Domains using Skill Chaining
Optimal Scoring for Unsupervised Learning
Estimating image bases for visual image reconstruction from human brain activity
A Biologically Plausible Model for Rapid Natural Scene Identification
Adaptive Design Optimization in Experiments with People
Tracking Dynamic Sources of Malicious Activity at Internet Scale
An LP View of the M-best MAP problem
Asymptotically Optimal Regularization in Smooth Parametric Models
Clustering sequence sets for motif discovery
Localizing Bugs in Program Executions with Graphical Models
An Online Algorithm for Large Scale Image Similarity Learning
Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies
Differential Use of Implicit Negative Evidence in Generative and Discriminative Language Learning
Fast Graph Laplacian Regularized Kernel Learning via Semidefinite–Quadratic–Linear Programming
Learning Non-Linear Combinations of Kernels
Efficient Moments-based Permutation Tests
Sharing Features among Dynamical Systems with Beta Processes
Nonparametric Greedy Algorithms for the Sparse Learning Problem
Predicting the Optimal Spacing of Study: A Multiscale Context Model of Memory
Discriminative Network Models of Schizophrenia
Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data
Spatial Normalized Gamma Processes
Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization
Statistical Models of Linear and Nonlinear Contextual Interactions in Early Visual Processing
Know Thy Neighbour: A Normative Theory of Synaptic Depression
A Rate Distortion Approach for Semi-Supervised Conditional Random Fields
Variational Gaussian-process factor analysis for modeling spatio-temporal data
Non-stationary continuous dynamic Bayesian networks
STDP enables spiking neurons to detect hidden causes of their inputs
Bayesian Sparse Factor Models and DAGs Inference and Comparison
Adaptive Regularization for Transductive Support Vector Machine
Extending Phase Mechanism to Differential Motion Opponency for Motion Pop-out
Constructing Topological Maps using Markov Random Fields and Loop-Closure Detection
Noisy Generalized Binary Search
Positive Semidefinite Metric Learning with Boosting
Replacing supervised classification learning by Slow Feature Analysis in spiking neural networks
fMRI-Based Inter-Subject Cortical Alignment Using Functional Connectivity
Streaming Pointwise Mutual Information
Bayesian Belief Polarization
Adaptive Regularization of Weight Vectors
Free energy score space
Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models
Region-based Segmentation and Object Detection
The Wisdom of Crowds in the Recollection of Order Information
Statistical Consistency of Top-k Ranking
Multiple Incremental Decremental Learning of Support Vector Machines
Submodularity Cuts and Applications
Anomaly Detection with Score functions based on Nearest Neighbor Graphs
Code-specific policy gradient rules for spiking neurons
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Hierarchical Mixture of Classification Experts Uncovers Interactions between Brain Regions
Distribution Matching for Transduction
Fast subtree kernels on graphs
Indian Buffet Processes with Power-law Behavior
Inter-domain Gaussian Processes for Sparse Inference using Inducing Features
Statistical Analysis of Semi-Supervised Learning: The Limit of Infinite Unlabelled Data
Bayesian Nonparametric Models on Decomposable Graphs
Sparsistent Learning of Varying-coefficient Models with Structural Changes
The Ordered Residual Kernel for Robust Motion Subspace Clustering
The "tree-dependent components" of natural scenes are edge filters
Bootstrapping from Game Tree Search
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units
The Infinite Partially Observable Markov Decision Process
Ensemble Nystrom Method
Linear-time Algorithms for Pairwise Statistical Problems
On Stochastic and Worst-case Models for Investing
A Bayesian Model for Simultaneous Image Clustering, Annotation and Object Segmentation
Nash Equilibria of Static Prediction Games
A Data-Driven Approach to Modeling Choice
Online Learning of Assignments
Beyond Convexity: Online Submodular Minimization
Linearly constrained Bayesian matrix factorization for blind source separation
Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models
An Additive Latent Feature Model for Transparent Object Recognition
Learning from Neighboring Strokes: Combining Appearance and Context for Multi-Domain Sketch Recognition
From PAC-Bayes Bounds to KL Regularization
Rank-Approximate Nearest Neighbor Search: Retaining Meaning and Speed in High Dimensions
Improving Existing Fault Recovery Policies
Measuring Invariances in Deep Networks
Semi-Supervised Learning in Gigantic Image Collections
Speaker Comparison with Inner Product Discriminant Functions
Monte Carlo Sampling for Regret Minimization in Extensive Games
Zero-shot Learning with Semantic Output Codes
Directed Regression
Manifold Regularization for SIR with Rate Root-n Convergence
Matrix Completion from Noisy Entries
Graph Zeta Function in the Bethe Free Energy and Loopy Belief Propagation
Bayesian Source Localization with the Multivariate Laplace Prior
Learning Bregman Distance Functions and Its Application for Semi-Supervised Clustering
Toward Provably Correct Feature Selection in Arbitrary Domains
A Gaussian Tree Approximation for Integer Least-Squares
Fast Image Deconvolution using Hyper-Laplacian Priors
Learning to Rank by Optimizing NDCG Measure
A Bayesian Analysis of Dynamics in Free Recall
Efficient Learning using Forward-Backward Splitting
Efficient Large-Scale Distributed Training of Conditional Maximum Entropy Models
On Learning Rotations
Regularized Distance Metric Learning:Theory and Algorithm
Sparse Metric Learning via Smooth Optimization
Ranking Measures and Loss Functions in Learning to Rank
Potential-Based Agnostic Boosting
Conditional Random Fields with High-Order Features for Sequence Labeling
Human Rademacher Complexity
Modeling Social Annotation Data with Content Relevance using a Topic Model
Sensitivity analysis in HMMs with application to likelihood maximization
Efficient Bregman Range Search
Data-driven calibration of linear estimators with minimal penalties
Multi-Step Dyna Planning for Policy Evaluation and Control
Neurometric function analysis of population codes
Sufficient Conditions for Agnostic Active Learnable
Variational Inference for the Nested Chinese Restaurant Process
Gaussian process regression with Student-t likelihood
Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
Hierarchical Modeling of Local Image Features through $L_p$-Nested Symmetric Distributions
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process
Abstraction and Relational learning
Breaking Boundaries Between Induction Time and Diagnosis Time Active Information Acquisition
$L_1$-Penalized Robust Estimation for a Class of Inverse Problems Arising in Multiview Geometry
Optimal context separation of spiking haptic signals by second-order somatosensory neurons
Quantification and the language of thought
Local Rules for Global MAP: When Do They Work ?
A joint maximum-entropy model for binary neural population patterns and continuous signals
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
Probabilistic Relational PCA
Robust Value Function Approximation Using Bilinear Programming
Boosting with Spatial Regularization
Evaluating multi-class learning strategies in a generative hierarchical framework for object detection
No evidence for active sparsification in the visual cortex
Analysis of SVM with Indefinite Kernels
Exponential Family Graph Matching and Ranking
Reading Tea Leaves: How Humans Interpret Topic Models
Multi-Label Prediction via Compressed Sensing
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
DUOL: A Double Updating Approach for Online Learning
Nonparametric Bayesian Texture Learning and Synthesis
Locality-sensitive binary codes from shift-invariant kernels
Individuation, Identification and Object Discovery
A General Projection Property for Distribution Families
Beyond Categories: The Visual Memex Model for Reasoning About Object Relationships
Structured output regression for detection with partial truncation
Occlusive Components Analysis
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora
Generalization Errors and Learning Curves for Regression with Multi-task Gaussian Processes
Robust Principal Component Analysis: Exact Recovery of Corrupted Low-Rank Matrices via Convex Optimization
A Stochastic approximation method for inference in probabilistic graphical models
Whose Vote Should Count More: Optimal Integration of Labels from Labelers of Unknown Expertise
Optimizing Multi-Class Spatio-Spectral Filters via Bayes Error Estimation for EEG Classification
Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording
Sparse and Locally Constant Gaussian Graphical Models
Unsupervised Feature Selection for the $k$-means Clustering Problem
An Efficient Interior-Point Method for Minimum-Regret Learning in Online Convex Optimization
Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
Discrete MDL Predicts in Total Variation
Compositionality of optimal control laws
Who’s Doing What: Joint Modeling of Names and Verbs for Simultaneous Face and Pose Annotation
Learning transport operators for image manifolds
Structural inference affects depth perception in the context of potential occlusion
Solving Stochastic Games
White Functionals for Anomaly Detection in Dynamical Systems
Multi-Label Prediction via Sparse Infinite CCA
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness
A unified framework for high-dimensional analysis of $M$-estimators with decomposable regularizers
Sequential effects reflect parallel learning of multiple environmental regularities
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
Help or Hinder: Bayesian Models of Social Goal Inference
Posterior vs Parameter Sparsity in Latent Variable Models
Submanifold density estimation
Measuring model complexity with the prior predictive
Filtering Abstract Senses From Image Search Results
Accelerating Bayesian Structural Inference for Non-Decomposable Gaussian Graphical Models
Unsupervised feature learning for audio classification using convolutional deep belief networks
A Sparse Non-Parametric Approach for Single Channel Separation of Known Sounds
Periodic Step Size Adaptation for Single Pass On-line Learning
An Infinite Factor Model Hierarchy Via a Noisy-Or Mechanism
Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation
A Parameter-free Hedging Algorithm
Subject independent EEG-based BCI decoding
Hierarchical Learning of Dimensional Biases in Human Categorization
Streaming k-means approximation
Exploring Functional Connectivities of the Human Brain using Multivariate Information Analysis
Efficient Recovery of Jointly Sparse Vectors
Fast Learning from Non-i.i.d. Observations
Nonlinear directed acyclic structure learning with weakly additive noise models
Dual Averaging Method for Regularized Stochastic Learning and Online Optimization
Entropic Graph Regularization in Non-Parametric Semi-Supervised Classification
Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs
Learning to Explore and Exploit in POMDPs
Speeding up Magnetic Resonance Image Acquisition by Bayesian Multi-Slice Adaptive Compressed Sensing
An Integer Projected Fixed Point Method for Graph Matching and MAP Inference
Information-theoretic lower bounds on the oracle complexity of convex optimization
Asymptotic Analysis of MAP Estimation via the Replica Method and Compressed Sensing
Learning with Compressible Priors
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