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Fast Kernel Learning for Multidimensional Pattern Extrapolation
Consistent Binary Classification with Generalized Performance Metrics
Combinatorial Pure Exploration of Multi-Armed Bandits
From Stochastic Mixability to Fast Rates
Dynamic Rank Factor Model for Text Streams
Clustered factor analysis of multineuronal spike data
Feature Cross-Substitution in Adversarial Classification
Submodular Attribute Selection for Action Recognition in Video
Sensory Integration and Density Estimation
Deep Joint Task Learning for Generic Object Extraction
Improved Multimodal Deep Learning with Variation of Information
Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
Learning Multiple Tasks in Parallel with a Shared Annotator
Analysis of Brain States from Multi-Region LFP Time-Series
A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System
New Rules for Domain Independent Lifted MAP Inference
Communication Efficient Distributed Machine Learning with the Parameter Server
Scalable Inference for Neuronal Connectivity from Calcium Imaging
Provable Submodular Minimization using Wolfe's Algorithm
(Almost) No Label No Cry
Online combinatorial optimization with stochastic decision sets and adversarial losses
The Infinite Mixture of Infinite Gaussian Mixtures
Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
Graph Clustering With Missing Data: Convex Algorithms and Analysis
Fast Prediction for Large-Scale Kernel Machines
Sparse PCA via Covariance Thresholding
Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings
Object Localization based on Structural SVM using Privileged Information
Design Principles of the Hippocampal Cognitive Map
Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
Clamping Variables and Approximate Inference
Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics
Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
Coresets for k-Segmentation of Streaming Data
large scale canonical correlation analysis with iterative least squares
Time--Data Tradeoffs by Aggressive Smoothing
Unsupervised Deep Haar Scattering on Graphs
Top Rank Optimization in Linear Time
Structure learning of antiferromagnetic Ising models
The Large Margin Mechanism for Differentially Private Maximization
Zeta Hull Pursuits: Learning Nonconvex Data Hulls
The Blinded Bandit: Learning with Adaptive Feedback
Zero-shot recognition with unreliable attributes
Testing Unfaithful Gaussian Graphical Models
Universal Option Models
Variational Gaussian Process State-Space Models
Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
Stochastic Network Design in Bidirected Trees
Weighted importance sampling for off-policy learning with linear function approximation
Tight Continuous Relaxation of the Balanced k-Cut Problem
Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards
Smoothed Gradients for Stochastic Variational Inference
Stochastic variational inference for hidden Markov models
Weakly-supervised Discovery of Visual Pattern Configurations
Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers
Subspace Embeddings for the Polynomial Kernel
On the Convergence Rate of Decomposable Submodular Function Minimization
Stochastic Proximal Gradient Descent with Acceleration Techniques
Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
Spectral Methods for Indian Buffet Process Inference
Tight convex relaxations for sparse matrix factorization
Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Spectral Methods for Supervised Topic Models
Spectral Clustering of graphs with the Bethe Hessian
Spectral k-Support Norm Regularization
Sparse Multi-Task Reinforcement Learning
Spectral Learning of Mixture of Hidden Markov Models
Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
Sparse Bayesian structure learning with dependent relevance determination prior
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space
Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
Streaming, Memory Limited Algorithms for Community Detection
Simple MAP Inference via Low-Rank Relaxations
Shaping Social Activity by Incentivizing Users
Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
Deep Convolutional Neural Network for Image Deconvolution
SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
SerialRank: Spectral Ranking using Seriation
Scale Adaptive Blind Deblurring
Self-Paced Learning with Diversity
Shape and Illumination from Shading using the Generic Viewpoint Assumption
Scalable Kernel Methods via Doubly Stochastic Gradients
Rounding-based Moves for Metric Labeling
Robust Tensor Decomposition with Gross Corruption
Sequential Monte Carlo for Graphical Models
Scaling-up Importance Sampling for Markov Logic Networks
Self-Adaptable Templates for Feature Coding
Restricted Boltzmann machines modeling human choice
Real-Time Decoding of an Integrate and Fire Encoder
Robust Bayesian Max-Margin Clustering
Repeated Contextual Auctions with Strategic Buyers
Recursive Inversion Models for Permutations
Probabilistic Differential Dynamic Programming
Quantized Kernel Learning for Feature Matching
Reputation-based Worker Filtering in Crowdsourcing
Scalable Non-linear Learning with Adaptive Polynomial Expansions
Predicting Useful Neighborhoods for Lazy Local Learning
Provable Tensor Factorization with Missing Data
Recursive Context Propagation Network for Semantic Scene Labeling
Orbit Regularization
Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
Positive Curvature and Hamiltonian Monte Carlo
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
Projecting Markov Random Field Parameters for Fast Mixing
Ranking via Robust Binary Classification
Pre-training of Recurrent Neural Networks via Linear Autoencoders
Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision
Optimal prior-dependent neural population codes under shared input noise
Partition-wise Linear Models
Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization
Optimal Neural Codes for Control and Estimation
Sparse PCA with Oracle Property
Parallel Direction Method of Multipliers
Robust Logistic Regression and Classification
Optimal rates for k-NN density and mode estimation
Optimistic Planning in Markov Decision Processes Using a Generative Model
On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures
Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
Probabilistic low-rank matrix completion on finite alphabets
Online Decision-Making in General Combinatorial Spaces
Parallel Feature Selection Inspired by Group Testing
On the Number of Linear Regions of Deep Neural Networks
On the Computational Efficiency of Training Neural Networks
PEWA: Patch-based Exponentially Weighted Aggregation for image denoising
Projective dictionary pair learning for pattern classification
PAC-Bayesian AUC classification and scoring
Optimizing F-Measures by Cost-Sensitive Classification
Online Optimization for Max-Norm Regularization
On the relations of LFPs & Neural Spike Trains
On Integrated Clustering and Outlier Detection
On Multiplicative Multitask Feature Learning
On Prior Distributions and Approximate Inference for Structured Variables
On Sparse Gaussian Chain Graph Models
On the Information Theoretic Limits of Learning Ising Models
Parallel Double Greedy Submodular Maximization
Neural Word Embedding as Implicit Matrix Factorization
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
Nonparametric Bayesian inference on multivariate exponential families
Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms
Multi-scale Graphical Models for Spatio-Temporal Processes
Near-optimal sample compression for nearest neighbors
Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks
On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification
Multivariate f-divergence Estimation With Confidence
Multivariate Regression with Calibration
Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
Multi-Class Deep Boosting
Multitask learning meets tensor factorization: task imputation via convex optimization
Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition
Model-based Reinforcement Learning and the Eluder Dimension
Multi-Resolution Cascades for Multiclass Object Detection
Minimax-optimal Inference from Partial Rankings
Multi-Scale Spectral Decomposition of Massive Graphs
Low Rank Approximation Lower Bounds in Row-Update Streams
Multiscale Fields of Patterns
Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
Mondrian Forests: Efficient Online Random Forests
Learning convolution filters for inverse covariance estimation of neural network connectivity
Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells"
Magnitude-sensitive preference formation`
Learning to Optimize via Information-Directed Sampling
Metric Learning for Temporal Sequence Alignment
Message Passing Inference for Large Scale Graphical Models with High Order Potentials
Low-dimensional models of neural population activity in sensory cortical circuits
Learning Time-Varying Coverage Functions
Learning to Search in Branch and Bound Algorithms
Learning Mixed Multinomial Logit Model from Ordinal Data
Low-Rank Time-Frequency Synthesis
Learning the Learning Rate for Prediction with Expert Advice
Learning with Pseudo-Ensembles
Learning with Fredholm Kernels
Mind the Nuisance: Gaussian Process Classification using Privileged Noise
Learning on graphs using Orthonormal Representation is Statistically Consistent
Parallel Sampling of HDPs using Sub-Cluster Splits
Local Linear Convergence of Forward--Backward under Partial Smoothness
Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm
Learning Shuffle Ideals Under Restricted Distributions
Learning a Concept Hierarchy from Multi-labeled Documents
Local Decorrelation For Improved Pedestrian Detection
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
Latent Support Measure Machines for Bag-of-Words Data Classification
Learning Optimal Commitment to Overcome Insecurity
Large-Margin Convex Polytope Machine
Learning Mixtures of Submodular Functions for Image Collection Summarization
Iterative Neural Autoregressive Distribution Estimator NADE-k
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
Learning Chordal Markov Networks by Dynamic Programming
Incremental Local Gaussian Regression
Inferring synaptic conductances from spike trains with a biophysically inspired point process model
Greedy Subspace Clustering
Kernel Mean Estimation via Spectral Filtering
Improved Distributed Principal Component Analysis
Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data
Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit
Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
Hardness of parameter estimation in graphical models
Gibbs-type Indian Buffet Processes
Gaussian Process Volatility Model
General Stochastic Networks for Classification
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Information-based learning by agents in unbounded state spaces
Just-In-Time Learning for Fast and Flexible Inference
Global Sensitivity Analysis for MAP Inference in Graphical Models
General Table Completion using a Bayesian Nonparametric Model
Incremental Clustering: The Case for Extra Clusters
Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities
Generalized Dantzig Selector: Application to the k-support norm
Finding a sparse vector in a subspace: Linear sparsity using alternating directions
Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction
Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion
Feedback Detection for Live Predictors
Extreme bandits
Fast Sampling-Based Inference in Balanced Neuronal Networks
Generalized Unsupervised Manifold Alignment
Fast Training of Pose Detectors in the Fourier Domain
Exponential Concentration of a Density Functional Estimator
Fairness in Multi-Agent Sequential Decision-Making
Flexible Transfer Learning under Support and Model Shift
Factoring Variations in Natural Images with Deep Gaussian Mixture Models
Generative Adversarial Nets
From MAP to Marginals: Variational Inference in Bayesian Submodular Models
Estimation with Norm Regularization
LSDA: Large Scale Detection through Adaptation
Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
Extremal Mechanisms for Local Differential Privacy
Extracting Latent Structure From Multiple Interacting Neural Populations
Expectation-Maximization for Learning Determinantal Point Processes
Efficient learning by implicit exploration in bandit problems with side observations
Exact Post Model Selection Inference for Marginal Screening
Efficient Optimization for Average Precision SVM
Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
Efficient Structured Matrix Rank Minimization
Exclusive Feature Learning on Arbitrary Structures via $\ell_{1,2}$-norm
Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights
Do Convnets Learn Correspondence?
Efficient Minimax Signal Detection on Graphs
Do Deep Nets Really Need to be Deep?
Efficient Minimax Strategies for Square Loss Games
Diverse Sequential Subset Selection for Supervised Video Summarization
Elementary Estimators for Graphical Models
Divide-and-Conquer Learning by Anchoring a Conical Hull
Efficient Sampling for Learning Sparse Additive Models in High Dimensions
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis
Deep Learning Face Representation by Joint Identification-Verification
Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
Diverse Randomized Agents Vote to Win
Distributed Bayesian Posterior Sampling via Moment Sharing
Efficient Partial Monitoring with Prior Information
Distributed Parameter Estimation in Probabilistic Graphical Models
Distance-Based Network Recovery under Feature Correlation
Deep Recursive Neural Networks for Compositionality in Language
Distributed Estimation, Information Loss and Exponential Families
Discriminative Metric Learning by Neighborhood Gerrymandering
Distributed Balanced Clustering via Mapping Coresets
Discovering, Learning and Exploiting Relevance
Dimensionality Reduction with Subspace Structure Preservation
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
Discovering Structure in High-Dimensional Data Through Correlation Explanation
Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
Deterministic Symmetric Positive Semidefinite Matrix Completion
Decoupled Variational Gaussian Inference
Controlling privacy in recommender systems
Decomposing Parameter Estimation Problems
Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain
Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
Covariance shrinkage for autocorrelated data
Convex Deep Learning via Normalized Kernels
DFacTo: Distributed Factorization of Tensors
Deep Networks with Internal Selective Attention through Feedback Connections
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Dependent nonparametric trees for dynamic hierarchical clustering
Deep Symmetry Networks
Concavity of reweighted Kikuchi approximation
Constrained convex minimization via model-based excessive gap
Content-based recommendations with Poisson factorization
Cone-Constrained Principal Component Analysis
Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
Computing Nash Equilibria in Generalized Interdependent Security Games
Consistency of weighted majority votes
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
Convex Optimization Procedure for Clustering: Theoretical Revisit
Conditional Swap Regret and Conditional Correlated Equilibrium
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
Causal Inference through a Witness Protection Program
Blossom Tree Graphical Models
Communication-Efficient Distributed Dual Coordinate Ascent
Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs
Global Belief Recursive Neural Networks
Approximating Hierarchical MV-sets for Hierarchical Clustering
Bayesian Sampling Using Stochastic Gradient Thermostats
Bayes-Adaptive Simulation-based Search with Value Function Approximation
Automated Variational Inference for Gaussian Process Models
Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling
Bandit Convex Optimization: Towards Tight Bounds
Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems
Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning
Best-Arm Identification in Linear Bandits
Biclustering Using Message Passing
Attentional Neural Network: Feature Selection Using Cognitive Feedback
An Accelerated Proximal Coordinate Gradient Method
Active Regression by Stratification
Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
Bregman Alternating Direction Method of Multipliers
A statistical model for tensor PCA
Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning
Bayesian Inference for Structured Spike and Slab Priors
Bounded Regret for Finite-Armed Structured Bandits
Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
Algorithms for CVaR Optimization in MDPs
An Autoencoder Approach to Learning Bilingual Word Representations
A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment
A framework for studying synaptic plasticity with neural spike train data
A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
Accelerated Mini-batch Randomized Block Coordinate Descent Method
Active Learning and Best-Response Dynamics
A Safe Screening Rule for Sparse Logistic Regression
Altitude Training: Strong Bounds for Single-Layer Dropout
Algorithm selection by rational metareasoning as a model of human strategy selection
A Unified Semantic Embedding: Relating Taxonomies and Attributes
A provable SVD-based algorithm for learning topics in dominant admixture corpus
Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP
A Representation Theory for Ranking Functions
Analysis of Learning from Positive and Unlabeled Data
A Framework for Testing Identifiability of Bayesian Models of Perception
An Integer Polynomial Programming Based Framework for Lifted MAP Inference
A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process
A Filtering Approach to Stochastic Variational Inference
A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation
Unsupervised learning of an efficient short-term memory network
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
A Dual Algorithm for Olfactory Computation in the Locust Brain
A Boosting Framework on Grounds of Online Learning
Structure Regularization for Structured Prediction
Unsupervised Transcription of Piano Music
A Complete Variational Tracker
A Drifting-Games Analysis for Online Learning and Applications to Boosting
Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
Tree-structured Gaussian Process Approximations
Transportability from Multiple Environments with Limited Experiments: Completeness Results
Two-Stream Convolutional Networks for Action Recognition in Videos
Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
The Noisy Power Method: A Meta Algorithm with Applications
Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
Semi-supervised Learning with Deep Generative Models
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
Recovery of Coherent Data via Low-Rank Dictionary Pursuit
Robust Classification Under Sample Selection Bias
Randomized Experimental Design for Causal Graph Discovery
Recurrent Models of Visual Attention
Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
The limits of squared Euclidean distance regularization
Sparse Space-Time Deconvolution for Calcium Image Analysis
Rates of Convergence for Nearest Neighbor Classification
Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
Searching for Higgs Boson Decay Modes with Deep Learning
Optimizing Energy Production Using Policy Search and Predictive State Representations
Optimal Teaching for Limited-Capacity Human Learners
RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
Optimal decision-making with time-varying evidence reliability
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers
Making Pairwise Binary Graphical Models Attractive
Mode Estimation for High Dimensional Discrete Tree Graphical Models
Non-convex Robust PCA
Learning to Discover Efficient Mathematical Identities
Near-optimal Reinforcement Learning in Factored MDPs
Reducing the Rank in Relational Factorization Models by Including Observable Patterns
Learning Mixtures of Ranking Models
Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
Learning Distributed Representations for Structured Output Prediction
Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models
Learning Deep Features for Scene Recognition using Places Database
Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
Fast and Robust Least Squares Estimation in Corrupted Linear Models
Extended and Unscented Gaussian Processes
Discrete Graph Hashing
Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
Exploiting easy data in online optimization
Large-scale L-BFGS using MapReduce
Difference of Convex Functions Programming for Reinforcement Learning
Convolutional Kernel Networks
Causal Strategic Inference in Networked Microfinance Economies
Clustering from Labels and Time-Varying Graphs
Beyond Disagreement-Based Agnostic Active Learning
Augur: Data-Parallel Probabilistic Modeling
Sparse Polynomial Learning and Graph Sketching
A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights
How transferable are features in deep neural networks?
Advances in Learning Bayesian Networks of Bounded Treewidth
Median Selection Subset Aggregation for Parallel Inference
Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
Probabilistic ODE Solvers with Runge-Kutta Means
On Communication Cost of Distributed Statistical Estimation and Dimensionality
A Bayesian model for identifying hierarchically organised states in neural population activity
A Latent Source Model for Online Collaborative Filtering
Learning Generative Models with Visual Attention
Sequence to Sequence Learning with Neural Networks
Feedforward Learning of Mixture Models
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
Analog Memories in a Balanced Rate-Based Network of E-I Neurons
A Statistical Decision-Theoretic Framework for Social Choice
Asynchronous Anytime Sequential Monte Carlo
"How hard is my MDP?" The distribution-norm to the rescue
A* Sampling
A Wild Bootstrap for Degenerate Kernel Tests
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