# Downloads

Number of events: 473

- 3rd NIPS Workshop on Probabilistic Programming
- 4th Workshop on Automated Knowledge Base Construction (AKBC)
- A 3D Simulator for Evaluating Reinforcement and Imitation Learning Algorithms on Complex Tasks
- A Bayesian model for identifying hierarchically organised states in neural population activity
- ABC in Montreal
- A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation
- A Boosting Framework on Grounds of Online Learning
- Accelerated Mini-batch Randomized Block Coordinate Descent Method
- A Complete Variational Tracker
- Active Learning and Best-Response Dynamics
- Active Regression by Stratification
- A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights
- A Drifting-Games Analysis for Online Learning and Applications to Boosting
- A Dual Algorithm for Olfactory Computation in the Locust Brain
- Advances in Learning Bayesian Networks of Bounded Treewidth
- Advances in Variational Inference
- A Filtering Approach to Stochastic Variational Inference
- A framework for studying synaptic plasticity with neural spike train data
- A Framework for Testing Identifiability of Bayesian Models of Perception
- A Latent Source Model for Online Collaborative Filtering
- Algorithm selection by rational metareasoning as a model of human strategy selection
- Algorithms for CVaR Optimization in MDPs
- (Almost) No Label No Cry
- Altitude Training: Strong Bounds for Single-Layer Dropout
- A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
- A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input
- An Accelerated Proximal Coordinate Gradient Method
- Analog Memories in a Balanced Rate-Based Network of E-I Neurons
- Analysis of Brain States from Multi-Region LFP Time-Series
- Analysis of Learning from Positive and Unlabeled Data
- Analysis of Rank Data: Confluence of Social Choice, Operations Research, and Machine Learning
- Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP
- Analyzing the omics of the brain
- An Autoencoder Approach to Learning Bilingual Word Representations
- An Integer Polynomial Programming Based Framework for Lifted MAP Inference
- Approximating Hierarchical MV-sets for Hierarchical Clustering
- A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process
- A provable SVD-based algorithm for learning topics in dominant admixture corpus
- A Representation Theory for Ranking Functions
- A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs
- Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations
- A Safe Screening Rule for Sparse Logistic Regression
- A* Sampling
- A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment
- A Statistical Decision-Theoretic Framework for Social Choice
- A statistical model for tensor PCA
- Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS)
- A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System
- Asynchronous Anytime Sequential Monte Carlo
- Attentional Neural Network: Feature Selection Using Cognitive Feedback
- Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning
- Augur: Data-Parallel Probabilistic Modeling
- A Unified Semantic Embedding: Relating Taxonomies and Attributes
- Automated Variational Inference for Gaussian Process Models
- Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning
- Autonomously Learning Robots
- A Visual and Interactive IDE for Probabilistic Programming
- A Wild Bootstrap for Degenerate Kernel Tests
- Bandit Convex Optimization: Towards Tight Bounds
- Bayes-Adaptive Simulation-based Search with Value Function Approximation
- Bayesian Inference for Structured Spike and Slab Priors
- Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling
- Bayesian Optimization in Academia and Industry
- Bayesian Sampling Using Stochastic Gradient Thermostats
- Best-Arm Identification in Linear Bandits
- Beta-Negative Binomial Process and Exchangeable ￼Random Partitions for Mixed-Membership Modeling
- Beyond Disagreement-Based Agnostic Active Learning
- Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems
- Biclustering Using Message Passing
- BIDMach: High Performance Machine Learning through Codesign and Rooflining
- Blossom Tree Graphical Models
- Bounded Regret for Finite-Armed Structured Bandits
- Bregman Alternating Direction Method of Multipliers
- Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs
- Causal Inference through a Witness Protection Program
- Causal Strategic Inference in Networked Microfinance Economies
- Challenges in Machine Learning workshop (CiML 2014)
- Clamping Variables and Approximate Inference
- Climate Change: Challenges for Machine Learning
- Clustered factor analysis of multineuronal spike data
- Clustering from Labels and Time-Varying Graphs
- Combinatorial Pure Exploration of Multi-Armed Bandits
- Communication-Efficient Distributed Dual Coordinate Ascent
- Communication Efficient Distributed Machine Learning with the Parameter Server
- Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
- Computing Game-Theoretic Solutions
- Computing Nash Equilibria in Generalized Interdependent Security Games
- Concavity of reweighted Kikuchi approximation
- Conditional Random Field Autoencoders for Unsupervised Structured Prediction
- Conditional Swap Regret and Conditional Correlated Equilibrium
- Cone-Constrained Principal Component Analysis
- Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
- Consistency of weighted majority votes
- Consistent Binary Classification with Generalized Performance Metrics
- Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings
- Constrained convex minimization via model-based excessive gap
- Content-based recommendations with Poisson factorization
- Controlling privacy in recommender systems
- Convex Deep Learning via Normalized Kernels
- Convex Optimization Procedure for Clustering: Theoretical Revisit
- Convolutional Kernel Networks
- Convolutional Neural Network Architectures for Matching Natural Language Sentences
- Coresets for k-Segmentation of Streaming Data
- Covariance shrinkage for autocorrelated data
- Decomposing Parameter Estimation Problems
- Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain
- Decoupled Variational Gaussian Inference
- Deep Convolutional Neural Network for Image Deconvolution
- Deep Fragment Embeddings for Bidirectional Image Sentence Mapping
- Deep Joint Task Learning for Generic Object Extraction
- Deep Learning and Representation Learning
- Deep Learning Face Representation by Joint Identification-Verification
- Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
- Deep Networks with Internal Selective Attention through Feedback Connections
- Deep Recursive Neural Networks for Compositionality in Language
- Deep Symmetry Networks
- Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning
- Dependent nonparametric trees for dynamic hierarchical clustering
- Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
- Design Principles of the Hippocampal Cognitive Map
- Deterministic Symmetric Positive Semidefinite Matrix Completion
- DFacTo: Distributed Factorization of Tensors
- Difference of Convex Functions Programming for Reinforcement Learning
- Differential Privacy and Learning: The Tools, The Results, and The Frontier
- Dimensionality Reduction with Subspace Structure Preservation
- Discovering, Learning and Exploiting Relevance
- Discovering Structure in High-Dimensional Data Through Correlation Explanation
- Discrete Graph Hashing
- Discrete Optimization in Machine Learning
- Discriminative Metric Learning by Neighborhood Gerrymandering
- Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
- Distance-Based Network Recovery under Feature Correlation
- Distributed Balanced Clustering via Mapping Coresets
- Distributed Bayesian Posterior Sampling via Moment Sharing
- Distributed Estimation, Information Loss and Exponential Families
- Distributed Machine Learning and Matrix Computations
- Distributed Parameter Estimation in Probabilistic Graphical Models
- Distributed Power-law Graph Computing: Theoretical and Empirical Analysis
- Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models
- Diverse Randomized Agents Vote to Win
- Diverse Sequential Subset Selection for Supervised Video Summarization
- Divide-and-Conquer Learning by Anchoring a Conical Hull
- Do Convnets Learn Correspondence?
- Do Deep Nets Really Need to be Deep?
- Dynamic Rank Factor Model for Text Streams
- Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials
- Efficient learning by implicit exploration in bandit problems with side observations
- Efficient Minimax Signal Detection on Graphs
- Efficient Minimax Strategies for Square Loss Games
- Efficient Optimization for Average Precision SVM
- Efficient Partial Monitoring with Prior Information
- Efficient Sampling for Learning Sparse Additive Models in High Dimensions
- Efficient Structured Matrix Rank Minimization
- Elementary Estimators for Graphical Models
- Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors
- Estimation with Norm Regularization
- Exact Post Model Selection Inference for Marginal Screening
- 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
- Expectation-Maximization for Learning Determinantal Point Processes
- Exploiting easy data in online optimization
- Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
- Exponential Concentration of a Density Functional Estimator
- Extended and Unscented Gaussian Processes
- Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities
- Extracting Latent Structure From Multiple Interacting Neural Populations
- Extremal Mechanisms for Local Differential Privacy
- Extreme bandits
- Factoring Variations in Natural Images with Deep Gaussian Mixture Models
- Fairness, Accountability, and Transparency in Machine Learning
- Fairness in Multi-Agent Sequential Decision-Making
- Fast and Robust Least Squares Estimation in Corrupted Linear Models
- Fast Kernel Learning for Multidimensional Pattern Extrapolation
- Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning
- Fast Prediction for Large-Scale Kernel Machines
- Fast Sampling-Based Inference in Balanced Neuronal Networks
- Fast Training of Pose Detectors in the Fourier Domain
- Feature Cross-Substitution in Adversarial Classification
- Feedback Detection for Live Predictors
- Feedforward Learning of Mixture Models
- Finding a sparse vector in a subspace: Linear sparsity using alternating directions
- Flexible Transfer Learning under Support and Model Shift
- From Bad Models to Good Policies (Sequential Decision Making under Uncertainty)
- From MAP to Marginals: Variational Inference in Bayesian Submodular Models
- From Stochastic Mixability to Fast Rates
- Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation
- Games, Networks, and People
- Gaussian Process Volatility Model
- Generalized Dantzig Selector: Application to the k-support norm
- Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion
- Generalized Unsupervised Manifold Alignment
- General Stochastic Networks for Classification
- General Table Completion using a Bayesian Nonparametric Model
- Generative Adversarial Nets
- Gibbs-type Indian Buffet Processes
- Global Belief Recursive Neural Networks
- Global Sensitivity Analysis for MAP Inference in Graphical Models
- Graph Clustering With Missing Data: Convex Algorithms and Analysis
- Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data
- Greedy Subspace Clustering
- Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction
- Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models
- Hardness of parameter estimation in graphical models
- High-energy particle physics, machine learning, and the HiggsML data challenge (HEPML)
- "How hard is my MDP?" The distribution-norm to the rescue
- How transferable are features in deep neural networks?
- Human Propelled Machine Learning
- ICE: Interactive Classification and Entity Extraction
- Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
- Improved Distributed Principal Component Analysis
- Improved Multimodal Deep Learning with Variation of Information
- Incremental Clustering: The Case for Extra Clusters
- Incremental Local Gaussian Regression
- Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers
- Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit
- Inferring synaptic conductances from spike trains with a biophysically inspired point process model
- Information-based learning by agents in unbounded state spaces
- Integrated Information Theory of Consciousness - Conceptual Formulation & Adventures in Simulated Evolution
- Iterative Neural Autoregressive Distribution Estimator NADE-k
- Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
- Just-In-Time Learning for Fast and Flexible Inference
- Kernel Mean Estimation via Spectral Filtering
- Large-Margin Convex Polytope Machine
- large scale canonical correlation analysis with iterative least squares
- Large-scale L-BFGS using MapReduce
- Large scale optical physiology: From data-acquisition to models of neural coding
- Large-scale reinforcement learning and Markov decision problems
- Latent Support Measure Machines for Bag-of-Words Data Classification
- Learning a Concept Hierarchy from Multi-labeled Documents
- Learning Chordal Markov Networks by Dynamic Programming
- Learning convolution filters for inverse covariance estimation of neural network connectivity
- Learning Deep Features for Scene Recognition using Places Database
- Learning Distributed Representations for Structured Output Prediction
- Learning for Tactile Manipulation
- Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm
- Learning Generative Models with Visual Attention
- Learning Mixed Multinomial Logit Model from Ordinal Data
- Learning Mixtures of Ranking Models
- Learning Mixtures of Submodular Functions for Image Collection Summarization
- Learning Multiple Tasks in Parallel with a Shared Annotator
- Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics
- Learning on graphs using Orthonormal Representation is Statistically Consistent
- Learning Optimal Commitment to Overcome Insecurity
- Learning Semantics
- Learning Shuffle Ideals Under Restricted Distributions
- Learning the Learning Rate for Prediction with Expert Advice
- Learning Time-Varying Coverage Functions
- Learning to Discover Efficient Mathematical Identities
- Learning to Optimize via Information-Directed Sampling
- Learning to Search in Branch and Bound Algorithms
- Learning with Fredholm Kernels
- Learning with Pseudo-Ensembles
- Local Decorrelation For Improved Pedestrian Detection
- Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology
- Local Linear Convergence of Forward--Backward under Partial Smoothness
- Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces
- Low-dimensional models of neural population activity in sensory cortical circuits
- Low Rank Approximation Lower Bounds in Row-Update Streams
- Low-Rank Time-Frequency Synthesis
- LSDA: Large Scale Detection through Adaptation
- Machine Learning for Clinical Data Analysis, Healthcare and Genomics
- Machine Learning in Computational Biology
- Machine Learning in the Browser
- Magnitude-sensitive preference formation`
- Making Pairwise Binary Graphical Models Attractive
- Median Selection Subset Aggregation for Parallel Inference
- Message Passing Inference for Large Scale Graphical Models with High Order Potentials
- Metric Learning for Temporal Sequence Alignment
- Mind the Nuisance: Gaussian Process Classification using Privileged Noise
- Minimax-optimal Inference from Partial Rankings
- MLINI 2014 - 4th NIPS Workshop on Machine Learning and Interpretation in Neuroimaging: Beyond the Scanner
- Mode Estimation for High Dimensional Discrete Tree Graphical Models
- Model-based Reinforcement Learning and the Eluder Dimension
- Modeling Deep Temporal Dependencies with Recurrent "Grammar Cells"
- Modern Machine Learning and Natural Language Processing
- Modern Nonparametrics 3: Automating the Learning Pipeline
- Mondrian Forests: Efficient Online Random Forests
- Multi-Class Deep Boosting
- Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks
- Multi-Resolution Cascades for Multiclass Object Detection
- Multiscale Fields of Patterns
- Multi-scale Graphical Models for Spatio-Temporal Processes
- Multi-Scale Spectral Decomposition of Massive Graphs
- Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition
- Multitask learning meets tensor factorization: task imputation via convex optimization
- Multivariate f-divergence Estimation With Confidence
- Multivariate Regression with Calibration
- Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations
- Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms
- Near-optimal Reinforcement Learning in Factored MDPs
- Near-optimal sample compression for nearest neighbors
- Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures
- Networks: From Graphs to Rich Data
- Networks in Climate Science
- Neural Machine Translation
- Neural Word Embedding as Implicit Matrix Factorization
- Neurons as Monte Carlo Samplers: Bayesian ￼Inference and Learning in Spiking Networks
- New Rules for Domain Independent Lifted MAP Inference
- NIPS’14 Workshop on Crowdsourcing and Machine Learning
- NIPS Workshop on Transactional Machine Learning and E-Commerce
- Non-convex Robust PCA
- Nonparametric Bayesian inference on multivariate exponential families
- Non-Parametric Causal Models
- Novel Trends and Applications in Reinforcement Learning
- Object Localization based on Structural SVM using Privileged Information
- On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification
- On Communication Cost of Distributed Statistical Estimation and Dimensionality
- On Integrated Clustering and Outlier Detection
- On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
- Online and Stochastic Gradient Methods for Non-decomposable Loss Functions
- Online combinatorial optimization with stochastic decision sets and adversarial losses
- Online Decision-Making in General Combinatorial Spaces
- Online Optimization for Max-Norm Regularization
- On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
- On Multiplicative Multitask Feature Learning
- On Prior Distributions and Approximate Inference for Structured Variables
- On Sparse Gaussian Chain Graph Models
- On the Computational Efficiency of Training Neural Networks
- On the Convergence Rate of Decomposable Submodular Function Minimization
- On the Information Theoretic Limits of Learning Ising Models
- On the Number of Linear Regions of Deep Neural Networks
- On the relations of LFPs & Neural Spike Trains
- On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures
- OPT2014: Optimization for Machine Learning
- Optimal decision-making with time-varying evidence reliability
- Optimal Neural Codes for Control and Estimation
- Optimal prior-dependent neural population codes under shared input noise
- Optimal rates for k-NN density and mode estimation
- Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers
- Optimal Teaching for Limited-Capacity Human Learners
- Optimal Transport and Machine Learning
- Optimistic Planning in Markov Decision Processes Using a Generative Model
- Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection
- Optimizing Energy Production Using Policy Search and Predictive State Representations
- Optimizing F-Measures by Cost-Sensitive Classification
- Orbit Regularization
- Out of the Box: Robustness in High Dimension
- PAC-Bayesian AUC classification and scoring
- Parallel Direction Method of Multipliers
- Parallel Double Greedy Submodular Maximization
- Parallel Feature Selection Inspired by Group Testing
- Parallel Sampling of HDPs using Sub-Cluster Splits
- Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization
- Partition-wise Linear Models
- Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision
- Personalization: Methods and Applications
- Perturbations, Optimization, and Statistics
- PEWA: Patch-based Exponentially Weighted Aggregation for image denoising
- Playing with Convnets
- Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data
- Positive Curvature and Hamiltonian Monte Carlo
- Predicting Useful Neighborhoods for Lazy Local Learning
- Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
- Pre-training of Recurrent Neural Networks via Linear Autoencoders
- Privacy in the Land of Plenty
- Probabilistic Differential Dynamic Programming
- Probabilistic low-rank matrix completion on finite alphabets
- Probabilistic ODE Solvers with Runge-Kutta Means
- Projecting Markov Random Field Parameters for Fast Mixing
- Projective dictionary pair learning for pattern classification
- Provable Submodular Minimization using Wolfe's Algorithm
- Provable Tensor Factorization with Missing Data
- Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators
- Quantized Estimation of Gaussian Sequence Models in Euclidean Balls
- Quantized Kernel Learning for Feature Matching
- QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models
- RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning
- Randomized Experimental Design for Causal Graph Discovery
- Ranking via Robust Binary Classification
- Rates of Convergence for Nearest Neighbor Classification
- Real-Time Decoding of an Integrate and Fire Encoder
- Real-time machine-vision applications on a million-spiking-neuron chip
- Recent Progress in the Structure of Large-Treewidth Graphs and Some Applications
- Recovery of Coherent Data via Low-Rank Dictionary Pursuit
- Recurrent Models of Visual Attention
- Recursive Context Propagation Network for Semantic Scene Labeling
- Recursive Inversion Models for Permutations
- Reducing the Rank in Relational Factorization Models by Including Observable Patterns
- Repeated Contextual Auctions with Strategic Buyers
- Representation and Learning Methods for Complex Outputs
- Reputation-based Worker Filtering in Crowdsourcing
- Restricted Boltzmann machines modeling human choice
- Riemannian geometry in machine learning, statistics and computer vision
- Robust Bayesian Max-Margin Clustering
- Robust Classification Under Sample Selection Bias
- Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space
- Robust Logistic Regression and Classification
- Robust Tensor Decomposition with Gross Corruption
- Role of Coupled Networks in 21st Century Energy Infrastructure
- Rounding-based Moves for Metric Labeling
- SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives
- Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature
- Scalable Inference for Neuronal Connectivity from Calcium Imaging
- Scalable Kernel Methods via Doubly Stochastic Gradients
- Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices
- Scalable Non-linear Learning with Adaptive Polynomial Expansions
- Scale Adaptive Blind Deblurring
- Scaling-up Importance Sampling for Markov Logic Networks
- Searching for Higgs Boson Decay Modes with Deep Learning
- Second Workshop on Transfer and Multi-Task Learning: Theory meets Practice
- Self-Adaptable Templates for Feature Coding
- Self-Paced Learning with Diversity
- Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models
- Semi-supervised Learning with Deep Generative Models
- Sensory Integration and Density Estimation
- Sequence to Sequence Learning with Neural Networks
- Sequential Monte Carlo for Graphical Models
- SerialRank: Spectral Ranking using Seriation
- Shape and Illumination from Shading using the Generic Viewpoint Assumption
- Shaping Social Activity by Incentivizing Users
- Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation
- Simple MAP Inference via Low-Rank Relaxations
- Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning
- SmartWheeler – A smart robotic wheelchair platform
- Smoothed Gradients for Stochastic Variational Inference
- Software Engineering for Machine Learning
- Sparse Bayesian structure learning with dependent relevance determination prior
- Sparse Multi-Task Reinforcement Learning
- Sparse PCA via Covariance Thresholding
- Sparse PCA with Oracle Property
- Sparse Polynomial Learning and Graph Sketching
- Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space
- Sparse Space-Time Deconvolution for Calcium Image Analysis
- Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
- Spectral Clustering of graphs with the Bethe Hessian
- Spectral k-Support Norm Regularization
- Spectral Learning of Mixture of Hidden Markov Models
- Spectral Methods for Indian Buffet Process Inference
- Spectral Methods for Supervised Topic Models
- Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing
- Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks
- Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
- Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards
- Stochastic Network Design in Bidirected Trees
- Stochastic Proximal Gradient Descent with Acceleration Techniques
- Stochastic variational inference for hidden Markov models
- Streaming, Memory Limited Algorithms for Community Detection
- Structure learning of antiferromagnetic Ising models
- Structure Regularization for Structured Prediction
- Subgradient Methods for Huge-Scale Optimization Problems
- Submodular Attribute Selection for Action Recognition in Video
- Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets
- Subspace Embeddings for the Polynomial Kernel
- Testing Unfaithful Gaussian Graphical Models
- The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
- The Blinded Bandit: Learning with Adaptive Feedback
- The Infinite Mixture of Infinite Gaussian Mixtures
- The Large Margin Mechanism for Differentially Private Maximization
- The limits of squared Euclidean distance regularization
- The Noisy Power Method: A Meta Algorithm with Applications
- Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology
- Tight Continuous Relaxation of the Balanced k-Cut Problem
- Tight convex relaxations for sparse matrix factorization
- Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time
- Time--Data Tradeoffs by Aggressive Smoothing
- Top Rank Optimization in Linear Time
- Toronto Deep Learning
- Transportability from Multiple Environments with Limited Experiments: Completeness Results
- Tree-structured Gaussian Process Approximations
- Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets
- Two-Stream Convolutional Networks for Action Recognition in Videos
- Universal Option Models
- Unsupervised Deep Haar Scattering on Graphs
- Unsupervised learning of an efficient short-term memory network
- Unsupervised Transcription of Piano Music
- Unsupervised Transcription of Piano Music
- Using Convolutional Neural Networks to Recognize Rhythm ￼Stimuli from Electroencephalography Recordings
- Using the Emergent Dynamics of Attractor Networks for Computation
- Variational Gaussian Process State-Space Models
- Weakly-supervised Discovery of Visual Pattern Configurations
- Weighted importance sampling for off-policy learning with linear function approximation
- Zero-shot recognition with unreliable attributes
- Zeta Hull Pursuits: Learning Nonconvex Data Hulls