Papers
176,624 papers found
Contextual Multi-Armed Bandits
Tyler Lu, David Pal, Martin Pal
Continuous Time Bayesian Network Reasoning and Learning Engine
Christian R. Shelton, Yu Fan, William Lam et al.
Convexity of Proper Composite Binary Losses
Mark Reid, Robert Williamson
Convex Multiple-Instance Learning by Estimating Likelihood Ratio
Fuxin Li, Cristian Sminchisescu
Convex Structure Learning in Log-Linear Models: Beyond Pairwise Potentials
Mark Schmidt, Kevin Murphy
Copula Bayesian Networks
Gal Elidan
Copula Processes
Andrew G Wilson, Zoubin Ghahramani
Co-regularization Based Semi-supervised Domain Adaptation
Abhishek Kumar, Avishek Saha, Hal Daume
Covariance in Unsupervised Learning of Probabilistic Grammars
Shay B. Cohen, Noah A. Smith
Cross Species Expression Analysis using a Dirichlet Process Mixture Model with Latent Matchings
Ziv Bar-joseph, Hai-son P. Le
CUR from a Sparse Optimization Viewpoint
Jacob Bien, Ya Xu, Michael W. Mahoney
Deciphering subsampled data: adaptive compressive sampling as a principle of brain communication
Guy Isely, Christopher Hillar, Fritz Sommer
Decision Tree for Dynamic and Uncertain Data Streams
Chunquan Liang, Yang Zhang, Qun Song
Decoding Ipsilateral Finger Movements from ECoG Signals in Humans
Yuzong Liu, Mohit Sharma, Charles Gaona et al.
Decomposing Isotonic Regression for Efficiently Solving Large Problems
Ronny Luss, Saharon Rosset, Moni Shahar
Deep Coding Network
Yuanqing Lin, Tong Zhang, Shenghuo Zhu et al.
Dense Message Passing for Sparse Principal Component Analysis
Kevin Sharp, Magnus Rattray
Dependent Indian Buffet Processes
Sinead Williamson, Peter Orbanz, Zoubin Ghahramani
Descent Methods for Tuning Parameter Refinement
Alexander Lorbert, Peter Ramadge
Detecting Weak but Hierarchically-Structured Patterns in Networks
Aarti Singh, Robert Nowak, Robert Calderbank
Deterministic Bayesian inference for the $p*$ model
Haakon Austad, Nial Friel
Deterministic Single-Pass Algorithm for LDA
Issei Sato, Kenichi Kurihara, Hiroshi Nakagawa
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
Philippos Mordohai, Gérard Medioni
Direct Loss Minimization for Structured Prediction
Tamir Hazan, Joseph Keshet, David A. McAllester