Papers
184,605 papers found
Learning to Explore and Exploit in POMDPs
Chenghui Cai, Xuejun Liao, Lawrence Carin
Learning to Hash with Binary Reconstructive Embeddings
Brian Kulis, Trevor Darrell
Learning to Rank by Optimizing NDCG Measure
Hamed Valizadegan, Rong Jin, Ruofei Zhang et al.
Learning transport operators for image manifolds
Benjamin Culpepper, Bruno A. Olshausen
Learning When Concepts Abound
Omid Madani, Michael Connor, Wiley Greiner
Learning with Compressible Priors
Volkan Cevher
Linear-time Algorithms for Pairwise Statistical Problems
Parikshit Ram, Dongryeol Lee, William March et al.
Locality-sensitive binary codes from shift-invariant kernels
Maxim Raginsky, Svetlana Lazebnik
Localizing Bugs in Program Executions with Graphical Models
Laura Dietz, Valentin Dallmeier, Andreas Zeller et al.
Local Rules for Global MAP: When Do They Work ?
Kyomin Jung, Pushmeet Kohli, Devavrat Shah
Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness
Garvesh Raskutti, Bin Yu, Martin J. Wainwright
Low-Rank Kernel Learning with Bregman Matrix Divergences
Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon
Manifold Embeddings for Model-Based Reinforcement Learning under Partial Observability
Keith Bush, Joelle Pineau
Manifold Regularization for SIR with Rate Root-n Convergence
Wei Bian, Dacheng Tao
Marginal Likelihood Integrals for Mixtures of Independence Models
Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
Cynthia Rudin, Robert E. Schapire
Markov Properties for Linear Causal Models with Correlated Errors
Changsung Kang, Jin Tian
Matrix Completion from Noisy Entries
Raghunandan Keshavan, Andrea Montanari, Sewoong Oh
Matrix Completion from Power-Law Distributed Samples
Raghu Meka, Prateek Jain, Inderjit S. Dhillon
Maximin affinity learning of image segmentation
Kevin Briggman, Winfried Denk, Sebastian Seung et al.
Maximum Entropy Discrimination Markov Networks
Jun Zhu, Eric P. Xing
Maximum likelihood trajectories for continuous-time Markov chains
Theodore J. Perkins
Measuring Invariances in Deep Networks
Ian Goodfellow, Honglak Lee, Quoc V. Le et al.