Papers
21,849 papers found
Multi-label Multiple Kernel Learning by Stochastic Approximation: Application to Visual Object Recognition
Serhat Bucak, Rong Jin, Anil K. Jain
Multiparty Differential Privacy via Aggregation of Locally Trained Classifiers
Manas Pathak, Shantanu Rane, Bhiksha Raj
Multiple Kernel Learning and the SMO Algorithm
Zhaonan Sun, Nawanol Ampornpunt, Manik Varma et al.
Multi-Stage Dantzig Selector
Ji Liu, Peter Wonka, Jieping Ye
Multitask Learning without Label Correspondences
Novi Quadrianto, James Petterson, Tibério S. Caetano et al.
Multivariate Dyadic Regression Trees for Sparse Learning Problems
Han Liu, Xi Chen
Multi-View Active Learning in the Non-Realizable Case
Wei Wang, Zhi-Hua Zhou
Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks
Atsushi Miyamae, Yuichi Nagata, Isao Ono et al.
Near-Optimal Bayesian Active Learning with Noisy Observations
Daniel Golovin, Andreas Krause, Debajyoti Ray
Network Flow Algorithms for Structured Sparsity
Julien Mairal, Rodolphe Jenatton, Francis R. Bach et al.
New Adaptive Algorithms for Online Classification
Francesco Orabona, Koby Crammer
Nonparametric Bayesian Policy Priors for Reinforcement Learning
Finale Doshi-velez, David Wingate, Nicholas Roy et al.
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable
Lauren Hannah, Warren Powell, David M. Blei
Non-Stochastic Bandit Slate Problems
Satyen Kale, Lev Reyzin, Robert E. Schapire
Object Bank: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification
Li-jia Li, Hao Su, Li Fei-fei et al.
Occlusion Detection and Motion Estimation with Convex Optimization
Alper Ayvaci, Michalis Raptis, Stefano Soatto
On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient
Tang Jie, Pieter Abbeel
On Herding and the Perceptron Cycling Theorem
Andrew Gelfand, Yutian Chen, Laurens Maaten et al.
Online Classification with Specificity Constraints
Andrey Bernstein, Shie Mannor, Nahum Shimkin
Online Learning for Latent Dirichlet Allocation
Matthew Hoffman, Francis R. Bach, David M. Blei
Online Learning in The Manifold of Low-Rank Matrices
Uri Shalit, Daphna Weinshall, Gal Chechik
Online Learning: Random Averages, Combinatorial Parameters, and Learnability
Alexander Rakhlin, Karthik Sridharan, Ambuj Tewari
Online Markov Decision Processes under Bandit Feedback
Gergely Neu, Andras Antos, András György et al.
On the Convexity of Latent Social Network Inference
Seth Myers, Jure Leskovec
On the Theory of Learnining with Privileged Information
Dmitry Pechyony, Vladimir Vapnik