Papers
21,849 papers found
Monte Carlo Sampling for Regret Minimization in Extensive Games
Marc Lanctot, Kevin Waugh, Martin Zinkevich et al.
Multi-Label Prediction via Compressed Sensing
Daniel J. Hsu, Sham M. Kakade, John Langford et al.
Multi-Label Prediction via Sparse Infinite CCA
Piyush Rai, Hal Daume
Multiple Incremental Decremental Learning of Support Vector Machines
Masayuki Karasuyama, Ichiro Takeuchi
Multi-Step Dyna Planning for Policy Evaluation and Control
Hengshuai Yao, Shalabh Bhatnagar, Dongcui Diao et al.
Nash Equilibria of Static Prediction Games
Michael Brückner, Tobias Scheffer
Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling
Lei Shi, Thomas L. Griffiths
Neurometric function analysis of population codes
Philipp Berens, Sebastian Gerwinn, Alexander Ecker et al.
No evidence for active sparsification in the visual cortex
Pietro Berkes, Ben White, Jozsef Fiser
Noise Characterization, Modeling, and Reduction for In Vivo Neural Recording
Zhi Yang, Qi Zhao, Edward Keefer et al.
Noisy Generalized Binary Search
Robert Nowak
Nonlinear directed acyclic structure learning with weakly additive noise models
Arthur Gretton, Peter Spirtes, Robert E. Tillman
Nonlinear Learning using Local Coordinate Coding
Kai Yu, Tong Zhang, Yihong Gong
Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations
Mingyuan Zhou, Haojun Chen, Lu Ren et al.
Nonparametric Bayesian Models for Unsupervised Event Coreference Resolution
Cosmin Bejan, Matthew Titsworth, Andrew Hickl et al.
Nonparametric Bayesian Texture Learning and Synthesis
Long Zhu, Yuanahao Chen, Bill Freeman et al.
Nonparametric Greedy Algorithms for the Sparse Learning Problem
Han Liu, Xi Chen
Nonparametric Latent Feature Models for Link Prediction
Kurt Miller, Michael I. Jordan, Thomas L. Griffiths
Non-stationary continuous dynamic Bayesian networks
Marco Grzegorczyk, Dirk Husmeier
Occlusive Components Analysis
Jörg Lücke, Richard Turner, Maneesh Sahani et al.
On Invariance in Hierarchical Models
Jake Bouvrie, Lorenzo Rosasco, Tomaso Poggio
On Learning Rotations
Raman Arora
Online Learning of Assignments
Matthew Streeter, Daniel Golovin, Andreas Krause
On Stochastic and Worst-case Models for Investing
Elad Hazan, Satyen Kale
On the Algorithmics and Applications of a Mixed-norm based Kernel Learning Formulation
Saketha N. Jagarlapudi, Dinesh G, Raman S et al.