Papers
4,122 papers found
Learning Linear Ranking Functions for Beam Search with Application to Planning
Yuehua Xu, Alan Fern, Sungwook Yoon
Learning Nondeterministic Classifiers
Juan José del Coz, Jorge Díez, Antonio Bahamonde
Learning Permutations with Exponential Weights
David P. Helmbold, Manfred K. Warmuth
Learning When Concepts Abound
Omid Madani, Michael Connor, Wiley Greiner
Low-Rank Kernel Learning with Bregman Matrix Divergences
Brian Kulis, Mátyás A. Sustik, Inderjit S. Dhillon
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
Maximum Entropy Discrimination Markov Networks
Jun Zhu, Eric P. Xing
Model Monitor (M2): Evaluating, Comparing, and Monitoring Models
Troy Raeder, Nitesh V. Chawla
Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
Hui Li, Xuejun Liao, Lawrence Carin
Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions
Sébastien Bubeck, Ulrike von Luxburg
NEUROSVM: An Architecture to Reduce the Effect of the Choice of Kernel on the Performance of SVM
Pradip Ghanty, Samrat Paul, Nikhil R. Pal
Nieme: Large-Scale Energy-Based Models
Francis Maes
Nonextensive Information Theoretic Kernels on Measures
André F. T. Martins, Noah A. Smith, Eric P. Xing et al.
Nonlinear Models Using Dirichlet Process Mixtures
Babak Shahbaba, Radford Neal
On Efficient Large Margin Semisupervised Learning: Method and Theory
Junhui Wang, Xiaotong Shen, Wei Pan
Online Learning with Sample Path Constraints
Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu
Online Learning with Samples Drawn from Non-identical Distributions
Ting Hu, Ding-Xuan Zhou
On The Power of Membership Queries in Agnostic Learning
Vitaly Feldman
Particle Swarm Model Selection
Hugo Jair Escalante, Manuel Montes, Luis Enrique Sucar
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther, Manfred Opper