Papers
Dlib-ml: A Machine Learning Toolkit
Davis E. King
SGD-QN: Careful Quasi-Newton Stochastic Gradient Descent
Antoine Bordes, Léon Bottou, Patrick Gallinari
Learning Permutations with Exponential Weights
David P. Helmbold, Manfred K. Warmuth
Application of Non Parametric Empirical Bayes Estimation to High Dimensional Classification
Eitan Greenshtein, Junyong Park
Transfer Learning for Reinforcement Learning Domains: A Survey
Matthew E. Taylor, Peter Stone
Marginal Likelihood Integrals for Mixtures of Independence Models
Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu
Learning Linear Ranking Functions for Beam Search with Application to Planning
Yuehua Xu, Alan Fern, Sungwook Yoon
Bayesian Network Structure Learning by Recursive Autonomy Identification
Raanan Yehezkel, Boaz Lerner
Strong Limit Theorems for the Bayesian Scoring Criterion in Bayesian Networks
Nikolai Slobodianik, Dmitry Zaporozhets, Neal Madras
Robustness and Regularization of Support Vector Machines
Huan Xu, Constantine Caramanis, Shie Mannor
Entropy Inference and the James-Stein Estimator, with Application to Nonlinear Gene Association Networks
Jean Hausser, Korbinian Strimmer
Classification with Gaussians and Convex Loss
Dao-Hong Xiang, Ding-Xuan Zhou
A Least-squares Approach to Direct Importance Estimation
Takafumi Kanamori, Shohei Hido, Masashi Sugiyama
Model Monitor (M2): Evaluating, Comparing, and Monitoring Models
Troy Raeder, Nitesh V. Chawla
Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination
Eugene Tuv, Alexander Borisov, George Runger et al.
Robust Process Discovery with Artificial Negative Events
Stijn Goedertier, David Martens, Jan Vanthienen et al.
Perturbation Corrections in Approximate Inference: Mixture Modelling Applications
Ulrich Paquet, Ole Winther, Manfred Opper
Incorporating Functional Knowledge in Neural Networks
Charles Dugas, Yoshua Bengio, François Bélisle et al.
The Hidden Life of Latent Variables: Bayesian Learning with Mixed Graph Models
Ricardo Silva, Zoubin Ghahramani
Multi-task Reinforcement Learning in Partially Observable Stochastic Environments
Hui Li, Xuejun Liao, Lawrence Carin
Universal Kernel-Based Learning with Applications to Regular Languages
Leonid (Aryeh) Kontorovich, Boaz Nadler
An Algorithm for Reading Dependencies from the Minimal Undirected Independence Map of a Graphoid that Satisfies Weak Transitivity
Jose M. Peña, Roland Nilsson, Johan Björkegren et al.
Fourier Theoretic Probabilistic Inference over Permutations
Jonathan Huang, Carlos Guestrin, Leonidas Guibas