JMLR

Gaussian Process Bandits for Tree Search: Theory and Application to Planning in Discounted MDPs

Louis Dorard, John Shawe-Taylor

Comments Second draft. Tried to follow the JMLR formatting guidelines. Made corrections to the section on planning in MDPs

Graph Kernels

S. V. N. Vishwanathan, Karsten M. Borgwardt, Imre Risi Kondor, Nicol N. Schraudolph

Comments http://jmlr.csail.mit.edu/papers/v11/vishwanathan10a.html

Journal ref Journal of Machine Learning Research 11 (Apr): 1201-1242, 2010

A Quasi-Newton Approach to Nonsmooth Convex Optimization Problems in Machine Learning

Jin Yu, S. V. N. Vishwanathan, Simon Guenter, Nicol N. Schraudolph

Journal ref Journal of Machine Learning Research 11(Mar):1145-1200, 2010

Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines

Masayuki Karasuyama, Naoyuki Harada, Masashi Sugiyama, Ichiro Takeuchi

Comments Submitted to Journal of Machine Learning Research

An Alternative Prior Process for Nonparametric Bayesian Clustering

Hanna M. Wallach, Shane T. Jensen, Lee Dicker, Katherine A. Heller

Journal ref Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS) 2010, JMLR W & CP 9, pp. 892-899

Kernel Partial Least Squares is Universally Consistent

Gilles Blanchard, Nicole Kraemer

Comments 18 pages, no figures

Journal ref JMLR Workshop and Conference Proceedings 9 (AISTATS 2010) 57-64, 2010

Lanczos Approximations for the Speedup of Kernel Partial Least Squares Regression

Nicole Kraemer, Masashi Sugiyama, Mikio Braun

Comments to appear in Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS 09)

Journal ref JMLR Workshop and Conference Proceedings 5 (AISTATS 2009), p 288-295, 2009

Sparse Causal Discovery in Multivariate Time Series

Stefan Haufe, Guido Nolte, Klaus-Robert Mueller, Nicole Kraemer

Comments to appear in Journal of Machine Learning Research, Proceedings of the NIPS'08 workshop on Causality

Journal ref JMLR Workshop and Conference Proceedings 6: Causality: Objectives and Assessment (NIPS 2008), 97 - 106

Data-driven calibration of penalties for least-squares regression

Sylvain Arlot, Pascal Massart

Journal ref Journal of Machine Learning Research 10 (2009) 245-279

Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary $β$-Mixing Processes

Liva Ralaivola, Marie Szafranski, Guillaume Stempfel

Comments Long version of the AISTATS 09 paper: http://jmlr.csail.mit.edu/proceedings/papers/v5/ralaivola09a/ralaivola09a.pdf

Elliptical slice sampling

Iain Murray, Ryan Prescott Adams, David J. C. MacKay

Comments 8 pages, 6 figures, appearing in AISTATS 2010 (JMLR: W&CP volume 6). Differences from first submission: some minor edits in response to feedback.

Online Learning for Matrix Factorization and Sparse Coding

Julien Mairal, Francis Bach, Jean Ponce, Guillermo Sapiro

Comments revised version

Journal ref Journal of Machine Learning Research 11 (2010) 19--60

Composite Binary Losses

Mark D. Reid, Robert C. Williamson

Comments 38 pages, 4 figures. Submitted to JMLR

Prediction with expert advice for the Brier game

Vladimir Vovk, Fedor Zhdanov

Comments 34 pages, 22 figures, 2 tables. The conference version (8 pages) is published in the ICML 2008 Proceedings

Journal ref Journal of Machine Learning Research 10 (2009), 2413 - 2440

Variable Metric Stochastic Approximation Theory

Peter Sunehag, Jochen Trumpf, S. V. N. Vishwanathan, Nicol Schraudolph

Comments Correctment of theorem 3.4. from AISTATS 2009 article

Journal ref Proceedings of AISTATS 2009 Clearwater Florida, Volume 5. of JMLR: W&CP 5

Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks

Jean Hausser, Korbinian Strimmer

Comments 18 pages, 3 figures, 1 table

Journal ref Journal of Machine Learning Research 10: 1469-1484 (2009)

Approximate inference on planar graphs using Loop Calculus and Belief Propagation

V. Gómez, H. J. Kappen, M. Chertkov

Comments 23 pages, 10 figures. Submitted to Journal of Machine Learning Research. Proceedings version accepted for UAI 2009

Marginal Likelihood Integrals for Mixtures of Independence Models

Shaowei Lin, Bernd Sturmfels, Zhiqiang Xu

Comments 28 pages. Journal of Machine Learning Research, to appear

Stability Bound for Stationary Phi-mixing and Beta-mixing Processes

Mehryar Mohri, Afshin Rostamizadeh

Comments 23 pages, 1 figure, submitted to JMLR

Manifold Learning: The Price of Normalization

Y. Goldberg, A. Zakai, D. Kushnir, Y. Ritov

Comments Submitted to JMLR

Graphical methods for efficient likelihood inference in Gaussian covariance models

Mathias Drton, Thomas S. Richardson

Comments Major revision; to appear in Journal of Machine Learning Research

Novel Bounds on Marginal Probabilities

Joris M. Mooij, Hilbert J. Kappen

Comments 33 pages. Submitted to Journal of Machine Learning Research

Graph Laplacians and their convergence on random neighborhood graphs

Matthias Hein, Jean-Yves Audibert, Ulrike von Luxburg

Comments Improved presentation, typos corrected, to appear in JMLR

Undercomplete Blind Subspace Deconvolution

Zoltan Szabo, Barnabas Poczos, Andras Lorincz

Comments Final version, appeared in Journal of Machine Learning Research

Journal ref Zoltan Szabo, Barnabas Poczos, Andras Lorincz: Undercomplete Blind Subspace Deconvolution. Journal of Machine Learning Research 8(May):1063-1095, 2007

Adaptive Online Prediction by Following the Perturbed Leader

Marcus Hutter, Jan Poland

Comments 25 pages

Journal ref Journal of Machine Learning Research 6 (2005) 639--660

Optimality of Universal Bayesian Sequence Prediction for General Loss and Alphabet

Marcus Hutter

Comments 34 pages

Journal ref Journal of Machine Learning Research 4 (2003) 971-1000

Statistical Dynamics of On-line Independent Component Analysis

Gleb Basalyga, Magnus Rattray

Comments 18 pages, 13 figures, to appear in Journal of Machine Learning Research special issue on ICA

Memory-Based Shallow Parsing

Erik F. Tjong Kim Sang

Journal ref Journal of Machine Learning Research, volume 2 (March), 2002, pp. 559-594

Computing maximum likelihood estimates in recursive linear models with correlated errors

Mathias Drton, Michael Eichler, Thomas S. Richardson

Comments 22 pages; removed an incorrect identifiability claim

Journal ref Journal of Machine Learning Research 2009, Vol. 10(Oct), 2329-2348

Robustness and Regularization of Support Vector Machines

Huan Xu, Constantine Caramanis, Shie Mannor

Journal ref Journal of Machine Learning Research, vol 10, 1485-1510, year 2009