MLJ 2013

57 papers

A Comparative Evaluation of Stochastic-Based Inference Methods for Gaussian Process Models Maurizio Filippone, Mingjun Zhong, Mark A. Girolami

A Reinforcement Learning Approach to Autonomous Decision-Making in Smart Electricity Markets Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins

A Theory of Transfer Learning with Applications to Active Learning Liu Yang, Steve Hanneke, Jaime G. Carbonell

Active Evaluation of Ranking Functions Based on Graded Relevance Christoph Sawade, Steffen Bickel, Timo von Oertzen, Tobias Scheffer, Niels Landwehr

Adaptive Regularization of Weight Vectors Koby Crammer, Alex Kulesza, Mark Dredze

Alignment Based Kernel Learning with a Continuous Set of Base Kernels Arash Afkanpour, Csaba Szepesvári, Michael Bowling

Bayesian Object Matching Arto Klami

Beam Search Algorithms for Multilabel Learning Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan

Block Coordinate Descent Algorithms for Large-Scale Sparse Multiclass Classification Mathieu Blondel, Kazuhiro Seki, Kuniaki Uehara

BoostingTree: Parallel Selection of Weak Learners in Boosting, with Application to Ranking Levente Kocsis, András György, Andrea N. Bán

Calibration and Regret Bounds for Order-Preserving Surrogate Losses in Learning to Rank Clément Calauzènes, Nicolas Usunier, Patrick Gallinari

Completing Causal Networks by Meta-Level Abduction Katsumi Inoue, Andrei Doncescu, Hidetomo Nabeshima

Computational Complexity of Kernel-Based Density-Ratio Estimation: A Condition Number Analysis Takafumi Kanamori, Taiji Suzuki, Masashi Sugiyama

Conditional Validity of Inductive Conformal Predictors Vladimir Vovk

Correlated Topographic Analysis: Estimating an Ordering of Correlated Components Hiroaki Sasaki, Michael Gutmann, Hayaru Shouno, Aapo Hyvärinen

Density Estimation with Minimization of U-Divergence Kanta Naito, Shinto Eguchi

Differential Privacy Based on Importance Weighting Zhanglong Ji, Charles Elkan

Efficient Regularized Least-Squares Algorithms for Conditional Ranking on Relational Data Tapio Pahikkala, Antti Airola, Michiel Stock, Bernard De Baets, Willem Waegeman

Efficiently Learning the Preferences of People Adriana Birlutiu, Perry Groot, Tom Heskes

Exploiting Label Dependencies for Improved Sample Complexity Lena Chekina, Dan Gutfreund, Aryeh Kontorovich, Lior Rokach, Bracha Shapira

Exploiting Symmetries for Scaling Loopy Belief Propagation and Relational Training Babak Ahmadi, Kristian Kersting, Martin Mladenov, Sriraam Natarajan

Exploration and Exploitation of Scratch Games Raphaël Féraud, Tanguy Urvoy

Forecasting Electricity Consumption by Aggregating Specialized Experts - A Review of the Sequential Aggregation of Specialized Experts, with an Application to Slovakian and French Country-Wide One-Day-Ahead (half-)hourly Predictions Marie Devaine, Pierre Gaillard, Yannig Goude, Gilles Stoltz

Geometry Preserving Multi-Task Metric Learning Peipei Yang, Kaizhu Huang, Cheng-Lin Liu

Hypervolume Indicator and Dominance Reward Based Multi-Objective Monte-Carlo Tree Search Weijia Wang, Michèle Sebag

Learning a Factor Model via Regularized PCA Yi-Hao Kao, Benjamin Van Roy

Learning Figures with the Hausdorff Metric by Fractals - Towards Computable Binary Classification Mahito Sugiyama, Eiju Hirowatari, Hideki Tsuiki, Akihiro Yamamoto

Learning Policies for Battery Usage Optimization in Electric Vehicles Stefano Ermon, Yexiang Xue, Carla P. Gomes, Bart Selman

Learning with Infinitely Many Features Alain Rakotomamonjy, Rémi Flamary, Florian Yger

Mass Estimation Kai Ming Ting, Guang-Tong Zhou, Fei Tony Liu, Swee Chuan Tan

Massively Parallel Feature Selection: An Approach Based on Variance Preservation Zheng Zhao, Ruiwen Zhang, James Cox, David Duling, Warren Sarle

Minimax PAC Bounds on the Sample Complexity of Reinforcement Learning with a Generative Model Mohammad Gheshlaghi Azar, Rémi Munos, Hilbert J. Kappen

Modeling Individual Email Patterns over Time with Latent Variable Models Nicholas Navaroli, Christopher DuBois, Padhraic Smyth

Multi-Stage Classifier Design Kirill Trapeznikov, Venkatesh Saligrama, David A. Castañón

Multiclass Classification with Bandit Feedback Using Adaptive Regularization Koby Crammer, Claudio Gentile

New Algorithms for Budgeted Learning Kun Deng, Yaling Zheng, Chris Bourke, Stephen Scott, Julie Masciale

Non-Homogeneous Dynamic Bayesian Networks with Bayesian Regularization for Inferring Gene Regulatory Networks with Gradually Time-Varying Structure Frank Dondelinger, Sophie Lèbre, Dirk Husmeier

On Evaluating Stream Learning Algorithms João Gama, Raquel Sebastião, Pedro Pereira Rodrigues

On Using Nearly-Independent Feature Families for High Precision and Confidence Omid Madani, Manfred Georg, David A. Ross

Online Multiple Kernel Classification Steven C. H. Hoi, Rong Jin, Peilin Zhao, Tianbao Yang

Pairwise Meta-Rules for Better Meta-Learning-Based Algorithm Ranking Quan Sun, Bernhard Pfahringer

Probabilistic Topic Models for Sequence Data Nicola Barbieri, Giuseppe Manco, Ettore Ritacco, Marco Carnuccio, Antonio Bevacqua

Quantum Speed-up for Unsupervised Learning Esma Aïmeur, Gilles Brassard, Sébastien Gambs

Ranking Data with Ordinal Labels: Optimality and Pairwise Aggregation Stéphan Clémençon, Sylvain Robbiano, Nicolas Vayatis

Recovering Networks from Distance Data Sandhya Prabhakaran, David Adametz, Karin J. Metzner, Alexander Böhm, Volker Roth

Regularization of Non-Homogeneous Dynamic Bayesian Networks with Global Information-Coupling Based on Hierarchical Bayesian Models Marco Grzegorczyk, Dirk Husmeier

Robust Ordinal Regression in Preference Learning and Ranking Salvatore Corrente, Salvatore Greco, Milosz Kadzinski, Roman Slowinski

ROC Curves in Cost Space José Hernández-Orallo, Peter A. Flach, César Ferri

Semi-Supervised Learning with Density-Ratio Estimation Masanori Kawakita, Takafumi Kanamori

Sequential Event Prediction Benjamin Letham, Cynthia Rudin, David Madigan

Sparse Non Gaussian Component Analysis by Semidefinite Programming Elmar Diederichs, Anatoli B. Juditsky, Arkadi Nemirovski, Vladimir G. Spokoiny

Spatio-Temporal Random Fields: Compressible Representation and Distributed Estimation Nico Piatkowski, Sangkyun Lee, Katharina Morik

Supervised Clustering of Label Ranking Data Using Label Preference Information Mihajlo Grbovic, Nemanja Djuric, Shengbo Guo, Slobodan Vucetic

TEXPLORE: Real-Time Sample-Efficient Reinforcement Learning for Robots Todd Hester, Peter Stone

The Flip-the-State Transition Operator for Restricted Boltzmann Machines Kai Brügge, Asja Fischer, Christian Igel

Tune and Mix: Learning to Rank Using Ensembles of Calibrated Multi-Class Classifiers Róbert Busa-Fekete, Balázs Kégl, Tamás Éltetö, György Szarvas

Variational Bayesian Sparse Additive Matrix Factorization Shinichi Nakajima, Masashi Sugiyama, S. Derin Babacan