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ORCID23. ALT 2012: Lyon, France
- Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Algorithmic Learning Theory - 23rd International Conference, ALT 2012, Lyon, France, October 29-31, 2012. Proceedings. Lecture Notes in Computer Science 7568, Springer 2012, ISBN 978-3-642-34105-2
Editors' Introduction
- Nader H. Bshouty, Gilles Stoltz, Nicolas Vayatis, Thomas Zeugmann:
Editors' Introduction. 1-11
Invited Papers
- Luc De Raedt
:
Declarative Modeling for Machine Learning and Data Mining. 12 - Shai Shalev-Shwartz:
Learnability beyond Uniform Convergence. 13-16 - Pascal Massart, Caroline Meynet:
Some Rates of Convergence for the Selected Lasso Estimator. 17-33 - Toon Calders:
Recent Developments in Pattern Mining. 34 - Gilbert Ritschard:
Exploring Sequential Data. 35
Regular Contributions
- Sanjay Jain, Timo Kötzing, Frank Stephan:
Enlarging Learnable Classes. 36-50 - Ziyuan Gao, Frank Stephan:
Confident and Consistent Partial Learning of Recursive Functions. 51-65 - Sanjay Jain, Efim B. Kinber:
Automatic Learning from Positive Data and Negative Counterexamples. 66-80 - Christophe Costa Florêncio, Sicco Verwer:
Regular Inference as Vertex Coloring. 81-95 - Rahim Samei, Pavel Semukhin, Boting Yang, Sandra Zilles:
Sauer's Bound for a Notion of Teaching Complexity. 96-110 - Dana Angluin, James Aspnes, Aryeh Kontorovich:
On the Learnability of Shuffle Ideals. 111-123 - Mehryar Mohri, Andres Muñoz Medina:
New Analysis and Algorithm for Learning with Drifting Distributions. 124-138 - Shai Ben-David, Ruth Urner:
On the Hardness of Domain Adaptation and the Utility of Unlabeled Target Samples. 139-153 - Hal Daumé III, Jeff M. Phillips, Avishek Saha, Suresh Venkatasubramanian:
Efficient Protocols for Distributed Classification and Optimization. 154-168 - Peter Grünwald:
The Safe Bayesian - Learning the Learning Rate via the Mixability Gap. 169-183 - Lev Reyzin:
Data Stability in Clustering: A Closer Look. 184-198 - Emilie Kaufmann, Nathaniel Korda, Rémi Munos:
Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis. 199-213 - Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos:
Regret Bounds for Restless Markov Bandits. 214-228 - Alexandra Carpentier, Rémi Munos:
Minimax Number of Strata for Online Stratified Sampling Given Noisy Samples. 229-244 - Edward Moroshko, Koby Crammer:
Weighted Last-Step Min-Max Algorithm with Improved Sub-logarithmic Regret. 245-259 - Daiki Suehiro, Kohei Hatano, Shuji Kijima, Eiji Takimoto, Kiyohito Nagano:
Online Prediction under Submodular Constraints. 260-274 - Eyal Gofer, Yishay Mansour:
Lower Bounds on Individual Sequence Regret. 275-289 - Dmitry Adamskiy, Wouter M. Koolen, Alexey V. Chernov, Vladimir Vovk:
A Closer Look at Adaptive Regret. 290-304 - Gábor Bartók, Csaba Szepesvári:
Partial Monitoring with Side Information. 305-319 - Tor Lattimore, Marcus Hutter:
PAC Bounds for Discounted MDPs. 320-334 - Wouter M. Koolen, Vladimir Vovk:
Buy Low, Sell High. 335-349 - Manfred K. Warmuth, Wojciech Kotlowski, Shuisheng Zhou:
Kernelization of Matrix Updates, When and How? 350-364 - Mrinalkanti Ghosh, Satyadev Nandakumar:
Predictive Complexity and Generalized Entropy Rate of Stationary Ergodic Processes. 365-379
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