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ORCID26. ALT 2015: Banff, AB, Canada
- Kamalika Chaudhuri, Claudio Gentile, Sandra Zilles:
Algorithmic Learning Theory - 26th International Conference, ALT 2015, Banff, AB, Canada, October 4-6, 2015, Proceedings. Lecture Notes in Computer Science 9355, Springer 2015, ISBN 978-3-319-24485-3
Invited Papers
- Kai Zhong, Prateek Jain, Inderjit S. Dhillon:
Efficient Matrix Sensing Using Rank-1 Gaussian Measurements. 3-18 - Anima Anandkumar, Rong Ge, Daniel J. Hsu, Sham M. Kakade, Matus Telgarsky:
Tensor Decompositions for Learning Latent Variable Models (A Survey for ALT). 19-38
Inductive Inference
- Sanjay Jain, Junqi Ma, Frank Stephan
:
Priced Learning. 41-55 - Ziyuan Gao, Frank Stephan, Sandra Zilles:
Combining Models of Approximation with Partial Learning. 56-70
Learning from Queries, Teaching Complexity
- Montserrat Hermo, Ana Ozaki:
Exact Learning of Multivalued Dependencies. 73-88 - Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi:
Non-adaptive Learning of a Hidden Hypergraph. 89-101 - Ziyuan Gao, Hans Ulrich Simon, Sandra Zilles:
On the Teaching Complexity of Linear Sets. 102-116
Computational Learning Theory and Algorithms
- Dana Angluin, Dongqu Chen:
Learning a Random DFA from Uniform Strings and State Information. 119-133 - Malte Darnstädt, Christoph Ries, Hans Ulrich Simon:
Hierarchical Design of Fast Minimum Disagreement Algorithms. 134-148 - Steve Hanneke, Varun Kanade, Liu Yang:
Learning with a Drifting Target Concept. 149-164 - Ádám Dániel Lelkes, Lev Reyzin:
Interactive Clustering of Linear Classes and Cryptographic Lower Bounds. 165-176
Statistical Learning Theory and Sample Complexity
- Borja Balle, Mehryar Mohri:
On the Rademacher Complexity of Weighted Automata. 179-193 - Anastasia Pentina, Shai Ben-David:
Multi-task and Lifelong Learning of Kernels. 194-208 - Ilya O. Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard:
Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning. 209-223 - Jeff M. Phillips, Yan Zheng:
Subsampling in Smoothed Range Spaces. 224-238 - Shrinu Kushagra, Shai Ben-David:
Information Preserving Dimensionality Reduction. 239-253 - Giulia DeSalvo, Mehryar Mohri, Umar Syed:
Learning with Deep Cascades. 254-269 - Liu Yang, Steve Hanneke, Jaime G. Carbonell:
Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks. 270-284
Online Learning, Stochastic Optimization
- Francesco Orabona, Dávid Pál:
Scale-Free Algorithms for Online Linear Optimization. 287-301 - Yi-Te Hong, Chi-Jen Lu:
Online Learning in Markov Decision Processes with Continuous Actions. 302-316 - Guillaume Papa, Pascal Bianchi, Stéphan Clémençon:
Adaptive Sampling for Incremental Optimization Using Stochastic Gradient Descent. 317-331 - Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto:
Online Linear Optimization for Job Scheduling Under Precedence Constraints. 332-346
Kolmogorov Complexity, Algorithmic Information Theory
- Jan Leike, Marcus Hutter:
Solomonoff Induction Violates Nicod's Criterion. 349-363 - Jan Leike, Marcus Hutter:
On the Computability of Solomonoff Induction and Knowledge-Seeking. 364-378 - Peter Bloem, Steven de Rooij, Pieter Adriaans:
Two Problems for Sophistication. 379-394
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