ALT 2014

23 papers

A Chain Rule for the Expected Suprema of Gaussian Processes Andreas Maurer

A mAP of Update Constraints in Inductive Inference Timo Kötzing, Raphaela Palenta

A Safe Approximation for Kolmogorov Complexity Peter Bloem, Francisco Mota, Steven de Rooij, Luis Antunes, Pieter Adriaans

A Survey of Preference-Based Online Learning with Bandit Algorithms Róbert Busa-Fekete, Eyke Hüllermeier

Algorithmic Identification of Probabilities Is Hard Laurent Bienvenu, Benoit Monin, Alexander Shen

Bandit Online Optimization over the Permutahedron Nir Ailon, Kohei Hatano, Eiji Takimoto

Bayesian Reinforcement Learning with Exploration Tor Lattimore, Marcus Hutter

Cellular Tree Classifiers Gérard Biau, Luc Devroye

Clustering, Hamming Embedding, Generalized LSH and the Max Norm Behnam Neyshabur, Yury Makarychev, Nathan Srebro

Extreme State Aggregation Beyond MDPs Marcus Hutter

Generalization Bounds for Time Series Prediction with Non-Stationary Processes Vitaly Kuznetsov, Mehryar Mohri

Generalizing Labeled and Unlabeled Sample Compression to Multi-Label Concept Classes Rahim Samei, Boting Yang, Sandra Zilles

Indefinitely Oscillating Martingales Jan Leike, Marcus Hutter

Learning Boolean Halfspaces with Small Weights from Membership Queries Hasan Abasi, Ali Z. Abdi, Nader H. Bshouty

Learning Regular Omega Languages Dana Angluin, Dana Fisman

Offline to Online Conversion Marcus Hutter

On Exact Learning Monotone DNF from Membership Queries Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi

On Learning the Optimal Waiting Time Tor Lattimore, András György, Csaba Szepesvári

On the Role of Update Constraints and Text-Types in Iterative Learning Sanjay Jain, Timo Kötzing, Junqi Ma, Frank Stephan

Parallel Learning of Automatic Classes of Languages Sanjay Jain, Efim B. Kinber

Policy Gradients for CVaR-Constrained MDPs L. A. Prashanth

Robust and Private Bayesian Inference Christos Dimitrakakis, Blaine Nelson, Aikaterini Mitrokotsa, Benjamin I. P. Rubinstein

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko