Learning Theory

Improved Dynamic Regret for Non-degenerate Functions NeurIPS 2017 Improving Viterbi is Hard: Better Runtimes Imply Faster Clique Algorithms ICML 2017 Information-theoretic analysis of generalization capability of learning algorithms NeurIPS 2017 Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications NeurIPS 2017 Investigating Scalability in Hierarchical Language Identification System Interspeech 2017 Is the Bellman residual a bad proxy? NeurIPS 2017 Iterative Machine Teaching ICML 2017 Large-Scale Evolution of Image Classifiers ICML 2017 Learning a Ground Truth Ranking Using Noisy Approval Votes IJCAI 2017 Learning Disjunctions of Predicates COLT 2017 Learning Feature Engineering for Classification IJCAI 2017 Learning Graphical Games from Behavioral Data: Sufficient and Necessary Conditions AISTATS 2017 Learning Hedonic Games IJCAI 2017 Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity NeurIPS 2017 Learning Infinite Layer Networks Without the Kernel Trick ICML 2017 Learning Linear Dynamical Systems via Spectral Filtering NeurIPS 2017 Learning MSO-definable hypotheses on strings ALT 2017 Learning Overcomplete HMMs NeurIPS 2017 Learning ReLUs via Gradient Descent NeurIPS 2017 Learning-Theoretic Foundations of Algorithm Configuration for Combinatorial Partitioning Problems COLT 2017 Learning Theory for Conditional Risk Minimization AISTATS 2017 Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network JMLR 2017 Learning with Limited Rounds of Adaptivity: Coin Tossing, Multi-Armed Bandits, and Ranking from Pairwise Comparisons COLT 2017 Limits of End-to-End Learning ACML 2017 Linear Convergence of Stochastic Frank Wolfe Variants AISTATS 2017