ALT 2019

37 papers

A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation Clayton Scott

A Minimax Near-Optimal Algorithm for Adaptive Rejection Sampling Juliette Achddou, Joseph Lam-Weil, Alexandra Carpentier, Gilles Blanchard

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes Steve Hanneke, Aryeh Kontorovich

A Simple Parameter-Free and Adaptive Approach to Optimization Under a Minimal Local Smoothness Assumption Peter L. Bartlett, Victor Gabillon, Michal Valko

A Tight Excess Risk Bound via a Unified PAC-Bayesian–Rademacher–Shtarkov–MDL Complexity Peter D. Grünwald, Nishant A. Mehta

Adaptive Exact Learning of Decision Trees from Membership Queries Nader H. Bshouty, Catherine A. Haddad-Zaknoon

An Exponential Efron-Stein Inequality for $L_q$ Stable Learning Rules Karim Abou-Moustafa, Csaba Szepesvári

Attribute-Efficient Learning of Monomials over Highly-Correlated Variables Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli

Average-Case Information Complexity of Learning Ido Nachum, Amir Yehudayoff

Can Adversarially Robust Learning LeverageComputational Hardness? Saeed Mahloujifar, Mohammad Mahmoody

Cleaning up the Neighborhood: A Full Classification for Adversarial Partial Monitoring Tor Lattimore, Csaba Szepesvári

Competitive Ratio vs Regret Minimization: Achieving the Best of Both Worlds Amit Daniely, Yishay Mansour

Dimensionality Reduction and (Bucket) Ranking: A Mass Transportation Approach Mastane Achab, Anna Korba, Stephan Clémençon

Dynamic Pricing with Finitely Many Unknown Valuations Nicolò Cesa-Bianchi, Tommaso Cesari, Vianney Perchet

Exploiting Geometric Structure in Mixture Proportion Estimation with Generalised Blanchard-Lee-Scott Estimators Henry Reeve, Ata Kabán

General Parallel Optimization a Without Metric Xuedong Shang, Emilie Kaufmann, Michal Valko

Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni

Hardness of Improper One-Sided Learning of Conjunctions for All Uniformly Falsifiable CSPs Alexander Durgin, Brendan Juba

Improved Generalization Bounds for Robust Learning Idan Attias, Aryeh Kontorovich, Yishay Mansour

Interplay of Minimax Estimation and Minimax Support Recovery Under Sparsity Mohamed Ndaoud

Ising Models with Latent Conditional Gaussian Variables Frank Nussbaum, Joachim Giesen

Limit Learning Equivalence Structures Ekaterina Fokina, Timo Kötzing, Luca San Mauro

Minimax Learning of Ergodic Markov Chains Geoffrey Wolfer, Aryeh Kontorovich

Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations Di Wang, Adam Smith, Jinhui Xu

Old Techniques in Differentially Private Linear Regression Or Sheffet

On Learning Graphs with Edge-Detecting Queries Hasan Abasi, Bshouty Nader

Online Influence Maximization with Local Observations Gábor Lugosi, Gergely Neu, Julia Olkhovskaya

Online Linear Optimization with Sparsity Constraints Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin

Online Non-Additive Path Learning Under Full and Partial Information Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred Warmuth

Optimal Collusion-Free Teaching David Kirkpatrick, Hans U. Simon, Sandra Zilles

PAC Battling Bandits in the Plackett-Luce Model Aadirupa Saha, Aditya Gopalan

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review Ivan Stelmakh, Nihar B. Shah, Aarti Singh

Sample Compression for Real-Valued Learners Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi

Sequential Change-Point Detection: Laplace Concentration of Scan Statistics and Non-Asymptotic Delay Bounds Odalric-Ambrym Maillard

Stochastic Nonconvex Optimization with Large Minibatches Weiran Wang, Nathan Srebro

Two-Player Games for Efficient Non-Convex Constrained Optimization Andrew Cotter, Heinrich Jiang, Karthik Sridharan

Uniform Regret Bounds over $\mathbb{R}^d$ for the Sequential Linear Regression Problem with the Square Loss Pierre Gaillard, Sébastien Gerchinovitz, Malo Huard, Gilles Stoltz