Volume 98: Algorithmic Learning Theory, 22-24 March 2019, Chicago, Illinois

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Editors: Aurélien Garivier, Satyen Kale

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Algorithmic Learning Theory 2019: Preface

Aurélien Garivier, Satyen Kale ; PMLR 98:1-2

On Learning Graphs with Edge-Detecting Queries

Hasan Abasi, Bshouty Nader ; PMLR 98:3-30

An Exponential Tail bound for Lq Stable Learning Rules

Karim Abou-Moustafa, Csaba Szepesvári ; PMLR 98:31-63

Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach

Mastane Achab, Anna Korba, Stephan Clémençon ; PMLR 98:64-93

A minimax near-optimal algorithm for adaptive rejection sampling

Juliette Achddou, Joseph Lam-Weil, Alexandra Carpentier, Gilles Blanchard ; PMLR 98:94-126

Attribute-efficient learning of monomials over highly-correlated variables

Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli ; PMLR 98:127-161

Improved Generalization Bounds for Robust Learning

Idan Attias, Aryeh Kontorovich, Yishay Mansour ; PMLR 98:162-183

A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption

Peter L. Bartlett, Victor Gabillon, Michal Valko ; PMLR 98:184-206

Adaptive Exact Learning of Decision Trees from Membership Queries

Nader H. Bshouty, Catherine A. Haddad-Zaknoon ; PMLR 98:207-234

Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning

Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni ; PMLR 98:235-246

Dynamic Pricing with Finitely Many Unknown Valuations

Nicolò Cesa-Bianchi, Tommaso Cesari, Vianney Perchet ; PMLR 98:247-273

Online Non-Additive Path Learning under Full and Partial Information

Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred Warmuth ; PMLR 98:274-299

Two-Player Games for Efficient Non-Convex Constrained Optimization

Andrew Cotter, Heinrich Jiang, Karthik Sridharan ; PMLR 98:300-332

Competitive ratio vs regret minimization: achieving the best of both worlds

Amit Daniely, Yishay Mansour ; PMLR 98:333-368

Hardness of Improper One-Sided Learning of Conjunctions For All Uniformly Falsifiable CSPs

Alexander Durgin, Brendan Juba ; PMLR 98:369-382

Limit Learning Equivalence Structures

Ekaterina Fokina, Timo Kötzing, Luca San Mauro ; PMLR 98:383-403

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 ; PMLR 98:404-432

A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity

Peter D. Grünwald, Nishant A. Mehta ; PMLR 98:433-465

Sample Compression for Real-Valued Learners

Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi ; PMLR 98:466-488

A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes

Steve Hanneke, Aryeh Kontorovich ; PMLR 98:489-505

Optimal Collusion-Free Teaching

David Kirkpatrick, Hans U. Simon, Sandra Zilles ; PMLR 98:506-528

Cleaning up the neighborhood: A full classification for adversarial partial monitoring

Tor Lattimore, Csaba Szepesvári ; PMLR 98:529-556

Online Influence Maximization with Local Observations

Gábor Lugosi, Gergely Neu, Julia Olkhovskaya ; PMLR 98:557-580

Can Adversarially Robust Learning LeverageComputational Hardness?

Saeed Mahloujifar, Mohammad Mahmoody ; PMLR 98:581-609

Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds

Odalric-Ambrym Maillard ; PMLR 98:610-632

Average-Case Information Complexity of Learning

Ido Nachum, Amir Yehudayoff ; PMLR 98:633-646

Interplay of minimax estimation and minimax support recovery under sparsity

Mohamed Ndaoud ; PMLR 98:647-668

Ising Models with Latent Conditional Gaussian Variables

Frank Nussbaum, Joachim Giesen ; PMLR 98:669-681

Exploiting geometric structure in mixture proportion estimation with generalised Blanchard-Lee-Scott estimators

Henry Reeve, Ata Kabán ; PMLR 98:682-699

PAC Battling Bandits in the Plackett-Luce Model

Aadirupa Saha, Aditya Gopalan ; PMLR 98:700-737

A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation

Clayton Scott ; PMLR 98:738-761

General parallel optimization a without metric

Xuedong Shang, Emilie Kaufmann, Michal Valko ; PMLR 98:762-788

Old Techniques in Differentially Private Linear Regression

Or Sheffet ; PMLR 98:789-827

PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review

Ivan Stelmakh, Nihar B. Shah, Aarti Singh ; PMLR 98:828-856

Stochastic Nonconvex Optimization with Large Minibatches

Weiran Wang, Nathan Srebro ; PMLR 98:857-882

Online Linear Optimization with Sparsity Constraints

Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin ; PMLR 98:883-897

Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations

Di Wang, Adam Smith, Jinhui Xu ; PMLR 98:898-903

Minimax Learning of Ergodic Markov Chains

Geoffrey Wolfer, Aryeh Kontorovich ; PMLR 98:904-930

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