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ORCID30th ALT 2019: Chicago, Illinois, USA
- Aurélien Garivier, Satyen Kale:
Algorithmic Learning Theory, ALT 2019, 22-24 March 2019, Chicago, Illinois, USA. Proceedings of Machine Learning Research 98, PMLR 2019 - Aurélien Garivier, Satyen Kale:
Algorithmic Learning Theory 2019: Preface. 1-2 - Hasan Abasi, Nader H. Bshouty:
On Learning Graphs with Edge-Detecting Queries. 3-30 - Karim T. Abou-Moustafa, Csaba Szepesvári:
An Exponential Efron-Stein Inequality for Lq Stable Learning Rules. 31-63 - Mastane Achab, Anna Korba, Stéphan Clémençon:
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach. 64-93 - Juliette Achdou, Joseph Lam-Weil, Alexandra Carpentier, Gilles Blanchard:
A minimax near-optimal algorithm for adaptive rejection sampling. 94-126 - Alexandr Andoni, Rishabh Dudeja, Daniel Hsu, Kiran Vodrahalli:
Attribute-efficient learning of monomials over highly-correlated variables. 127-161 - Idan Attias, Aryeh Kontorovich, Yishay Mansour:
Improved Generalization Bounds for Robust Learning. 162-183 - Peter L. Bartlett, Victor Gabillon, Michal Valko:
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption. 184-206 - Nader H. Bshouty, Catherine A. Haddad-Zaknoon:
Adaptive Exact Learning of Decision Trees from Membership Queries. 207-234 - Brian Bullins, Elad Hazan, Adam Kalai, Roi Livni:
Generalize Across Tasks: Efficient Algorithms for Linear Representation Learning. 235-246 - Nicolò Cesa-Bianchi, Tommaso Cesari, Vianney Perchet:
Dynamic Pricing with Finitely Many Unknown Valuations. 247-273 - Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Holakou Rahmanian, Manfred K. Warmuth:
Online Non-Additive Path Learning under Full and Partial Information. 274-299 - Andrew Cotter, Heinrich Jiang, Karthik Sridharan:
Two-Player Games for Efficient Non-Convex Constrained Optimization. 300-332 - Amit Daniely, Yishay Mansour:
Competitive ratio vs regret minimization: achieving the best of both worlds. 333-368 - Alexander Durgin, Brendan Juba:
Hardness of Improper One-Sided Learning of Conjunctions For All Uniformly Falsifiable CSPs. 369-382 - Ekaterina B. Fokina, Timo Kötzing, Luca San Mauro:
Limit Learning Equivalence Structures. 383-403 - Pierre Gaillard, Sébastien Gerchinovitz, Malo Huard, Gilles Stoltz:
Uniform regret bounds over Rd for the sequential linear regression problem with the square loss. 404-432 - Peter D. Grünwald, Nishant A. Mehta:
A tight excess risk bound via a unified PAC-Bayesian-Rademacher-Shtarkov-MDL complexity. 433-465 - Steve Hanneke, Aryeh Kontorovich, Menachem Sadigurschi:
Sample Compression for Real-Valued Learners. 466-488 - Steve Hanneke, Aryeh Kontorovich:
A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes. 489-505 - David G. Kirkpatrick, Hans Ulrich Simon, Sandra Zilles:
Optimal Collusion-Free Teaching. 506-528 - Tor Lattimore, Csaba Szepesvári:
Cleaning up the neighborhood: A full classification for adversarial partial monitoring. 529-556 - Gábor Lugosi, Gergely Neu, Julia Olkhovskaya:
Online Influence Maximization with Local Observations. 557-580 - Saeed Mahloujifar, Mohammad Mahmoody:
Can Adversarially Robust Learning LeverageComputational Hardness? 581-609 - Odalric-Ambrym Maillard:
Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds. 610-632 - Ido Nachum, Amir Yehudayoff:
Average-Case Information Complexity of Learning. 633-646 - Mohamed Ndaoud:
Interplay of minimax estimation and minimax support recovery under sparsity. 647-668 - Frank Nussbaum, Joachim Giesen:
Ising Models with Latent Conditional Gaussian Variables. 669-681 - Henry W. J. Reeve, Ata Kabán:
Exploiting geometric structure in mixture proportion estimation with generalised Blanchard-Lee-Scott estimators. 682-699 - Aadirupa Saha, Aditya Gopalan:
PAC Battling Bandits in the Plackett-Luce Model. 700-737 - Clayton Scott:
A Generalized Neyman-Pearson Criterion for Optimal Domain Adaptation. 738-761 - Xuedong Shang, Emilie Kaufmann, Michal Valko:
General parallel optimization a without metric. 762-787 - Or Sheffet:
Old Techniques in Differentially Private Linear Regression. 788-826 - Ivan Stelmakh, Nihar B. Shah, Aarti Singh:
PeerReview4All: Fair and Accurate Reviewer Assignment in Peer Review. 827-855 - Weiran Wang, Nathan Srebro:
Stochastic Nonconvex Optimization with Large Minibatches. 856-881 - Jun-Kun Wang, Chi-Jen Lu, Shou-De Lin:
Online Linear Optimization with Sparsity Constraints. 882-896 - Di Wang, Adam D. Smith, Jinhui Xu:
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations. 897-902 - Geoffrey Wolfer, Aryeh Kontorovich:
Minimax Learning of Ergodic Markov Chains. 903-929
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