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Proceedings of Machine Learning Research

Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research Proceedings of Machine Learning Research
Proceedings of Machine Learning Research
PMLR · 2026-06-02 · via Proceedings of Machine Learning Research

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Volume 76: International Conference on Algorithmic Learning Theory, 15-17 October 2017, Kyoto University, Kyoto, Japan

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Editors: Steve Hanneke, Lev Reyzin

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Algorithmic Learning Theory (ALT) 2017: Preface

; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:1-2

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New bounds on the price of bandit feedback for mistake-bounded online multiclass learning

Philip M. Long; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:3-10

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Minimax rates for cost-sensitive learning on manifolds with approximate nearest neighbours

Henry W. J. Reeve, Gavin Brown; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:11-56

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Universality of Bayesian mixture predictors

Daniil Ryabko; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:57-71

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Erasing Pattern Languages Distinguishable by a Finite Number of Strings

Fahimeh Bayeh, Ziyuan Gao, Sandra Zilles; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:72-108

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Non-Adaptive Randomized Algorithm for Group Testing

Nader H. Bshouty, Nuha Diab, Shada R. Kawar, Robert J. Shahla; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:109-128

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Automatic Learning from Repetitive Texts

Rupert Hölzl, Sanjay Jain, Philipp Schlicht, Karen Seidel, Frank Stephan; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:129-150

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Boundary Crossing for General Exponential Families

Odalric-Ambrym Maillard; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:151-184

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Preference-based Teaching of Unions of Geometric Objects

Ziyuan Gao, David Kirkpatrick, Christoph Ries, Hans Simon, Sandra Zilles; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:185-207

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Specifying a positive threshold function via extremal points

Vadim Lozin, Igor Razgon, Viktor Zamaraev, Elena Zamaraeva, Nikolai Yu. Zolotykh; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:208-222

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A minimax and asymptotically optimal algorithm for stochastic bandits

Pierre Ménard, Aurélien Garivier; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:223-237

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Graph Verification with a Betweenness Oracle

Mano Vikash Janardhanan; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:238-249

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Lifelong Learning in Costly Feature Spaces

Maria-Florina Balcan, Avrim Blum, Vaishnavh Nagarajan; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:250-287

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Collaborative Clustering: Sample Complexity and Efficient Algorithms

Jungseul Ok, Se-Young Yun, Alexandre Proutiere, Rami Mochaourab; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:288-329

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Parameter identification in Markov chain choice models

Arushi Gupta, Daniel Hsu; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:330-340

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The Complexity of Explaining Neural Networks Through (group) Invariants

Danielle Ensign, Scott Neville, Arnab Paul, Suresh Venkatasubramanian; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:341-359

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An efficient query learning algorithm for zero-suppressed binary decision diagrams

Hayato Mizumoto, Shota Todoroki,  Diptarama, Ryo Yoshinaka, Ayumi Shinohara; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:360-371

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Soft-Bayes: Prod for Mixtures of Experts with Log-Loss

Laurent Orseau, Tor Lattimore, Shane Legg; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:372-399

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Hypotheses testing on infinite random graphs

Daniil Ryabko; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:400-411

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Scale-Invariant Unconstrained Online Learning

Wojciech Kotłowski; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:412-433

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Learning MSO-definable hypotheses on strings

Martin Grohe, Christof Löding, Martin Ritzert; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:434-451

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The Power of Random Counterexamples

Dana Angluin, Tyler Dohrn; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:452-465

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A Strongly Quasiconvex PAC-Bayesian Bound

Niklas Thiemann, Christian Igel, Olivier Wintenberger, Yevgeny Seldin; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:466-492

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Normal Forms in Semantic Language Identification

Timo Kötzing, Martin Schirneck, Karen Seidel; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:493-516

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Efficient tracking of a growing number of experts

Jaouad Mourtada, Odalric-Ambrym Maillard; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:517-539

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Tight Bounds on $\ell_1$ Approximation and Learning of Self-Bounding Functions

Vitaly Feldman, Pravesh Kothari, Jan Vondrák; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:540-559

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Relative Error Embeddings of the Gaussian Kernel Distance

Di Chen, Jeff M. Phillips; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:560-576

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Adaptive Submodularity with Varying Query Sets: An Application to Active Multi-label Learning

Alan Fern, Robby Goetschalckx, Mandana Hamidi-Haines, Prasad Tadepalli; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:577-592

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Structured Best Arm Identification with Fixed Confidence

Ruitong Huang, Mohammad M. Ajallooeian, Csaba Szepesvári, Martin Müller; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:593-616

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On Compressive Ensemble Induced Regularisation: How Close is the Finite Ensemble Precision Matrix to the Infinite Ensemble?

Ata Kabán; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:617-628

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Dealing with Range Anxiety in Mean Estimation via Statistical Queries

Vitaly Feldman; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:629-640

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Learning from Networked Examples

Yuyi Wang, Zheng-Chu Guo, Jan Ramon; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:641-666

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PAC Learning Depth-3 $\textrm{AC}^0$ Circuits of Bounded Top Fanin

Ning Ding, Yanli Ren, Dawu Gu; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:667-680

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A Modular Analysis of Adaptive (Non-)Convex Optimization: Optimism, Composite Objectives, and Variational Bounds

Pooria Joulani, András György, Csaba Szepesvári; Proceedings of the 28th International Conference on Algorithmic Learning Theory, PMLR 76:681-720

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