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ORCID36th ALT 2025: Milan, Italy
- Gautam Kamath, Po-Ling Loh:
International Conference on Algorithmic Learning Theory, 24-27 February 2025, Politecnico di Milano, Milan, Italy. Proceedings of Machine Learning Research 272, PMLR 2025 - Preface. 1-3
- Marc Abeille, David Janz, Ciara Pike-Burke:
When and why randomised exploration works (in linear bandits). 4-22 - Baptiste Abélès, Eugenio Clerico, Gergely Neu:
Generalization bounds for mixing processes via delayed online-to-PAC conversions. 23-40 - Mohammad Afzali, Hassan Ashtiani, Christopher Liaw:
Agnostic Private Density Estimation for GMMs via List Global Stability. 41-66 - Sajad Ashkezari, Ruth Urner:
Refining the Sample Complexity of Comparative Learning. 67-88 - Julian Asilis, Mikael Møller Høgsgaard, Grigoris Velegkas:
Understanding Aggregations of Proper Learners in Multiclass Classification. 89-111 - Julian Asilis, Siddartha Devic, Shaddin Dughmi, Vatsal Sharan, Shang-Hua Teng:
Proper Learnability and the Role of Unlabeled Data. 112-133 - Idan Attias, Steve Hanneke, Arvind Ramaswami:
Sample Compression Scheme Reductions. 134-162 - Eric Balkanski, Cherlin Zhu:
Strategyproof Learning with Advice. 163-166 - Eric Balkanski, Jason Chatzitheodorou, Andreas Maggiori:
Cost-Free Fairness in Online Correlation Clustering. 167-203 - Yogev Bar-On, Yishay Mansour:
Non-stochastic Bandits With Evolving Observations. 204-227 - Avrim Blum, Kavya Ravichandran:
Nearly-tight Approximation Guarantees for the Improving Multi-Armed Bandits Problem. 228-245 - Avrim Blum, Kavya Ravichandran:
A Model for Combinatorial Dictionary Learning and Inference. 246-288 - Albert Cheu, Debanuj Nayak:
Differentially Private Multi-Sampling from Distributions. 289-314 - Christopher A. Choquette-Choo, Arun Ganesh, Abhradeep Guha Thakurta:
Near-Optimal Rates for O(1)-Smooth DP-SCO with a Single Epoch and Large Batches. 315-348 - Eugenio Clerico, Tyler Farghly, George Deligiannidis, Benjamin Guedj, Arnaud Doucet:
Generalisation under gradient descent via deterministic PAC-Bayes. 349-389 - Arthur da Cunha, Kasper Green Larsen, Martin Ritzert:
Boosting, Voting Classifiers and Randomized Sample Compression Schemes. 390-404 - Valentino Delle Rose, Alexander Kozachinskiy, Tomasz Steifer:
Effective Littlestone dimension. 405-417 - Shaddin Dughmi, Yusuf Hakan Kalayci, Grayson York:
Is Transductive Learning Equivalent to PAC Learning? 418-443 - Maxwell Fishelson, Robert Kleinberg, Princewill Okoroafor, Renato Paes Leme, Jon Schneider, Yifeng Teng:
Full Swap Regret and Discretized Calibration. 444-480 - Maxime Haddouche, Paul Viallard, Umut Simsekli, Benjamin Guedj:
A PAC-Bayesian Link Between Generalisation and Flat Minima. 481-511 - Steve Hanneke, Liu Yang, Gongju Wang, Yulun Song:
Reliable Active Apprenticeship Learning. 512-538 - Steve Hanneke, Amirreza Shaeiri, Hongao Wang:
For Universal Multiclass Online Learning, Bandit Feedback and Full Supervision are Equivalent. 539-559 - Steve Hanneke, Kun Wang:
A Complete Characterization of Learnability for Stochastic Noisy Bandits. 560-577 - Mikael Møller Høgsgaard:
Efficient Optimal PAC Learning. 578-580 - Max Hopkins, Daniel M. Kane, Shachar Lovett, Gaurav Mahajan:
Do PAC-Learners Learn the Marginal Distribution? 581-610 - Piotr Indyk, Isabelle Quaye, Ronitt Rubinfeld, Sandeep Silwal:
Optimal and learned algorithms for the online list update problem with Zipfian accesses. 611-648 - Eren C. Kizildag:
Information-Theoretic Guarantees for Recovering Low-Rank Tensors from Symmetric Rank-One Measurements. 649-652 - Aryeh Kontorovich, Ariel Avital:
Sharp bounds on aggregate expert error. 653-663 - Ivan Lau, Jonathan Scarlett:
Quantile Multi-Armed Bandits with 1-bit Feedback. 664-699 - Shuchen Li, Ilias Zadik, Manolis Zampetakis:
On the Hardness of Learning One Hidden Layer Neural Networks. 700-701 - Paul Liautaud, Pierre Gaillard, Olivier Wintenberger:
Minimax-optimal and Locally-adaptive Online Nonparametric Regression. 702-735 - Jackie Lok, Rishi Sonthalia, Elizaveta Rebrova:
Error dynamics of mini-batch gradient descent with random reshuffling for least squares regression. 736-770 - Mengqi Lou, Guy Bresler, Ashwin Pananjady:
Computationally efficient reductions between some statistical models. 771 - Anqi Mao, Mehryar Mohri, Yutao Zhong:
Enhanced H-Consistency Bounds. 772-813 - Alessio Mazzetto, Matteo Ceccarello, Andrea Pietracaprina, Geppino Pucci, Eli Upfal:
Center-Based Approximation of a Drifting Distribution. 814-845 - Siddharth Mitra, Andre Wibisono:
Fast Convergence of Φ-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler. 846-869 - Chirag Pabbaraju, Sahasrajit Sarmasarkar:
A Characterization of List Regression. 870-920 - Andrea Pinto, Akshay Rangamani, Tomaso A. Poggio:
On Generalization Bounds for Neural Networks with Low Rank Layers. 921-936 - Sudeep Raja Putta, Shipra Agrawal:
Data Dependent Regret Bounds for Online Portfolio Selection with Predicted Returns. 937-984 - Vinod Raman, Unique Subedi, Ambuj Tewari:
A Unified Theory of Supervised Online Learnability. 985-1007 - Sarah Sachs, Hédi Hadiji, Tim van Erven, Mathias Staudigl:
An Online Feasible Point Method for Benign Generalized Nash Equilibrium Problems. 1008-1040 - Matan Schliserman, Uri Sherman, Tomer Koren:
The Dimension Strikes Back with Gradients: Generalization of Gradient Methods in Stochastic Convex Optimization. 1041-1107 - Jie Shen:
Efficient PAC Learning of Halfspaces with Constant Malicious Noise Rate. 1108-1137 - Georgy Sokolov, Maximilian Thiessen, Margarita Akhmejanova, Fabio Vitale, Francesco Orabona:
Self-Directed Node Classification on Graphs. 1138-1168 - Vishwak Srinivasan, Andre Wibisono, Ashia Wilson:
High-accuracy sampling from constrained spaces with the Metropolis-adjusted Preconditioned Langevin Algorithm. 1169-1220 - Victor Thuot, Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen:
Clustering with bandit feedback: breaking down the computation/information gap. 1221-1284 - Wei-Fu Tseng, Kai-Chun Chen, Zi-Hong Xiao, Yen-Huan Li:
Online Learning of Quantum States with Logarithmic Loss via VB-FTRL. 1285-1312 - Ziao Wang, Nadim Ghaddar, Banghua Zhu, Lele Wang:
Noisy Computing of the Threshold Function. 1313-1315 - Manfred K. Warmuth, Wojciech Kotlowski, Matt Jones, Ehsan Amid:
How rotation invariant algorithms are fooled by noise on sparse targets. 1316-1360 - Julien Zhou, Pierre Gaillard, Thibaud Rahier, Julyan Arbel:
Logarithmic Regret for Unconstrained Submodular Maximization Stochastic Bandit. 1361-1385 - Matthew Zurek, Yudong Chen:
The Plug-in Approach for Average-Reward and Discounted MDPs: Optimal Sample Complexity Analysis. 1386-1387
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