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Editors: Matus Telgarsky, Jonathan Ullman
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Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-39
Smoothed Online Optimization for Target Tracking: Robust and Learning-Augmented Algorithms
Ali Zeynali, Mahsa Sahebdel, Qingsong Liu, Ramesh K. Sitaraman, Mohammad Hajiesmaili; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-36
Shallow Neural Networks Learn Low-Degree Spherical Polynomials with Feature Learning by Learnable Channel Attention
Yingzhen Yang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-48
Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy
Ruihan Wu, Yu-Xiang Wang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-22
PAC-Bayesian Analysis of the Surrogate Relation between Joint Embedding and Supervised Downstream Losses
Theresa Wasserer, Maximilian Fleissner, Debarghya Ghoshdastidar; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-33
Graph Inference with Effective Resistance Queries
Evelyn Warton, Huck Bennett, Mitchell Black, Amir Nayyeri; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-31
Bridging Lifelong and Multi-Task Representation Learning: An Algorithm and a Complexity Measure
Zhi Wang, Chicheng Zhang, Ramya Korlakai Vinayak; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-44
Last-iterate Convergence for Symmetric, General-sum, $2 \times 2$ Games Under The Exponential Weights Dynamic
Guanghui Wang, Krishna Acharya, Lokranjan Lakshmikanthan, Juba Ziani, Vidya Muthukumar; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-38
Multi-distribution Learning: From Worst-Case Optimality to Lexicographic Min-Max Optimality
Guanghui Wang, Umar Syed, Robert E. Schapire, Jacob Abernethy; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-19
Universality of conformal prediction under the assumption of randomness
Vladimir Vovk; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-18
Ranking Items from Discrete Ratings: The Cost of Unknown User Thresholds
Oscar Villemaud, Suryanarayana Sankagiri, Matthias Grossglauser; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-51
Universal Dynamic Regret and Constraint Violation Bounds for Constrained Online Convex Optimization
Subhamon Supantha, Abhishek Sinha; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-29
On the Role of Transformer Feed-Forward Layers in Nonlinear In-Context Learning
Haoyuan Sun, Ali Jadbabaie, Navid Azizan; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-3
Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
Vishwak Srinivasan, Qijia Jiang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-33
Compressibility Barriers to Neighborhood-Preserving Data Visualization
Szymon Snoeck, Noah Bergam, Nakul Verma; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-30
Complexity of Vector-valued Prediction: From Linear Models to Stochastic Convex Optimization
Matan Schliserman, Tomer Koren; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-19
Recycling History: Efficient Recommendations from Contextual Dueling Bandits
Suryanarayana Sankagiri, Jalal Etesami, Pouria Fatemi, Matthias Grossglauser; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-20
Optimal Bounds for Tyler’s M-Estimator for Elliptical Distributions
Akshay Ramachandran, Lap Chi Lau; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-25
Large Average Subtensor Problem: Ground-State, Algorithms, and Algorithmic Barriers
Abhishek Hegade K. R., Eren C. Kizildag; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-2
A Novel Data-Dependent Learning Paradigm for Large Hypothesis Classes
Alireza F. Pour, Shai Ben-David; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-27
How to Set $\beta_1, \beta_2$ in Adam: An Online Learning Perspective
Quan M. Nguyen; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-16
Online Covering with Multiple Experts
Kim Thang Nguyen; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-36
Online Markov Decision Processes with Terminal Law Constraints
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard, Nadia Oudjane; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-52
Vector-valued self-normalized concentration inequalities beyond sub-Gaussianity
Diego Martinez-Taboada, Tomás González, Aaditya Ramdas; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-31
Efficient Opportunistic Approachability
Teodor Vanislavov Marinov, Mehryar Mohri, Princewill Okoroafor, Jon Schneider, Julian Zimmert; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-23
Sample Complexity Bounds for Linear Constrained MDPs with a Generative Model
Xingtu Liu, Lin F. Yang, Sharan Vaswani; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-70
Online Convex Optimization with Heavy Tails: Old Algorithms, New Regrets, and Applications
Zijian Liu; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-47
Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method
Jiaming Liang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-25
Accelerated Mirror Descent for Non-Euclidean Star-convex Functions
Clement LEZANE, Sophie Langer, Wouter M Koolen; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-41
No Scale Sensitive Dimension for Distribution Learning
Tosca Lechner, Shai Ben-David; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-27
Improved Replicable Boosting with Majority-of-Majorities
Kasper Green Larsen, Markus Engelund Mathiasen, Clement Svendsen; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-18
Learning with Monotone Adversarial Corruptions
Kasper Green Larsen, Chirag Pabbaraju, Abhishek Shetty; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-18
Differentially Private Bilevel Optimization
Guy Kornowski; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-36
DS-Compatible Log-Linear Reliability with KL-Prox EM: Monotone Ascent, Identifiability, and Generalization
Shiva Koreddi, Sravani Sowrupilli; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-17
The Planted Number Partitioning Problem
Eren C. Kizildag; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-2
Optimal L2 Regularization in High-dimensional Continual Linear Regression
Gilad Karpel, Edward Moroshko, Ran Levinstein, Ron Meir, Daniel Soudry, Itay Evron; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-62
On Characterizations for Language Generation: Interplay of Hallucinations, Breadth, and Stability
Alkis Kalavasis, Anay Mehrotra, Grigoris Velegkas; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-49
Reusing Samples in Variance Reduction
Yujia Jin, Ishani Karmarkar, Aaron Sidford, Jiayi Wang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-52
Strategy-robust Online Learning in Contextual Pricing
Joon Suk Huh, Kirthevasan Kandasamy; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-32
Nearly Minimax Discrete Distribution Estimation in Kullback-Leibler Divergence with High Probability
Dirk van der Hoeven, Julia Olkhovskaya, Tim van Erven; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-38
Distribution-Dependent Rates for Multi-Distribution Learning
Rafael Hanashiro, Patrick Jaillet; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-52
Relative Information Gain and Gaussian Process Regression
Hamish Flynn; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-30
Sparse Nonparametric Contextual Bandits
Hamish Flynn, Julia Olkhovskaya, Paul Rognon-Vael; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-44
Online and Offline Learning of Orderly Hypergraphs Using Queries
Shaun Fallat, Kamyar Khodamoradi, David G. Kirkpatrick, Valerii Maliuk, Seyed Ahmad Mojallal, Sandra Zilles; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-21
From Continual Learning to SGD and Back: Better Rates for Continual Linear Models
Itay Evron, Ran Levinstein, Matan Schliserman, Uri Sherman, Tomer Koren, Daniel Soudry, Nathan Srebro; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-50
Phase Transition of Regret for Logistic Regression with Large Weights
Michael Drmota, Philippe Jacquet, Changlong Wu, Wojciech Szpankowski; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-28
Uniform Convergence Beyond Glivenko-Cantelli
Tanmay Devale, Pramith Devulapalli, Steve Hanneke; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-21
Suspicious Alignment of SGD:A Fine-Grained Step Size Condition Analysis
Shenyang Deng, Boyao Liao, Zhuoli Ouyang, Tianyu Pang, Minhak Song, Yaoqing Yang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-66
On Purely Private Covariance Estimation
Tommaso d’Orsi, Gleb Novikov; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-11
Sample-Near-Optimal Agnostic Boosting with Improved Running Time
Arthur da Cunha, Mikael Møller Høgsgaard, Andrea Paudice; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-27
Talagrand Meets Talagrand: Upper and Lower Bounds on Expected Soft Maxima of Gaussian Processes with Finite Index Sets
Yifeng Chu, Maxim Raginsky; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-17
A Martingale Kernel Two-Sample Test
Anirban Chatterjee, Aaditya Ramdas; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-44
Pareto-optimal Non-uniform Language Generation
Moses Charikar, Chirag Pabbaraju; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-27
Closeness testing from distributed measurements
Clement Louis Canonne, Aditya Vikram Singh; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-23
Privately Learning Decision Lists and a Differentially Private Winnow
Mark Bun, William Fang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-27
Enjoying Non-linearity in Multinomial Logistic Bandits: A Minimax-Optimal Algorithm
Pierre Boudart, Pierre Gaillard, Alessandro Rudi; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-43
Regularized Robustly Reliable Learners
Avrim Blum, Donya Saless; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-35
Sink equilibria and the attractors of learning in games
Oliver Biggar, Christos H. Papadimitriou; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-21
Beyond Discrepancy: A Closer Look at the Theory of Distribution Shift
Robi Bhattacharjee, Nicholas Rittler, Kamalika Chaudhuri; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-19
Predictive inference for time series: why is split conformal effective despite temporal dependence?
Rina Foygel Barber, Ashwin Pananjady; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-24
Discriminative Feature Feedback with General Teacher Classes
Omri Bar Oz, Tosca Lechner, Sivan Sabato; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-32
Reward Selection with Noisy Observations
Kamyar Azizzadenesheli, Trung Dang, Aranyak Mehta, Alexandros Psomas, Qian Zhang; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-34
On the Hardness of Learning Regular Expressions
Idan Attias, Lev Reyzin, Nathan Srebro, Gal Vardi; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-19
Robust Online Learning
Sajad Ashkezari; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-14
Group-realizable multi-group learning by minimizing empirical risk
Navid Ardeshir, Samuel Deng, Daniel Hsu, Jingwen Liu; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-12
Learning from Synthetic Data: Limitations of ERM
Kareem Amin, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-23
Eventually LIL Regret: Almost Sure $\ln\ln T$ Regret for a sub-Gaussian Mixture on Unbounded Data
Shubhada Agrawal, Aaditya Ramdas; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-26
Convex optimization with $p$-norm oracles
Deeksha Adil, Brian Bullins, Arun Jambulapati, Aaron Sidford; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-38
Efficient and Provable Algorithms for Covariate Shift
Deeksha Adil, Jaroslaw Blasiok; Proceedings of The 37th International Conference on Algorithmic Learning Theory, PMLR 313:1-34
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