GeezerVisonThe World Wide Web, restored to 800 pixels of glory. |
| Home|About|Gaming cabinet|Archives|How it works|Starter sites |
GeezerVison is showing proceedings.mlr.press — Proceedings of Machine Learning Research [edit] Volume 313: Algorithmic Learning Theory, 23-26 February 2026, Fields Institute, Toronto, Canada[edit] Editors: Matus Telgarsky, Jonathan Ullman Filter Authors: Filter Titles: Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential Yuping Zheng, Andrew Lamperski; 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 subscribe via RSS |
| You are visitor 01402503 — GeezerVison does not store the pages you look up. Best viewed with Netscape Navigator 4.7 or Internet Explorer 6 at 800×600. No cookies required. |