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