Cover Image

Online SuBmodular + SuPermodular (BP) Maximization with Bandit Feedback

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Narang, Adhyyan, Sadeghi, Omid, Ratliff, Lillian J, Fazel, Maryam, Bilmes, Jeff
Format:	Preprint
Published:	2022
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing
by: Narang, Adhyyan, et al.
Published: (2026)

Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning
by: Narang, Adhyyan, et al.
Published: (2024)

Principal-Agent Bandit Games with Self-Interested and Exploratory Learning Agents
by: Liu, Junyan, et al.
Published: (2024)

Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
by: Maiti, Arnab, et al.
Published: (2025)

Emergent specialization from participation dynamics and multi-learner retraining
by: Dean, Sarah, et al.
Published: (2022)

On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback
by: Williams, Marcus, et al.
Published: (2024)

On the Limitations and Possibilities of Nash Regret Minimization in Zero-Sum Matrix Games under Noisy Feedback
by: Maiti, Arnab, et al.
Published: (2023)

Initializing Services in Interactive ML Systems for Diverse Users
by: Bose, Avinandan, et al.
Published: (2023)

On The Complexity of Best-Arm Identification in Non-Stationary Linear Bandits
by: Maynard-Zhang, Leo, et al.
Published: (2026)

Extragradient Preference Optimization (EGPO): Beyond Last-Iterate Convergence for Nash Learning from Human Feedback
by: Zhou, Runlong, et al.
Published: (2025)

Adaptive Calibration in Non-Stationary Environments
by: Liu, Junyan, et al.
Published: (2026)

Linear Submodular Maximization with Bandit Feedback
by: Chen, Wenjing, et al.
Published: (2024)

Online Learning for Uninformed Markov Games: Empirical Nash-Value Regret and Non-Stationarity Adaptation
by: Liu, Junyan, et al.
Published: (2026)

Nearly Minimax Optimal Submodular Maximization with Bandit Feedback
by: Tajdini, Artin, et al.
Published: (2023)

Efficient Uncoupled Learning Dynamics with $\tilde{O}\!\left(T^{-1/4}\right)$ Last-Iterate Convergence in Bilinear Saddle-Point Problems over Convex Sets under Bandit Feedback
by: Maiti, Arnab, et al.
Published: (2026)

A/B Testing and Best-arm Identification for Linear Bandits with Robustness to Non-stationarity
by: Xiong, Zhihan, et al.
Published: (2023)

Beyond Bandit Feedback in Online Multiclass Classification
by: van der Hoeven, Dirk, et al.
Published: (2021)

Bandit and Delayed Feedback in Online Structured Prediction
by: Shibukawa, Yuki, et al.
Published: (2025)

Multiclass Online Learnability under Bandit Feedback
by: Raman, Ananth, et al.
Published: (2023)

Explore-then-Commit for Nonstationary Linear Bandits with Latent Dynamics
by: Choi, Sunmook, et al.
Published: (2025)

Deep Submodular Peripteral Networks
by: Bhatt, Gantavya, et al.
Published: (2024)

Unregularized Linear Convergence in Zero-Sum Game from Preference Feedback
by: Chen, Shulun, et al.
Published: (2025)

Stochastic Online Conformal Prediction with Semi-Bandit Feedback
by: Ge, Haosen, et al.
Published: (2024)

Efficient Online Set-valued Classification with Bandit Feedback
by: Wang, Zhou, et al.
Published: (2024)

Bandit-Feedback Online Multiclass Classification: Variants and Tradeoffs
by: Filmus, Yuval, et al.
Published: (2024)

Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting
by: Yang, Sifan, et al.
Published: (2025)

dUltra: Ultra-Fast Diffusion Language Models via Reinforcement Learning
by: Chen, Shirui, et al.
Published: (2025)

Convergence of Learning Dynamics in Stackelberg Games
by: Fiez, Tanner, et al.
Published: (2019)

Learning to Schedule Online Tasks with Bandit Feedback
by: Xu, Yongxin, et al.
Published: (2024)

Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback
by: Della Vecchia, Riccardo, et al.
Published: (2023)

Online Conformal Abstention for Factuality Control Under Adversarial Bandit Feedback
by: Lee, Minjae, et al.
Published: (2025)

Dual Approximation Policy Optimization
by: Xiong, Zhihan, et al.
Published: (2024)

Hybrid Preference Optimization for Alignment: Provably Faster Convergence Rates by Combining Offline Preferences with Online Exploration
by: Bose, Avinandan, et al.
Published: (2024)

A Learning Algorithm That Attains the Human Optimum in a Repeated Human-Machine Interaction Game
by: Isa, Jason T., et al.
Published: (2025)

Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
by: Zhao, Boxin, et al.
Published: (2021)

Online Budget Allocation with Censored Semi-Bandit Feedback
by: Bachoc, François, et al.
Published: (2025)

Many-Objective Multi-Solution Transport
by: Li, Ziyue, et al.
Published: (2024)

Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
by: Gonzales, Jake, et al.
Published: (2026)

DOPL: Direct Online Preference Learning for Restless Bandits with Preference Feedback
by: Xiong, Guojun, et al.
Published: (2024)

Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback
by: Lancewicki, Tal, et al.
Published: (2025)