Cover Image

Minimum mean-squared error estimation with bandit feedback

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ghosh, Ayon, Prashanth, L. A., Sen, Dipayan, Gopalan, Aditya
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

Concentration Bounds for Optimized Certainty Equivalent Risk Estimation
by: Ghosh, Ayon, et al.
Published: (2024)

Active clustering with bandit feedback
by: Thuot, Victor, et al.
Published: (2024)

Trading off rewards and errors in multi-armed bandits
by: Erraqabi, Akram, et al.
Published: (2026)

Fairness in two-player zero-sum games with bandit feedback
by: Akash, S, et al.
Published: (2026)

Optimal last-iterate convergence in matrix games with bandit feedback using the log-barrier
by: Fiegel, Come, et al.
Published: (2026)

Towards Reliable Alignment: Uncertainty-aware RLHF
by: Banerjee, Debangshu, et al.
Published: (2024)

Bad Values but Good Behavior: Learning Highly Misspecified Bandits and MDPs
by: Banerjee, Debangshu, et al.
Published: (2023)

Extreme bandits
by: Carpentier, Alexandra, et al.
Published: (2026)

Approximate information maximization for bandit games
by: Barbier-Chebbah, Alex, et al.
Published: (2023)

Spectral bandits
by: Kocák, Tomáš, et al.
Published: (2026)

Stochastic contextual bandits with graph feedback: from independence number to MAS number
by: Wen, Yuxiao, et al.
Published: (2024)

Testing the Feasibility of Linear Programs with Bandit Feedback
by: Gangrade, Aditya, et al.
Published: (2024)

Why DPO is a Misspecified Estimator and How to Fix It
by: Gopalan, Aditya, et al.
Published: (2025)

Towards Reliable, Uncertainty-Aware Alignment
by: Banerjee, Debangshu, et al.
Published: (2025)

Instance-dependent Stochastic Lipschitz bandit
by: Potfer, Marius, et al.
Published: (2026)

Spectral bandits for smooth graph functions
by: Valko, Michal, et al.
Published: (2026)

Online learning in bandits with predicted context
by: Guo, Yongyi, et al.
Published: (2023)

On the price of exact truthfulness in incentive-compatible online learning with bandit feedback: A regret lower bound for WSU-UX
by: Mortazavi, Ali, et al.
Published: (2024)

Risk and optimal policies in bandit experiments
by: Adusumilli, Karun
Published: (2021)

Revealing graph bandits for maximizing local influence
by: Carpentier, Alexandra, et al.
Published: (2026)

On the optimal regret of collaborative personalized linear bandits
by: Huang, Bruce, et al.
Published: (2025)

Offline-to-online hyperparameter transfer for stochastic bandits
by: Sharma, Dravyansh, et al.
Published: (2025)

Does DQN Learn?
by: Gopalan, Aditya, et al.
Published: (2022)

Linear bandits with polylogarithmic minimax regret
by: Lumbreras, Josep, et al.
Published: (2024)

Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs
by: Markovich-Golan, Shmulik, et al.
Published: (2025)

Lookahead identification in adversarial bandits: accuracy and memory bounds
by: Brukhim, Nataly, et al.
Published: (2026)

Efficient kernelized bandit algorithms via exploration distributions
by: Hu, Bingshan, et al.
Published: (2025)

Leveraging priors on distribution functions for multi-arm bandits
by: Vashishtha, Sumit, et al.
Published: (2025)

Ensemble sampling for linear bandits: small ensembles suffice
by: Janz, David, et al.
Published: (2023)

VITS : Variational Inference Thompson Sampling for contextual bandits
by: Clavier, Pierre, et al.
Published: (2023)

When and why randomised exploration works (in linear bandits)
by: Abeille, Marc, et al.
Published: (2025)

A policy gradient approach for optimization of smooth risk measures
by: Vijayan, Nithia, et al.
Published: (2022)

Policy Gradient Methods for Distortion Risk Measures
by: Vijayan, Nithia, et al.
Published: (2021)

Smoothed functional-based gradient algorithms for off-policy reinforcement learning: A non-asymptotic viewpoint
by: Vijayan, Nithia, et al.
Published: (2021)

Best-of-Both Worlds for linear contextual bandits with paid observations
by: Boyer, Nathan, et al.
Published: (2025)

Spectral bandits for smooth graph functions with applications in recommender systems
by: Kocák, Tomáš, et al.
Published: (2026)

Optimal cross-learning for contextual bandits with unknown context distributions
by: Schneider, Jon, et al.
Published: (2024)

Model selection for behavioral learning data and applications to contextual bandits
by: Aubert, Julien, et al.
Published: (2025)

Variance-sensitive Thompson sampling for generalised linear bandits, revisited
by: Perneczky, Tom, et al.
Published: (2026)

A single algorithm for both restless and rested rotting bandits
by: Seznec, Julien, et al.
Published: (2026)