Regret Minimization with Performative Feedback
Authors: Meena Jagadeesan, Tijana Zrnic, Celestine Mendler-Dünner
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is regret bounds that scale only with the complexity of the distribution shifts and not that of the reward function. The key algorithmic idea is careful exploration of the distribution shifts that informs a novel construction of confidence bounds on the risk of unexplored models. We study the problem of performative regret minimization based on performative feedback. Our main contribution is performative regret bounds that scale primarily with the complexity of the distribution map. |
| Researcher Affiliation | Academia | 1University of California, Berkeley 2Max Planck Institute for Intelligent Systems, Tübingen. Correspondence to: Meena Jagadeesan <mjagadeesan@berkeley.edu>, Tijana Zrnic <tijana.zrnic@berkeley.edu>, Celestine Mendler-Dünner < cmendler@tuebingen.mpg.de.> |
| Pseudocode | Yes | Algorithm 1 Performative Confidence Bounds Algorithm; Algorithm 2 Regret Minimization for Location Families |
| Open Source Code | No | The paper does not contain any statements about releasing code or links to repositories. |
| Open Datasets | No | The paper is theoretical and does not use or reference any publicly available datasets for training or evaluation. The numerical illustrations use synthetic functions for demonstration. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |