Bandit Convex Optimization: Towards Tight Bounds
Authors: Elad Hazan, Kfir Levy
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we investigate the BCO setting assuming that the adversary is limited to inflicting strongly-convex and smooth losses and the player may choose points from a constrained decision set. In this setting we devise an efficient algorithm that achieves a regret of O( T). This rate is the best possible up to logarithmic factors as implied by a recent work of [11], cleverly obtaining a lower bound of Ω( T) for the same setting. During our analysis, we develop a full-information algorithm that takes advantage of the strong-convexity of loss functions and uses a self-concordant barrier as a regularization term. |
| Researcher Affiliation | Academia | Elad Hazan Technion Israel Institute of Technology Haifa 32000, Israel ehazan@ie.technion.ac.il Kfir Y. Levy Technion Israel Institute of Technology Haifa 32000, Israel kfiryl@tx.technion.ac.il |
| Pseudocode | Yes | Algorithm 1 BCO Algorithm for Str.-convex & Smooth losses Algorithm 2 FTARL-σ |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not refer to any specific datasets or provide access information for a public/open dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameters or training configurations. |