Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Bandit Convex Optimization: Towards Tight Bounds
Authors: Elad Hazan, Kfir Levy
NeurIPS 2014 | Venue PDF | 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 EMAIL Kfir Y. Levy Technion Israel Institute of Technology Haifa 32000, Israel EMAIL |
| 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. |