Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff
Authors: Ofer Dekel, Ronen Eldan, Tomer Koren
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present an efficient algorithm for the bandit smooth convex optimization problem that guarantees a regret of e O(T 5/8). Our result rules out an (T 2/3) lower bound and takes a significant step towards the resolution of this open problem. We prove the following regret bound for this algorithm. Theorem 9. We conclude the paper by stating our main lemmas and sketching the proof Lemma 11. The full technical proofs are all deferred to the supplementary material |
| Researcher Affiliation | Collaboration | Ofer Dekel Microsoft Research Redmond, WA oferd@microsoft.com Ronen Eldan Weizmann Institute Rehovot, Israel roneneldan@gmail.com Tomer Koren Technion Haifa, Israel tomerk@technion.ac.il |
| Pseudocode | Yes | Algorithm 1: Bandit Smooth Convex Optimization |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is publicly available. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and describes algorithmic parameters rather than specific experimental setup details like hyperparameters or training configurations. |