Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions
Authors: Pierre Alquier
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study a generalized aggregation strategy, where the weights no longer depend exponentially on the losses. Our strategy is based on Follow The Regularized Leader (FTRL): we minimize the expected losses plus a regularizer, that is here a φ-divergence. When the regularizer is the Kullback-Leibler divergence, we obtain EWA as a special case. Using alternative divergences enables unbounded losses, at the cost of a worst regret bound in some cases. |
| Researcher Affiliation | Academia | Pierre Alquier 1 RIKEN AIP, Tokyo, Japan. Correspondence to: Pierre Alquier <pierrealain.alquier@riken.jp>. |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. Algorithms are described mathematically or textually. |
| Open Source Code | No | The paper does not contain any statements about releasing open-source code or links to a code repository for the methodology described. |
| Open Datasets | No | This paper is theoretical and does not involve experiments on datasets, thus no information regarding dataset availability for training is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experiments on datasets, thus no information regarding validation splits is provided. |
| Hardware Specification | No | This paper is theoretical and does not describe any experiments or the specific hardware used. |
| Software Dependencies | No | This paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup details, such as hyperparameters or training configurations. |