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..
Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions
Authors: Pierre Alquier
ICML 2021 | Venue PDF | 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 <EMAIL>. |
| 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. |