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..
Learning Functional Distributions with Private Labels
Authors: Changlong Wu, Yifan Wang, Ananth Grama, Wojciech Szpankowski
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that the minimax expected KL-risk is of order Θ( pT log |F|) for finite hypothesis class F and any non-trivial noise level. We then extend this result to general infinite classes via the concept of stochastic sequential covering and provide matching lower and upper bounds for a wide range of natural classes. |
| Researcher Affiliation | Academia | 1Center for Science of Information, Department of Computer Science, Purdue University. |
| Pseudocode | Yes | Algorithm 1 Noisy Smooth Bayesian Predictor |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe empirical experiments, thus no dataset is used or made publicly available for training. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments, thus no dataset splits for training, validation, or testing are provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments. Therefore, no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |