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..
Statistical Indistinguishability of Learning Algorithms
Authors: Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main results are information-theoretic equivalences between TV indistinguishability and existing algorithmic stability notions such as replicability and approximate differential privacy. Then, we provide statistical amplification and boosting algorithms for TV indistinguishable learners. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, NTUA, Athens, Greece 2Department of Computer Science, Yale University, New Haven, United States 3Google Research 4Department of Computer Science, Technion, Haifa, Israel. |
| Pseudocode | Yes | Algorithm 1 Replicable Heavy-Hitters; Algorithm 2 Replicable Agnostic Learner for Finite H; Algorithm 3 From Global Stability to Replicability; Algorithm 4 List-Global Stability = TV Indistinguishability; Algorithm 5 From TV Indistinguishability to Differential Privacy; Algorithm 6 Amplification of Indistinguishability Guarantees; Algorithm 7 Boosting of Accuracy Guarantee |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and algorithms, not empirical evaluations with datasets. Therefore, it does not mention specific training datasets or their public availability. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments. Therefore, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments. Therefore, it does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, it does not provide specific details about an experimental setup, such as hyperparameters or training settings. |