Statistical Indistinguishability of Learning Algorithms
Authors: Alkis Kalavasis, Amin Karbasi, Shay Moran, Grigoris Velegkas
ICML 2023 | Conference PDF | Archive PDF | Plain Text | 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. |