Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Limits of Private Learning with Access to Public Data
Authors: Noga Alon, Raef Bassily, Shay Moran
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,944 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,759 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the limits of learning in this setting in terms of private and public sample complexities. We show that any hypothesis class of VC-dimension d can be agnostically learned up to an excess error of α using only (roughly) d/α public examples and d/α2 private labeled examples. This result holds even when the public examples are unlabeled. This gives a quadratic improvement over the standard d/α2 upper bound on the public sample complexity (where private examples can be ignored altogether if the public examples are labeled). Furthermore, we give a nearly matching lower bound, which we prove via a generic reduction from this setting to the one of private learning without public data. |
| Researcher Affiliation | Collaboration | Noga Alon Department of Mathematics Princeton University EMAIL Raef Bassily Department of Computer Science & Engineering The Ohio State University EMAIL Shay Moran Google AI Princeton EMAIL |
| Pseudocode | Yes | Algorithm 1 ASSPP: Semi-Supervised Semi-Private Agnostic Learner... Algorithm 2 Description of the private learner B:... Algorithm 3 Private Sample Generator Priv Samp:... Algorithm 4 Public Sample Generator Pub Samp: |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on sample complexities and bounds for learning. It defines concepts like "private sample" and "public sample" but does not use or specify a named, publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments with data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe empirical experiments requiring specific software versions. It presents algorithms and proofs. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments. Therefore, there are no specific experimental setup details like hyperparameters or system-level training settings. |