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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness
Authors: Bogdan Chornomaz, Yonatan Koren, Shay Moran, Tom Waknine
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 28,760 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,478 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 | The paper discusses "Optimal Rates and the Role of Randomness", focuses on "the problem of learning in the presence of an adversary", "resolve the corresponding question in the agnostic setting", "prove Theorem 1 in a form of Theorem 4 below, which gives quantitative version of the bounds announced in it." It contains lemmas, theorems, and proofs. The NeurIPS Paper Checklist explicitly states "The paper does not include experiments." |
| Researcher Affiliation | Collaboration | Bogdan Chornomaz Department of Mathematics Technion Israel Institute of Technology Haifa, Israel EMAIL. Shay Moran Departments of Mathematics, Computer Science, and Data and Decision Sciences Technion Israel Institute of Technology and Google Research Haifa, Israel EMAIL. |
| Pseudocode | Yes | Putting everything together, our final algorithm proceeds as follows: 1. Given a sample S, draw a random sub-sample T of size O( p VC(H)/Ī·). 2. Use T to construct a finite Īµ-cover HT H, where Īµ = p VC(H)/Ī·. 3. Run exponential sampling over HT : select h HT with probability proportional to exp( Ī» ĖLS(h)). |
| Open Source Code | No | Justification: The paper does not include experiments requiring code. Justification: The paper does not release new assets |
| Open Datasets | No | Justification: The paper does not include experiments. |
| Dataset Splits | No | Justification: The paper does not include experiments. |
| Hardware Specification | No | Justification: The paper does not include experiments. |
| Software Dependencies | No | Justification: The paper does not include experiments. |
| Experiment Setup | No | Justification: The paper does not include experiments. |