Strategic Classification under Unknown Personalized Manipulation
Authors: Han Shao, Avrim Blum, Omar Montasser
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the fundamental mistake bound and sample complexity in the strategic classification... We begin by providing online mistake bounds and PAC sample complexity in these scenarios for ball manipulations. We also explore non-ball manipulations and show that, even in the simplest scenario where both the original and the manipulated feature vectors are revealed, the mistake bounds and sample complexity are lower bounded by Ω(|H|) when the target function belongs to a known class H. |
| Researcher Affiliation | Academia | Han Shao Toyota Technological Institute Chicago Chicago, 60637 han@ttic.edu Avrim Blum Toyota Technological Institute Chicago Chicago, 60637 avrim@ttic.edu Omar Montasser Toyota Technological Institute Chicago Chicago, 60637 omar@ttic.edu |
| Pseudocode | Yes | Algorithm 1 Strategic Halving |
| Open Source Code | No | The paper is theoretical and focuses on mathematical proofs and algorithms; it does not mention releasing any source code or provide links to a repository. |
| Open Datasets | No | The paper is theoretical and discusses abstract "data distributions" and "agents sampled from an underlying data distribution" without referring to any specific named datasets or their public availability for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not describe any empirical experiments or dataset usage that would involve validation splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper describes theoretical algorithms and proofs; it does not mention any specific software implementations or dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on mathematical proofs and bounds, not empirical experimental setups, hyperparameters, or training settings. |