Learning With Multi-Group Guarantees For Clusterable Subpopulations
Authors: Jessica Dai, Nika Haghtalab, Eric Zhao
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our multi-objective approach achieves O(T 1/2) online error without requiring separability in the underlying clusters. This is in contrast to the cluster-then-predict approach, for which we demonstrate O(T 2/3) error rates even under separability assumptions. Along the way, we prove that providing per-subgroup calibration guarantees for underlying clusters can be easier than learning the clusters: separation between median subgroup features is required for the latter but not the former. |
| Researcher Affiliation | Academia | 1Department of Electrial Engineering and Computer Science, U.C. Berkeley, Berkeley, CA, USA. Correspondence to: Jessica Dai <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: Cluster-Then-Predict Algorithm for Minimizing DCE. Algorithm 2: Online Multicalibration Algorithm for Coverable Distinguishers. Algorithm 3 Algorithm for computing a cover. Algorithm 4 Online multicalibration algorithm. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper discusses a generative model where instances are generated from a mixture of k distributions, and uses theoretical constructs like Gaussian mixture models. It does not refer to any specific publicly available datasets used for empirical evaluation. |
| Dataset Splits | No | The paper primarily presents theoretical work on algorithms and guarantees for generative models. It does not describe experiments that would involve splitting datasets into training, testing, or validation sets. |
| Hardware Specification | No | The paper is theoretical in nature, focusing on algorithmic approaches and error rates. It does not describe any empirical experiments or mention specific hardware used for computations. |
| Software Dependencies | No | The paper describes theoretical algorithms and proves bounds. It does not mention any specific software dependencies with version numbers that would be required to reproduce experimental results. |
| Experiment Setup | No | This paper is theoretical and does not describe empirical experiments. Therefore, there are no details provided regarding experimental setup, hyperparameters, or training configurations. |