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 embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Improved Bounds for Swap Multicalibration and Swap Omniprediction
Authors: Haipeng Luo, Spandan Senapati, Vatsal Sharan
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we answer this question in a strongly affirmative sense. We propose an efficient algorithm that achieves O(T 1 3 ) ℓ2-swap multicalibration error (both in high probability and expectation). On propagating this bound onward, we obtain significantly improved rates for ℓ1-swap multicalibration and swap omniprediction for a loss class of convex Lipschitz functions. In particular, we show that our algorithm achieves O(T 2 3 ) ℓ1-swap multicalibration and swap omniprediction errors, thereby improving upon the previous best-known bound of O(T 7 8 ). As a consequence of our improved online results, we further obtain several improved sample complexity rates in the distributional setting. In particular, we establish a O(ε 3) sample complexity of efficiently learning an ε-swap omnipredictor for the class of convex and Lipschitz functions, O(ε 2.5) sample complexity of efficiently learning an ε-swap agnostic learner for the squared loss, and O(ε 5), O(ε 2.5) sample complexities of learning ℓ1, ℓ2-swap multicalibrated predictors against linear functions, all of which significantly improve on the previous best-known bounds. |
| Researcher Affiliation | Academia | Haipeng Luo USC EMAIL Spandan Senapati USC EMAIL Vatsal Sharan USC EMAIL |
| Pseudocode | Yes | Algorithm 1 The i-th external regret algorithm (Ai) ... Algorithm 2 Randomized rounding (RRound(p)) ... Algorithm 3 Generic BM algorithmic template ... Algorithm 4 Online Newton Step (ONSi) with scaled losses ... Algorithm 5 ALGi |
| Open Source Code | No | The paper does not contain any explicit statements about code availability, nor does it provide any links to a code repository or mention code in supplementary materials. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and bounds. It defines an 'instance space be X Rd, label set be Y = {0, 1}' but does not refer to any specific public datasets for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not perform experiments on specific datasets, therefore, no dataset splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore, no experimental setup details such as hyperparameters or training configurations are provided. |