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..
Online Learning with Unknown Constraints
Authors: Karthik Sridharan, Seung Won Wilson Yoo
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | On the theoretical side, our algorithm s regret can be bounded by the regret of the online regression and online learning oracles, the eluder dimension of the model class containing the unknown safety constraint, and a novel complexity measure that characterizes the difficulty of safe learning. We complement our result with an asymptotic lower bound that shows that the aforementioned complexity measure is necessary. |
| Researcher Affiliation | Academia | Karthik Sridharan 1 Seung Won Wilson Yoo 1 1Department of Computer Science, Cornell University, Ithaca NY, United States. Correspondence to: Seung Won Wilson Yoo <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 General Constrained Online Learning 1: Input: Oracle OL, Oracle OR, A0( ), δ (0, 1), κ, M 2: F0 = {f F : x X, a A0(x), f(a, x) 0} 3: for t = 1, . . . , T do 4: Receive context xt ... Algorithm 2 Online Learning with Long Term Constraints ... Algorithm 3 Oracle OL for Linear Losses ... Algorithm 4 General Online Learning with Vector-Valued Constraints |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the availability of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments that use specific datasets. Therefore, no access information for publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on specific datasets, thus there is no mention of dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not report on experimental results requiring specific hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical algorithms and their bounds, and does not specify any software dependencies or version numbers for implementation. |
| Experiment Setup | No | The paper primarily presents theoretical results and algorithms, and does not include details on experimental setup such as hyperparameters or training configurations. |