Online Learning with Unknown Constraints
Authors: Karthik Sridharan, Seung Won Wilson Yoo
ICML 2025 | Venue PDF | Archive PDF | Plain Text | 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. |