No Internal Regret with Non-convex Loss Functions
Authors: Dravyansh Sharma
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In the full information setting, for the case of L-Lipschitz loss functions, we obtain an algorithm which achieves regret O( d T log RLT). Further, for the case of one-dimensional piecewise constant functions, we obtain an algorithm which achieves regret O( T log KT) under a mild smoothness assumption, where K is a bound on the number of pieces in each loss function. We show that our bounds are near-optimal by providing a ( T) lower bound on the internal regret for our loss functions. |
| Researcher Affiliation | Academia | Dravyansh Sharma Carnegie Mellon University dravyans@cs.cmu.edu |
| Pseudocode | Yes | Algorithm 1: DEPOM(η, ϵ) Algorithm 2: CEPOM(η) Algorithm 3: INTERNAL EXP3(ηt, γt) |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and regret bounds. It does not use specific datasets for empirical training or evaluation, therefore no concrete access information for a dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and focuses on algorithms and mathematical proofs. It does not mention any specific software dependencies or their version numbers used for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not detail an empirical experimental setup with hyperparameters or training configurations for a computational model. |