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 [1].
Shifting Regret, Mirror Descent, and Matrices
Authors: Andras Gyorgy, Csaba Szepesvari
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Introducing an arbitrary mapping inside the mirror decent algorithm, we provide a framework that uni๏ฌes and extends existing results. As an example, we prove new shifting regret bounds for matrix prediction problems. |
| Researcher Affiliation | Academia | Andr as Gy orgy EMAIL Dept. of Electrical and Electronic Engineering, Imperial College London, London, SW7 2BT, UK Csaba Szepesv ari EMAIL Dept. of Computing Science, University of Alberta, Edmonton, AB, T6G 2E8 CANADA |
| Pseudocode | Yes | Algorithm 1 Twisted mirror descent. 1. Set w1 K A . 2. At time t = 1, 2, . . . predict wt, and compute vt+1 = argmin u K A [ฮทt โt(wt), u + DR(u, wt) ] wt+1 = ฯt+1(vt+1, โ1, . . . , โt) |
| Open Source Code | No | The paper does not provide any concrete access information for source code. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any datasets, public or otherwise. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations. |