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].
ACCELERATING NONCONVEX LEARNING VIA REPLICA EXCHANGE LANGEVIN DIFFUSION
Authors: Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang
ICLR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We theoretically analyze the acceleration effect of replica exchange from two perspectives: (i) the convergence in χ2-divergence, and (ii) the large deviation principle. Such an acceleration effect allows us to faster approach the global minima. Furthermore, by discretizing the replica exchange Langevin diffusion, we obtain a discrete-time algorithm. For such an algorithm, we quantify its discretization error in theory and demonstrate its acceleration effect in practice. |
| Researcher Affiliation | Academia | Yi Chen Department of Industrial Engineering & Management Science Northwestern University Evanston, IL 60201, USA EMAIL Jinglin Chen Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801, USA EMAIL Jing Dong Columbia Business School School Columbia University New York City, NY 10027, USA EMAIL Jian Peng Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801, USA EMAIL Zhaoran Wang Department of Industrial Engineering & Management Science Northwestern University Evanston, IL 60201, USA EMAIL |
| Pseudocode | No | The paper describes the discrete-time algorithm using equations (3.15) and (3.16) in regular text, but does not present it as a formal pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not mention specific datasets or their public availability for training. |
| Dataset Splits | No | The paper does not provide details on training/validation/test dataset splits. It focuses on theoretical analysis. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for any practical demonstrations or experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper discusses theoretical parameters like swapping intensity and temperatures, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size) for an empirical training process. |