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].
In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies
Authors: Yunbum Kook, Santosh Vempala, Matthew Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The contribution of this paper is primarily theoretical, and the paper does not contain any experimental results. We present a new random walk for uniformly sampling high-dimensional convex bodies. It achieves state-of-the-art runtime complexity with stronger guarantees on the output than previously known, namely in Rényi divergence (which implies TV, W2, KL, χ2). The proof departs from known approaches for polytime algorithms for the problem we utilize a stochastic diffusion perspective to show contraction to the target distribution with the rate of convergence determined by functional isoperimetric constants of the stationary density. |
| Researcher Affiliation | Academia | Yunbum Kook School of Computer Science Georgia Institute of Technology EMAIL Santosh S. Vempala School of Computer Science Georgia Institute of Technology EMAIL Matthew S. Zhang Department of Computer Science, University of Toronto, EMAIL |
| Pseudocode | Yes | Algorithm 1 In-and-Out |
| Open Source Code | No | The paper does not contain experiments and therefore does not require data or code. |
| Open Datasets | No | The paper does not contain experiments. |
| Dataset Splits | No | The paper does not contain experiments. |
| Hardware Specification | No | The paper does not contain experiments. |
| Software Dependencies | No | The paper does not contain experiments. |
| Experiment Setup | No | The paper does not contain experiments. |