Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Clustering with Noisy Queries via Multi-Armed Bandit
Authors: Jinghui Xia, Zengfeng Huang
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 24,673 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,184 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we obtain the first matching upper and lower bounds for a wide range of parameters. In particular, a new polynomial time algorithm with O( n(k+log n)/δ2 + poly(k, 1/δ , log n)) queries is proposed. Moreover, we prove a new lower bound of Ω( n log n/δ2 ) |
| Researcher Affiliation | Academia | 1School of Data Science, Fudan University, Shanghai, China 2Shanghai Key Lab of Intelligent Information Processing. Correspondence to: Zengfeng Huang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Sub Cluster(V, k, δ, b): sub-clusters recovery, Algorithm 2 True Cluster Id(u, X1, ..., Xk, δ, α), Algorithm 3 Cluster Verify(v, B), Algorithm 4 Bal Noisy Clustering(V, k, δ, b), Algorithm 5 Gap SBM(T, h, δ), Algorithm 6 Noisy Clustering(V, k, δ), Algorithm 7 A s: algorithm for BAIF. |
| Open Source Code | No | The paper does not contain any statement about making its source code available or provide a link to a code repository. |
| Open Datasets | No | The paper describes a theoretical framework for clustering with noisy queries and does not involve real-world datasets or a training phase. |
| Dataset Splits | No | The paper is theoretical and does not describe experimental setups with data splits (train/validation/test). |
| Hardware Specification | No | The paper describes theoretical algorithms and proofs, and does not mention any hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide details on specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |