Clustering with Noisy Queries
Authors: Arya Mazumdar, Barna Saha
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we report some experimental results on real and synthetic datasets. Real Datasets. We use the following three real datasets... We also did experiments on the following synthetic datasets... Figure 1 plots the number of queries vs accuracy trade-off of our computationally efficient adaptive algorithm. |
| Researcher Affiliation | Academia | Arya Mazumdar and Barna Saha College of Information and Computer Sciences University of Massachusetts Amherst Amherst, MA 01003 |
| Pseudocode | Yes | Algorithm. 1 The algorithm that we propose is completely deterministic and has several phases. Phase 1: Selecting a small subgraph... Algorithm 2. Let N = 64k2 log n (1 2p)4. We define two thresholds... |
| Open Source Code | No | The paper mentions a companion paper and an extensive version available on arXiv ([45] A. Mazumdar and B. Saha. Clustering via crowdsourcing. arXiv preprint arXiv:1604.01839, 2016.), but this refers to another paper, not an explicit code release for the algorithms described in this paper. |
| Open Datasets | No | The paper mentions using real datasets (landmarks, captcha, gym) and synthetic datasets (skew, sqrtn), citing other papers as sources (e.g., [31, 52], [27]). However, it does not provide direct URLs, DOIs, or repository names for accessing these datasets, which are required for concrete access information. |
| Dataset Splits | No | The paper does not specify the dataset splits (e.g., percentages or counts for training, validation, and test sets) used for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that are required to reproduce the experiments. |
| Experiment Setup | No | The paper describes the datasets used and some general observations from experiments but does not provide specific experimental setup details such as hyperparameters (e.g., learning rates, batch sizes), model initialization, or optimizer settings. |