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].
Learning Structured Distributions From Untrusted Batches: Faster and Simpler
Authors: Sitan Chen, Jerry Li, Ankur Moitra
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluated our algorithm on synthetic data. Our experiments fall under two types: (a) learning an arbitrary distribution in Aℓnorm and B) learning a structured distribution in total variation distance. |
| Researcher Affiliation | Collaboration | Sitan Chen MIT EMAIL Jerry Li MSR EMAIL Ankur Moitra MIT EMAIL |
| Pseudocode | Yes | The pseudocode for LEARNWITHFILTER and 1DFILTER is given in Algorithm 1 and 2. |
| Open Source Code | Yes | All code can be found at https://github.com/ secanth/federated. |
| Open Datasets | No | The paper uses 'synthetic data' which was randomly generated by the authors, rather than a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper does not specify traditional training, validation, or test dataset splits. The problem is framed as learning from 'batches' of samples, some of which are corrupted, and the evaluation is performed on these generated batches. |
| Hardware Specification | Yes | The experiments were conducted on a Mac Book Pro with 2.6 GHz Dual-Core Intel Core i5 processor and 8 GB of RAM. |
| Software Dependencies | No | The paper mentions 'SCS solver in CVXPY' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | Throughout, ω = 0 and ℓ= 10. For each trial, we randomly generated µ... In (i), we fixed k = 1000, ϵ = 0.4, N ℓ/ϵ2. In (ii), we fixed ϵ = 0.4, n = 64, N ℓ/ϵ2. In (iii) we fixed n = 64, k = 1000, N ℓ/0.42; in (iv) we fixed n = 128, k = 1000, ϵ = 0.4. |