Learning Structured Distributions From Untrusted Batches: Faster and Simpler
Authors: Sitan Chen, Jerry Li, Ankur Moitra
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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 sitanc@mit.edu Jerry Li MSR jerrl@microsoft.com Ankur Moitra MIT moitra@mit.edu |
| 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. |