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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Robust Meta-learning for Mixed Linear Regression with Small Batches
Authors: Weihao Kong, Raghav Somani, Sham Kakade, Sewoong Oh
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Simulation results supporting our theoretical prediction are shown in Fig. 2. For the analysis and the experimental setup we refer to K. |
| Researcher Affiliation | Collaboration | EMAIL. University of Washington EMAIL. University of Washington EMAIL. University of Washington & Microsoft Research EMAIL. University of Washington |
| Pseudocode | Yes | Algorithm 1 Meta-learning ... Algorithm 2 Robust subspace estimation ... Algorithm 3 Double-Filtering |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of its source code. |
| Open Datasets | No | The paper describes a generative model for synthetic data (e.g., 'xi,j N(0, Id) and ϵi,j N(0, σ2 i )') and refers to 'meta-train dataset' as a collection of tasks. It does not mention the use of any specific publicly available datasets with concrete access information for training. |
| Dataset Splits | No | The paper describes theoretical data batches (DL1, DL2, DH) for analysis and refers to 'achievable accuracy' in theoretical terms, but does not provide details on empirical train/validation/test splits used for experimental reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers (e.g., programming languages, libraries, frameworks) used for the experiments. |
| Experiment Setup | No | The paper mentions 'For the analysis and the experimental setup we refer to K.' (Section K is in the supplementary material and not provided). Without access to Section K, no specific hyperparameters, training configurations, or system-level settings are detailed in the main text. |