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..
On Learning Mixture of Linear Regressions in the Non-Realizable Setting
Authors: Soumyabrata Pal, Arya Mazumdar, Rajat Sen, Avishek Ghosh
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we validate our theoretical findings via experiments. ... We implement and compare the performance of our algorithms on three non-linear datasets ... We compute the min-loss for five different algorithms on the train and test data (averaged over 30 implementations) for each pair of users and report them in Tables 6 and 3. |
| Researcher Affiliation | Collaboration | Avishek Ghosh 1 Arya Mazumdar 1 Soumyabrata Pal 2 Rajat Sen 3 1 Halıcıo glu Data Science Institute (HDSI), UC San Diego, USA 2 Google Research, India 3 Google Research, Palo Alto, USA. |
| Pseudocode | Yes | Algorithm 1 Gradient AM for Mixture of Regressions, Algorithm 2 Sub-sampled data driven learning of mixture of k regressions, Algorithm 3 Sub-sampled data driven learning of mixture of k regressions with random partitions. |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | For experiments on real datasets, we use the Movielens 1M dataset1 that consists of 1 million ratings from m = 6000 users on n = 4000 movies. 1https://grouplens.org/datasets/movielens/1m/ and Non-linear datasets: We implement and compare the performance of our algorithms on three non-linear datasets generated by sklearn namely makefriedman1 [A](fri, a), makefriedman2[B] (fri, b) and makefriedman3[C] (fri, c). Note that all these datasets are non-realizable. |
| Dataset Splits | Yes | We split this dataset into train and test (80 : 20); in Table 6 (in the appendix), we report the user ids and number of samples in train and test data. and All the three datasets A, B and C comprises of 3200 samples in the train data and 800 samples in the test data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions software components like 'sklearn.linearmodel.RANSACRegressor()' and 'sklearn.datasets.makeregression' and 'python' but does not specify their version numbers. |
| Experiment Setup | Yes | For dataset A, we implement Algorithm 1 with γ =0.1 and random initialization (every element of θ(0) 1 ,θ(0) 2 is generated i.i.d according to a Gaussian with mean 0 and standard deviation 10). and We implement Algorithm 3 with |A| = 150 and h = 1000 and in Steps 3,5 we use the Linear Regression model |