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..
PAC Generalization via Invariant Representations
Authors: Advait U Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate that in the setting described above using a notion of generalization that we describe, most approximately invariant representations generalize to most new distributions. |
| Researcher Affiliation | Collaboration | 1Department of Electrical and Computer Engineering, University of Texas at Austin 2Google Research India. |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm block. |
| Open Source Code | Yes | The code is available at https://github.com/advaitparulekar/PAC IRM |
| Open Datasets | No | We consider the 7-node linear SEM in Figure 3. The target variable is taken to be Xt. Each edge weight is set to 1 for the observational distribution. |
| Dataset Splits | No | The paper discusses drawing training and test samples but does not specify a separate validation set or exact split percentages for reproduction. |
| Hardware Specification | No | The paper describes experiments but does not specify hardware details such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper provides a link to its code but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We consider the 7-node linear SEM in Figure 3. The target variable is taken to be Xt. Each edge weight is set to 1 for the observational distribution. We consider an interventional distribution Dhard with support over the set of hard interventions on nodes {v3, v4, v5}. Recall that a hard intervention consists of assigning a value to a node. We draw m interventional distributions from Dhard as our training interventions, and draw a sample consisting of N = 200000 datapoints from each distribution. In our experiments, σ2 = 1. |