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..
Towards Understanding Extrapolation: a Causal Lens
Authors: Lingjing Kong, Guangyi Chen, Petar Stojanov, Haoxuan Li, Eric Xing, Kun Zhang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments on both synthetic and real-world data, we validate our theoretical findings and their practical implications. |
| Researcher Affiliation | Academia | 1 Carnegie Mellon University 2 Mohamed bin Zayed University of Artificial Intelligence 3 Broad Institute of MIT and Harvard, Cancer Program, Eric and Wendy Schmidt Center |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is provided here. |
| Open Datasets | Yes | We conduct experiments on Image Net-C [45] and Image Net100-C [46] with 15 different types of corruption. ...We conduct experiments on the CIFAR10-C, CIFAR100-C, and Image Net-C datasets [45]... |
| Dataset Splits | No | The paper mentions training and testing, but does not explicitly provide details about a validation dataset split (e.g., percentages or sample counts for validation). |
| Hardware Specification | Yes | The experiments are conducted with the Py Torch 1.11.0 framework, CUDA 12.0 with 4 NVIDIA A100 GPUs. ... The experiments are conducted with the Py Torch 1.13.0 framework, CUDA 11.7 with an NVIDIA A100 GPU. |
| Software Dependencies | Yes | The experiments are conducted with the Py Torch 1.11.0 framework, CUDA 12.0 with 4 NVIDIA A100 GPUs. ... The experiments are conducted with the Py Torch 1.13.0 framework, CUDA 11.7 with an NVIDIA A100 GPU. |
| Experiment Setup | Yes | We train all methods with Adam [68] and learning rate 2e 3 for 25 epochs. We fix the loss weights λcls = 1, λrecons = 0.1, λtgt_likelihood = 0.1, and λs_distance = 0.01 (for dense shifts) overall distance configurations. We only tune λKL from the interval {1e 1, 1e 2, 1e 3}. ... In the pre-train stage, we apply the Res Net50 [47] as the backbone network... |