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..
Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features
Authors: Annie S Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on four datasets, with multiple distribution shift settings for each, show that PRO2 improves performance by 5-15% when given limited target data compared to prior methods such as standard linear probing. |
| Researcher Affiliation | Collaboration | Annie S. Chen 1, Yoonho Lee 1, Amrith Setlur2, Sergey Levine3, Chelsea Finn1 Stanford University1, Carnegie Mellon University2, UC Berkeley3 |
| Pseudocode | Yes | Algorithm 1 Project and Probe |
| Open Source Code | No | No explicit statement found about releasing the source code for the methodology described in the paper or a direct link to a code repository. |
| Open Datasets | Yes | We run experiments on six datasets with distribution shifts: 4-way collages (Teney et al., 2021), Waterbirds (Sagawa et al., 2020), Celeb A (Liu et al., 2015), Camelyon (Bandi et al., 2018), Living17 (Santurkar et al., 2020), and FMo W (Koh et al., 2021) datasets. |
| Dataset Splits | Yes | For hyperparameter tuning, we adopt the typical practice of using a target validation set, which is common in prior work in similar transfer learning settings (Kirichenko et al., 2022; Mehta et al., 2022; Lee et al., 2022a). |
| Hardware Specification | No | No specific hardware details like GPU/CPU models or memory amounts were provided. It only mentions 'four standard CPUs and no GPUs'. |
| Software Dependencies | No | It mentions 'Py Torch implementation' and 'Adam W optimizer' but without specific version numbers for PyTorch or other dependencies. |
| Experiment Setup | Yes | For all comparisons, we hyperparameter tune over 3 different learning rates (0.1, 0.01, and 0.001) as well as 3 different L2 regularization weights (0.1, 0.01, 0.001). In our main experiments in Sec. 6.2, we also sweep over 6 different projection dimensions (d = 1, 4, 16, 64, 256, 1024) and report results over 10 runs. |