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..
DRoP: Distributionally Robust Data Pruning
Authors: Artem Vysogorets, Kartik Ahuja, Julia Kempe
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct the first systematic study of this effect and reveal that existing data pruning algorithms can produce highly biased classifiers. We present theoretical analysis... We thus propose DRo P, a distributionally robust approach to pruning and empirically demonstrate its performance on standard computer vision benchmarks. |
| Researcher Affiliation | Collaboration | Artem Vysogorets Data Science Platform Rockefeller University EMAIL Kartik Ahuja Meta FAIR Julia Kempe New York University Meta FAIR |
| Pseudocode | Yes | Algorithm 1: DRo P Input: Target dataset density d [0, 1]. For each class k [K]: original size Nk, validation recall rk [0, 1]. Initialize: Unsaturated set of classes U [K], excess E d N, class densities dk 0 k [K]. while E > 0 do k U Nk(1 rk); for k U do d k (1 rk)/Z; dk dk + d k; E E Nkd k; if dk > 1 then U U \ {k}; E E + Nk(dk 1); dk 1 end end end Return :{dk}K k=1. |
| Open Source Code | Yes | We make our code available at https://github.com/avysogorets/ drop-data-pruning. |
| Open Datasets | Yes | Our empirical work encompasses three standard computer vision benchmarks (Table 1). ... VGG-16 and VGG-19 (Simonyan & Zisserman, 2015) on CIFAR-10 and CIFAR-100 (Krizhevsky, 2009), respectively, Res Net-18 (He et al., 2016) on Tiny Image Net (MIT License) (Le & Yang, 2015), Image Net pre-trained Res Net-50 on Waterbirds (Sagawa* et al., 2020) (MIT License), and Res Net-50 on Image Net (Deng et al., 2009). |
| Dataset Splits | Yes | Since some of the methods require a hold-out validation set (e.g., DRo P, CDB-W), we reserve 50% of the test set for this purpose. This split is never used when reporting the final model performance. (Also implied by usage of standard benchmark datasets like CIFAR-10, ImageNet). |
| Hardware Specification | Yes | All code is implemented in Py Torch (Paszke et al., 2017) and run on an internal cluster equipped with NVIDIA RTX8000 GPUs. |
| Software Dependencies | No | All code is implemented in Py Torch (Paszke et al., 2017) and run on an internal cluster equipped with NVIDIA RTX8000 GPUs. This mentions PyTorch but does not provide a specific version number (e.g., PyTorch 1.x or 2.x). |
| Experiment Setup | Yes | Table 1: Summary of experimental work and hyperparameters. All architectures include batch normalization (Ioffe & Szegedy, 2015) layers followed by Re LU activations. Models are initialized with Kaiming normal (He et al., 2015) and optimized by SGD (momentum 0.9) with a stepwise LR schedule (0.2 drop factor applied on specified Drop Epochs) and categorical cross-entropy. |