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..
Boosted CVaR Classification
Authors: Runtian Zhai, Chen Dan, Arun Suggala, J. Zico Kolter, Pradeep Ravikumar
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate our proposed algorithm on four benchmark datasets and show that it achieves higher tail performance than deterministic model training methods. |
| Researcher Affiliation | Academia | Runtian Zhai, Chen Dan, Arun Sai Suggala, Zico Kolter, Pradeep Ravikumar School of Computer Science Carnegie Mellon University Pittsburgh, PA, USA 15213 EMAIL |
| Pseudocode | Yes | Algorithm 1 (Regularized) α-LPBoost for CVa R Classification; Algorithm 2 α-Ada LPBoost |
| Open Source Code | Yes | Code. Codes for this paper can be found at: https://github.com/Runtian Z/boosted_cvar. |
| Open Datasets | Yes | We conduct our experiments on four datasets: COMPAS [LMKA16], Celeb A [LLWT15], CIFAR-10 and CIFAR-100 [KH+09]. |
| Dataset Splits | Yes | On COMPAS we use the training set as the validation set because the dataset is very small. Celeb A has its official train-validation split. On CIFAR-10 and CIFAR-100 we take out 10% of the training samples and use them for validation. |
| Hardware Specification | Yes | We train our models with CPU on COMPAS and with one NVIDIA GTX 1080ti GPU on other datasets. |
| Software Dependencies | Yes | We solve linear programs with the CVXPY package [DB16, AVDB18], which at its core invokes MOSEK [Ap S21] and ECOS [DCB13] for optimization. [...] MOSEK Ap S. MOSEK Optimizer API for Python. Version 9.2.44, 2021. |
| Experiment Setup | Yes | We use a three-layer feed-forward neural network with Re LU activations on COMPAS, a Res Net-18 [HZRS16] on Celeb A, a WRN-28-1 [ZK16] on CIFAR-10 and a WRN-28-10 on CIFAR100. [...] We first warmup the model with a few epochs of ERM, and then train T = 100 base models on COMPAS and CIFAR-10, and T = 50 base models on Celeb A and CIFAR-100 from the warmup model with the sample weights given by the boosting algorithms. [...] For α-Ada LPBoost, we choose η = 1.0 on all datasets which is close to the theoretical optimal value η = p8 log n/T. |