Boosted CVaR Classification
Authors: Runtian Zhai, Chen Dan, Arun Suggala, J. Zico Kolter, Pradeep Ravikumar
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 {rzhai,cdan,asuggala,zkolter,pradeepr}@cs.cmu.edu |
| 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. |