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..
Probabilistically Robust Learning: Balancing Average and Worst-case Performance
Authors: Alexander Robey, Luiz Chamon, George J. Pappas, Hamed Hassani
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | From a practical point of view, we propose a novel algorithm based on risk-aware optimization that effectively balances average and worst-case performance at a considerably lower computational cost relative to adversarial training. Our results on MNIST, CIFAR-10, and SVHN illustrate the advantages of this framework on the spectrum from average to worst-case robustness. |
| Researcher Affiliation | Academia | 1Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, USA 2University of California, Berkeley, Berkeley, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Probabilistically Robust Learning (PRL) |
| Open Source Code | Yes | Our code is available at: https: //github.com/arobey1/advbench. |
| Open Datasets | Yes | We conclude our work by thoroughly evaluating the performance of the algorithm proposed in the previous section on three standard benchmarks: MNIST, CIFAR-10, and SVHN. |
| Dataset Splits | Yes | We conclude our work by thoroughly evaluating the performance of the algorithm proposed in the previous section on three standard benchmarks: MNIST, CIFAR-10, and SVHN. |
| Hardware Specification | Yes | All experiments were run across two four-GPU workstations, comprising a total of eight Quadro RTX 5000 GPUs. |
| Software Dependencies | No | The paper mentions optimizers like Adadelta and SGD, but it does not specify version numbers for these software components or any other libraries. |
| Experiment Setup | Yes | To train these models, we use the Adadelta optimizer (Zeiler, 2012) to minimize the cross-entropy loss for 150 epochs with no learning rate day and an initial learning rate of 1.0. All classifiers were evaluated with a 10-step PGD adversary. To compute the augmented accuracy, we sampled ten samples from r per data point, and to compute the Prob Acc metric, we sample 100 perturbations per data point. For CIFAR-10 (Krizhevsky et al., 2009) and SVHN (Netzer et al., 2011), we used the Res Net-18 architecture (He et al., 2016). We trained using SGD and an initial learning rate of 0.01 and a momentum of 0.9. We also used weight decay with a penalty weight of 3.5 10 3. All classifiers were trained for 115 epochs, and we decayed the learning rate by a factor of 10 at epochs 55, 75, and 90. |