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Brendon G. Anderson, University of California, Berkeley

* indicates co-first author and equal contribution.

Preprints

  1. Evolutionary games on infinite strategy sets: Convergence to Nash equilibria via dissipativity, 2023, under review
         B. G. Anderson, S. Sojoudi, and M. Arcak

  2. Towards optimal branching of linear and semidefinite relaxations for neural network robustness certification, 2023, under review
         B. G. Anderson, Z. Ma, J. Li, and S. Sojoudi

Journal Papers

  1. Improving the accuracy-robustness trade-off of classifiers via adaptive smoothing, SIAM Journal on Mathematics of Data Science (SIMODS), 2024
         Second place method on RobustBench CIFAR-100 \(\ell_\infty\)-leaderboard as of May 2023
         Y. Bai, B. G. Anderson, A. Kim, and S. Sojoudi

  2. Projected randomized smoothing for certified adversarial robustness, Transactions on Machine Learning Research (TMLR), 2023
         INFORMS Data Mining Best Student Paper Award runner-up
         S. Pfrommer, B. G. Anderson, and S. Sojoudi

  3. Data-driven certification of neural networks with random input noise, IEEE Transactions on Control of Network Systems (TCNS), 2022
         B. G. Anderson and S. Sojoudi

Conference Papers

  1. Approximately Gaussian replicator flows: Nonconvex optimization as a Nash-convergent evolutionary game, 63rd IEEE Conference on Decision and Control (CDC), 2024
         B. G. Anderson, S. Pfrommer, and S. Sojoudi

  2. Transport of algebraic structure to latent embeddings, International Conference on Machine Learning (ICML), 2024
         Spotlight paper (top 3.5% of submissions, top 12.8% of accepted papers)
         S. Pfrommer, B. G. Anderson, and S. Sojoudi

  3. Mixing classifiers to alleviate the accuracy-robustness trade-off, 6th Annual Learning for Dynamics and Control Conference (L4DC), 2024
         Y. Bai, B. G. Anderson, and S. Sojoudi

  4. Asymmetric certified robustness via feature-convex neural networks, Neural Information Processing Systems (NeurIPS), 2023
         First place in INFORMS OR/MS Tomorrow Mini-Poster Competition
         S. Pfrommer*, B. G. Anderson*, J. Piet, and S. Sojoudi

  5. Tight certified robustness via min-max representations of ReLU neural networks, 62nd IEEE Conference on Decision and Control (CDC), 2023
         B. G. Anderson, S. Pfrommer, and S. Sojoudi

  6. An overview and prospective outlook on robust training and certification of machine learning models, IFAC Symposium on System Structure and Control (SSSC), 2022
         B. G. Anderson*, T. Gautam*, and S. Sojoudi

  7. A sequential greedy approach for training implicit deep models, 61st IEEE Conference on Decision and Control (CDC), 2022
         T. Gautam, B. G. Anderson, S. Sojoudi, and L. El Ghaoui

  8. Towards optimal randomized smoothing: A semi-infinite linear programming approach, ICML Workshop on Formal Verification of Machine Learning (WFVML), 2022
         One of six selected for oral presentation
         B. G. Anderson, S. Pfrommer, and S. Sojoudi

  9. Certified robustness via locally biased randomized smoothing, 4th Annual Learning for Dynamics and Control Conference (L4DC), 2022
         B. G. Anderson and S. Sojoudi

  10. Node-variant graph filters in graph neural networks, IEEE Data Science and Learning Workshop (DSLW), 2022
         F. Gama, B. G. Anderson, and S. Sojoudi

  11. Tightened convex relaxations for neural network robustness certification, 59th IEEE Conference on Decision and Control (CDC), 2020
         B. G. Anderson, Z. Ma, J. Li, and S. Sojoudi

  12. Global optimality guarantees for nonconvex unsupervised video segmentation, 57th Annual Allerton Conference on Communication, Control, and Computing, 2019
         B. G. Anderson and S. Sojoudi

  13. Quantitative assessment of robotic swarm coverage, 15th International Conference on Informatics in Control, Automation and Robotics (ICINCO), 2018
         Shortlisted candidate for Best Student Paper Award
         B. G. Anderson, E. Loeser, M. Gee, F. Ren, S. Biswas, O. Turanova, M. Haberland, and A. L. Bertozzi

Book Chapters

  1. Quantifying robotic swarm coverage, Lecture Notes in Electrical Engineering (LNEE), volume 613, pp. 276–301, Springer, 2019
         B. G. Anderson, E. Loeser, M. Gee, F. Ren, S. Biswas, O. Turanova, M. Haberland, and A. L. Bertozzi