Training Neural Machines with Trace-Based Supervision. - Matthew Mirman; Dimitar Dimitrov 004; Pavle Djordjevic; Timon Gehr; Martin T. Vechev (ICML, 2018)

Differentiable Abstract Interpretation for Provably Robust Neural Networks. - Matthew Mirman; Timon Gehr; Martin T. Vechev (ICML, 2018)

Fast and Effective Robustness Certification. - Gagandeep Singh 001; Timon Gehr; Matthew Mirman; Markus Püschel; Martin T. Vechev (NeurIPS, 2018)

AI2: Safety and Robustness Certification of Neural Networks with Abstract Interpretation. - Timon Gehr; Matthew Mirman; Dana Drachsler-Cohen; Petar Tsankov; Swarat Chaudhuri; Martin T. Vechev (IEEE Symposium on Security and Privacy, 2018)

A Provable Defense for Deep Residual Networks. - Matthew Mirman; Gagandeep Singh 001; Martin T. Vechev (CoRR, 2019)

Online Robustness Training for Deep Reinforcement Learning. - Marc Fischer 002; Matthew Mirman; Steven Stalder; Martin T. Vechev (CoRR, 2019)

Universal Approximation with Certified Networks. - Maximilian Baader; Matthew Mirman; Martin T. Vechev (ICLR, 2020)

Robustness Certification of Generative Models. - Matthew Mirman; Timon Gehr; Martin T. Vechev (CoRR, 2020)

Robustness certification with generative models. - Matthew Mirman; Alexander Hägele; Pavol Bielik; Timon Gehr; Martin T. Vechev (PLDI, 2021)

The Fundamental Limits of Interval Arithmetic for Neural Networks. - Matthew Mirman; Maximilian Baader; Martin T. Vechev (CoRR, 2021)

The Fundamental Limits of Neural Networks for Interval Certified Robustness. - Matthew Mirman; Maximilian Baader; Martin T. Vechev (Trans. Mach. Learn. Res., 2022)

Certified Deep Learning: Verification and Training. - Matthew Mirman (2022)

LLM Guided Inductive Inference for Solving Compositional Problems. - Abhigya Sodani; Lauren Moos; Matthew Mirman (CoRR, 2023)

Ten simple rules for using large language models in science, version 1.0. - Gabriel Reuben Smith; Carolina Bello; Lalasia Bialic-Murphy; Emily Clark; Camille S. Delavaux; Camille Fournier de Lauriere; Johan van den Hoogen; Thomas Lauber; Haozhi Ma; Daniel S. Maynard; Matthew Mirman; Lidong Mo; et al. (PLoS Comput. Biol., 2024)