Certifiably Robust Interpretation in Deep Learning. - Alexander Levine 001; Sahil Singla 002; Soheil Feizi (CoRR, 2019)

Robustness Certificates for Sparse Adversarial Attacks by Randomized Ablation. - Alexander Levine 001; Soheil Feizi (AAAI, 2020)

Wasserstein Smoothing: Certified Robustness against Wasserstein Adversarial Attacks. - Alexander Levine 001; Soheil Feizi (AISTATS, 2020)

Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness. - Aounon Kumar; Alexander Levine 001; Tom Goldstein; Soheil Feizi (ICML, 2020)

(De)Randomized Smoothing for Certifiable Defense against Patch Attacks. - Alexander Levine 001; Soheil Feizi (NeurIPS, 2020)

Certifying Confidence via Randomized Smoothing. - Aounon Kumar; Alexander Levine 001; Soheil Feizi; Tom Goldstein (NeurIPS, 2020)

Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks. - Wei-An Lin; Chun Pong Lau 001; Alexander Levine 001; Rama Chellappa; Soheil Feizi (NeurIPS, 2020)

Deep Partition Aggregation: Provable Defense against General Poisoning Attacks. - Alexander Levine 001; Soheil Feizi (CoRR, 2020)

Tight Second-Order Certificates for Randomized Smoothing. - Alexander Levine 001; Aounon Kumar; Thomas A. Goldstein; Soheil Feizi (CoRR, 2020)

Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks. - Alexander Levine 001; Soheil Feizi (ICLR, 2021)

Improved, Deterministic Smoothing for L1 Certified Robustness. - Alexander Levine 001; Soheil Feizi (ICML, 2021)

Improved, Deterministic Smoothing for L1 Certified Robustness. - Alexander Levine 001; Soheil Feizi (CoRR, 2021)

Provable Adversarial Robustness for Fractional Lp Threat Models. - Alexander Levine 001; Soheil Feizi (AISTATS, 2022)

Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection. - Jiang Liu 014; Alexander Levine 001; Chun Pong Lau 001; Rama Chellappa; Soheil Feizi (CVPR, 2022)

Policy Smoothing for Provably Robust Reinforcement Learning. - Aounon Kumar; Alexander Levine 001; Soheil Feizi (ICLR, 2022)

Improved Certified Defenses against Data Poisoning with (Deterministic) Finite Aggregation. - Wenxiao Wang 002; Alexander Levine 001; Soheil Feizi (ICML, 2022)

Lethal Dose Conjecture on Data Poisoning. - Wenxiao Wang 002; Alexander Levine 001; Soheil Feizi (NeurIPS, 2022)

Certifying Model Accuracy under Distribution Shifts. - Aounon Kumar; Alexander Levine 001; Tom Goldstein; Soheil Feizi (CoRR, 2022)

Invariant Learning via Diffusion Dreamed Distribution Shifts. - Priyatham Kattakinda; Alexander Levine 001; Soheil Feizi (CoRR, 2022)

Goal-Conditioned Q-learning as Knowledge Distillation. - Alexander Levine 001; Soheil Feizi (AAAI, 2023)

Provable Robustness against Wasserstein Distribution Shifts via Input Randomization. - Aounon Kumar; Alexander Levine 001; Tom Goldstein; Soheil Feizi (ICLR, 2023)

Scalable Methods for Robust Machine Learning. - Alexander Levine 001 (2023)

Multistep Inverse Is Not All You Need. - Alexander Levine 001; Peter Stone 001; Amy Zhang 001 (RLJ, 2024)

Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory. - Alexander Levine 001; Peter Stone 001; Amy Zhang 001 (ICLR, 2025)

Offline Action-Free Learning of Ex-BMDPs by Comparing Diverse Datasets. - Alexander Levine 001; Peter Stone 001; Amy Zhang 001 (CoRR, 2025)