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Differentially Private Diffusion Models Generate Useful Synthetic Images., , , , , , , , , and . CoRR, (2023)Verification of Non-Linear Specifications for Neural Networks., , , , , , , , and . ICLR (Poster), OpenReview.net, (2019)Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation., , , , , , , and . EMNLP/IJCNLP (1), page 4081-4091. Association for Computational Linguistics, (2019)Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control., , , , , , and . ICLR, OpenReview.net, (2020)Reducing Sentiment Bias in Language Models via Counterfactual Evaluation., , , , , , , , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 65-83. Association for Computational Linguistics, (2020)Towards Stable and Efficient Training of Verifiably Robust Neural Networks., , , , , , , and . ICLR, OpenReview.net, (2020)A Dual Approach to Verify and Train Deep Networks., , , , and . IJCAI, page 6156-6160. ijcai.org, (2019)Scalable Verified Training for Provably Robust Image Classification., , , , , , , , and . ICCV, page 4841-4850. IEEE, (2019)Efficient Neural Network Verification with Exactness Characterization., , , , , and . UAI, volume 115 of Proceedings of Machine Learning Research, page 497-507. AUAI Press, (2019)Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications., , , , , , , and . NeurIPS, page 11136-11147. (2021)