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publications
Immuno-mimetic Deep Neural Networks (Immuno-Net)
Published in The 2021 ICML Workshop on Computational Biology, 2021
In this paper we introduce a different type of biomimetic model, one that borrows concepts from the immune system, for designing robust deep neural networks.
Recommended citation: R. Wang, T. Chen, S. Lindsly, C. Stansbury, I. Rajapakse, and A. Hero. Immuno-mimetic deep neural networks (immuno-net). arXiv preprint arXiv:2107.02842, 2021. https://icml-compbio.github.io/2021/papers/WCBICML2021_paper_34.pdf
RAILS: A Robust Adversarial Immune-Inspired Learning System
Published in IEEE Access, 2022
We develop a novel adversarial defense framework inspired by the adaptive immune system: the Robust Adversarial Immune-inspired Learning System (RAILS)
Recommended citation: R. Wang, T. Chen, S. M. Lindsly, C. M. Stansbury, A. Rehemtulla, I. Rajapakse, and A. O. Hero. RAILS: A Robust Adversarial Immune-Inspired Learning System. IEEE Access, 10:22061–22078, 2022 https://ieeexplore.ieee.org/document/9718107
ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense
Published in IEEE Access, 2022
In this work, we first introduce an information-theoretic surrogate loss for DkNN-based classification, based upon which we then propose an attack algorithm and a defense algorithm achieve SOTA adversarial results on DkNN-based models.
Recommended citation: R. Wang, T. Chen, P. Yao, S. Liu, I. Rajapakse, and A. O. Hero. Ask: Adversarial soft k-nearest neighbor attack and defense. IEEE Access, 10:103074–103088, 2022. https://ieeexplore.ieee.org/document/9902964
Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling
Published in Proceedings of Machine Learning Research, 2023
We propose a binomial/Poisson-based hierarchical variational autoencoding framework that are well suited for modeling non-standard non-negative distributions that exhibit sparsity, skewedness, heavy-tailedness and/or heterogeneity.
Recommended citation: T. Chen and M. Zhou. Learning to Jump: Thinning and Thickening Latent Counts for Generative Modeling. In Proceedings of the 40th International Conference on Machine Learning (ICML), pages 5367–5382. PMLR, 2023 https://proceedings.mlr.press/v202/chen23ap.html
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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