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Chen Chen

Alumni

Publications

Does a Hybrid Space-Aware Randomized Defense Improve Empirical and Certified Adversarial Robustness?
Joy Dhar
Manish Kumar Pandey
Behzad Bozorgtabar
Nayyar Zaidi
Wenyu Zhang
Wei-Hong Li
Tingting Mu
Dwarikanath Mahapatra
Mahsa Baktashmotlagh
Trung Le
Chen Chen
Sajib Mistry
Camila Gonzalez
Samira Ebrahimi Kahou
Lina Yao
Piotr Koniusz
Robert Burns Fisher
Dinh Phung
Bohyung Han
Nuno Vasconcelos … (see 1 more)
Pietro Lio
We introduce Hybrid Space-aware Stochastic Convolution Attention Noise (HySCAN), a hybrid randomized defense that helps close the long-stand… (see more)ing gap between provable robustness under ℓ2 certificates and empirical robustness against strong ℓ∞ attacks, while maintaining strong generalization across diverse imaging benchmarks. HySCAN jointly explores complementary sources of stochasticity at both training and inference: (i) implicit weight-space randomness via stochastic-aware Random Weights, and (ii) explicit feature-space randomness via Stochastic Attention Noise Injection modules. By incorporating randomness at both the parameter and representation levels, HySCAN enables meaningful certified guarantees while improving empirical robustness in practice. Comprehensive experiments on diverse imaging datasets, e.g., CelebA, CIFAR-10, and CIFAR-100, ImageNet-1k, HAM10000, and NIH Chest X-ray, demonstrate that HySCAN outperforms existing certified and empirical defenses, improving certified robustness by up to ≈ 9.6% and empirical robustness by up to ≈ 5% without reducing clean accuracy.
2026-04-29
International Conference on Machine Learning (accepted)
openreview.net
openreview.net
The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning
Donghang Wu
Tianyu Zhang
Yuxin Li
Hexin Liu
Chen Chen
Eng Siong Chng
Yoshua Bengio
During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal… (see more) cognitive processing may not always manifest as explicit linguistic structures, it is instrumental in formulating high-quality responses. Inspired by this cognitive phenomenon, we propose a novel **F**ull-duplex **LA**tent and **I**nternal **R**easoning method named FLAIR that conducts *latent* thinking simultaneously with speech perception. Unlike conventional "thinking" mechanisms in NLP, which require post-hoc generation, our approach aligns seamlessly with spoken dialogue systems: during the user’s speaking phase, it recursively feeds the latent embedding output from the previous step into the next step, enabling continuous reasoning that strictly adheres to causality without introducing additional latency. To enable this latent reasoning, we design an Evidence Lower Bound-based objective that supports efficient supervised finetuning via teacher forcing, circumventing the need for explicit reasoning annotations. Experiments demonstrate the effectiveness of this think-while-listening design, which achieves competitive results on a range of speech benchmarks. Furthermore, FLAIR robustly handles conversational dynamics and attains competitive performance on full-duplex interaction metrics.
2025-12-31
International Conference on Machine Learning (Accept (regular))
doi.org
openreview.net