My research focuses on the theory of machine learning and optimization. I have served as a reviewer for NeurIPS 2024 and ICLR 2025 and a volunteer at ISIT 2023. I enjoy learning mathematics, computer science, and natural sciences in general.
Papers below are classified by topics and sorted in the chronological order.
Please check my Google scholar profile for an up-to-date list.
Learning and optimization with logarithmic losses
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Online self-concordant and relatively smooth minimization, with applications to online portfolio selection and learning quantum states Chung-En Tsai, Hao-Chung Cheng, and Yen-Huan Li ALT 2023
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Faster stochastic first-order method for maximum-likelihood quantum state tomography Chung-En Tsai, Hao-Chung Cheng, and Yen-Huan Li QIP 2023
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Data-dependent bounds for online portfolio selection without Lipschitzness and smoothness Chung-En Tsai, Ying-Ting Lin, and Yen-Huan Li NeurIPS 2023, NOPTA 2024
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Fast minimization of expected logarithmic loss via stochastic dual averaging Chung-En Tsai, Hao-Chung Cheng, and Yen-Huan Li AISTATS 2024
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Computing Augustin information via hybrid geodesically convex optimization Guan-Ren Wang, Chung-En Tsai, Hao-Chung Cheng, and Yen-Huan Li ISIT 2024, NOPTA 2024
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Linear convergence in Hilbert's projective metric for computing Augustin information and a Rényi information measure Chung-En Tsai*, Guan-Ren Wang*, Hao-Chung Cheng, and Yen-Huan Li
Other topics
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On the synchronization analysis of a strong competition Kuramoto model Chun-Hsiung Hsia and Chung-En Tsai
Notes
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