Kwan Ho Ryan Chan

According to our database, Kwan Ho Ryan Chan authored at least 18 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to Jailbreak LLMs.

Buyun Liang, Kwan Ho Ryan Chan, Darshan Thaker, Jinqi Luo, René Vidal

CoRR, February, 2025

2024

Do LLMs "know" internally when they follow instructions?

Juyeon Heo, Christina Heinze-Deml, Oussama Elachqar, Shirley You Ren, Udhay Nallasamy, Andy Miller, Kwan Ho Ryan Chan, Jaya Narain

CoRR, 2024

PaCE: Parsimonious Concept Engineering for Large Language Models.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan, Darshan Thaker, Aditya Chattopadhyay, Chris Callison-Burch, René Vidal

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Bootstrapping Variational Information Pursuit with Large Language and Vision Models for Interpretable Image Classification.

Aditya Chattopadhyay, Kwan Ho Ryan Chan, René Vidal

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Learning Interpretable Queries for Explainable Image Classification with Information Pursuit.

Stefan Kolek, Aditya Chattopadhyay, Kwan Ho Ryan Chan, Héctor Andrade-Loarca, Gitta Kutyniok, René Vidal

CoRR, 2023

Knowledge Pursuit Prompting for Zero-Shot Multimodal Synthesis.

Jinqi Luo, Kwan Ho Ryan Chan, Dimitris Dimos, René Vidal

CoRR, 2023

Variational Information Pursuit with Large Language and Multimodal Models for Interpretable Predictions.

Kwan Ho Ryan Chan, Aditya Chattopadhyay, Benjamin David Haeffele, René Vidal

CoRR, 2023

Variational Information Pursuit for Interpretable Predictions.

Aditya Chattopadhyay, Kwan Ho Ryan Chan, Benjamin David Haeffele, Donald Geman, René Vidal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Unsupervised Manifold Linearizing and Clustering.

Tianjiao Ding, Shengbang Tong, Kwan Ho Ryan Chan, Xili Dai, Yi Ma, Benjamin D. Haeffele

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

ReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction.

Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma

J. Mach. Learn. Res., 2022

Dynamic modeling of hospitalized COVID-19 patients reveals disease state-dependent risk factors.

Braden C. Soper, Jose Cadena, Sam Nguyen, Kwan Ho Ryan Chan, Paul Kiszka, Lucas Aaron Womack, Mark Work, Joan M. Duggan, Steven T. Haller, Jennifer A. Hanrahan, David J. Kennedy, Deepa Mukundan, Priyadip Ray

J. Am. Medical Informatics Assoc., 2022

CTRL: Closed-Loop Transcription to an LDR via Minimaxing Rate Reduction.

Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Michael Psenka, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Xiaojun Yuan, Heung-Yeung Shum, Yi Ma

Entropy, 2022

Efficient Maximal Coding Rate Reduction by Variational Forms.

Christina Baek, Ziyang Wu, Kwan Ho Ryan Chan, Tianjiao Ding, Yi Ma, Benjamin D. Haeffele

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022

2021

Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction.

Xili Dai, Shengbang Tong, Mingyang Li, Ziyang Wu, Kwan Ho Ryan Chan, Pengyuan Zhai, Yaodong Yu, Michael Psenka, Xiaojun Yuan, Heung-Yeung Shum, Yi Ma

CoRR, 2021

End-to-End Framework for Imputation and State Discovery in Longitudinal Energy Data.

Alexander Ladd, Kwan Ho Ryan Chan, Sam Nguyen, Jose Cadena, Brenda Ng

Proceedings of the e-Energy '21: The Twelfth ACM International Conference on Future Energy Systems, Virtual Event, Torino, Italy, 28 June, 2021

2020

Deep Networks from the Principle of Rate Reduction.

Kwan Ho Ryan Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, Yi Ma

CoRR, 2020

Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction.

Yaodong Yu, Kwan Ho Ryan Chan, Chong You, Chaobing Song, Yi Ma

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Subspace Locally Competitive Algorithms.

Dylan M. Paiton, Steven Shepard, Kwan Ho Ryan Chan, Bruno A. Olshausen

Proceedings of the NICE '20: Neuro-inspired Computational Elements Workshop, 2020