Lazy Abstraction-Based Control for Safety Specifications. - Kyle Hsu; Rupak Majumdar; Kaushik Mallik; Anne-Kathrin Schmuck (CDC, 2018)

Multi-Layered Abstraction-Based Controller Synthesis for Continuous-Time Systems. - Kyle Hsu; Rupak Majumdar; Kaushik Mallik; Anne-Kathrin Schmuck (HSCC, 2018)

Lazy Abstraction-Based Control for Reachability. - Kyle Hsu; Rupak Majumdar; Kaushik Mallik; Anne-Kathrin Schmuck (CoRR, 2018)

Lazy Abstraction-Based Controller Synthesis. - Kyle Hsu; Rupak Majumdar; Kaushik Mallik; Anne-Kathrin Schmuck (ATVA, 2019)

Unsupervised Learning via Meta-Learning. - Kyle Hsu; Sergey Levine; Chelsea Finn (ICLR (Poster), 2019)

Unsupervised Curricula for Visual Meta-Reinforcement Learning. - Allan Jabri; Kyle Hsu; Abhishek Gupta 004; Ben Eysenbach; Sergey Levine; Chelsea Finn (NeurIPS, 2019)

On the role of data in PAC-Bayes bounds. - Gintare Karolina Dziugaite; Kyle Hsu; Waseem Gharbieh; Daniel M. Roy 001 (CoRR, 2020)

On the role of data in PAC-Bayes. - Gintare Karolina Dziugaite; Kyle Hsu; Waseem Gharbieh; Gabriel Arpino; Daniel M. Roy 001 (AISTATS, 2021)

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. - Guodong Zhang 006; Kyle Hsu; Jianing Li; Chelsea Finn; Roger B. Grosse (NeurIPS, 2021)

On the Opportunities and Risks of Foundation Models. - Rishi Bommasani; Drew A. Hudson; Ehsan Adeli 001; Russ B. Altman; Simran Arora; Sydney von Arx; Michael S. Bernstein; Jeannette Bohg; Antoine Bosselut; Emma Brunskill; Erik Brynjolfsson; Shyamal Buch; et al. (CoRR, 2021)

Vision-Based Manipulators Need to Also See from Their Hands. - Kyle Hsu; Moo Jin Kim; Rafael Rafailov; Jiajun Wu 001; Chelsea Finn (ICLR, 2022)

Extending Functionalities on a Web-based Portal for Research Computing. - Duy Pham; Kyle Hsu; Marinus Pennings; Tri Pham; Phi Au (PEARC, 2022)

Disentanglement via Latent Quantization. - Kyle Hsu; William Dorrell; James C. R. Whittington; Jiajun Wu 001; Chelsea Finn (NeurIPS, 2023)

Evaluating Real-World Robot Manipulation Policies in Simulation. - Xuanlin Li; Kyle Hsu; Jiayuan Gu; Oier Mees; Karl Pertsch; Homer Rich Walke; Chuyuan Fu; Ishikaa Lunawat; Isabel Sieh; Sean Kirmani; Sergey Levine; Jiajun Wu 001; et al. (CoRL, 2024)

Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning. - Kyle Hsu; Jubayer Ibn Hamid; Kaylee Burns; Chelsea Finn; Jiajun Wu 001 (ICML, 2024)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. - Abby O'Neill; Abdul Rehman; Abhiram Maddukuri; Abhishek Gupta 004; Abhishek Padalkar; Abraham Lee; Acorn Pooley; Agrim Gupta; Ajay Mandlekar; Ajinkya Jain; Albert Tung; Alex Bewley; et al. (ICRA, 2024)

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset. - Alexander Khazatsky; Karl Pertsch; Suraj Nair 003; Ashwin Balakrishna; Sudeep Dasari; Siddharth Karamcheti; Soroush Nasiriany; Mohan Kumar Srirama; Lawrence Yunliang Chen; Kirsty Ellis; Peter David Fagan; Joey Hejna; et al. (Robotics: Science and Systems, 2024)

Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations. - Will Dorrell; Kyle Hsu; Luke Hollingsworth; Jin Hwa Lee; Jiajun Wu 001; Chelsea Finn; Peter E. Latham; Timothy Edward John Behrens; James C. R. Whittington (CoRR, 2024)

Range, not Independence, Drives Modularity in Biologically Inspired Representations. - Will Dorrell; Kyle Hsu; Luke Hollingsworth; Jin Hwa Lee; Jiajun Wu 001; Chelsea Finn; Peter E. Latham; Timothy Edward John Behrens; James C. R. Whittington (ICLR, 2025)

FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. - Anikait Singh; Sheryl Hsu; Kyle Hsu; Eric Mitchell; Stefano Ermon; Tatsunori Hashimoto; Archit Sharma; Chelsea Finn (CoRR, 2025)

Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization. - Kyle Sargent; Kyle Hsu; Justin Johnson 001; Li Fei-Fei 001; Jiajun Wu 001 (CoRR, 2025)