Learning and Selecting Features Jointly with Point-wise Gated Boltzmann Machines. - Kihyuk Sohn; Guanyu Zhou; Chansoo Lee; Honglak Lee (ICML (2), 2013)

Online Linear Optimization via Smoothing. - Jacob D. Abernethy; Chansoo Lee; Abhinav Sinha; Ambuj Tewari (COLT, 2014)

Fighting Bandits with a New Kind of Smoothness. - Jacob D. Abernethy; Chansoo Lee; Ambuj Tewari (NIPS, 2015)

Hardness of Online Sleeping Combinatorial Optimization Problems. - Satyen Kale; Chansoo Lee; Dávid Pál (NIPS, 2016)

Online Learning via Differential Privacy. - Jacob D. Abernethy; Chansoo Lee; Audra McMillan; Ambuj Tewari (CoRR, 2017)

Stern Sequences for a Family of Multidimensional Continued Fractions: TRIP-Stern Sequences. - Ilya Amburg; Krishna Dasaratha; Laure Flapan; Thomas Garrity; Chansoo Lee; Cornelia Mihaila; Nicholas Neumann-Chun; Sarah Peluse; Matthew Stoffregen (J. Integer Seq., 2017)

Constrained-Based Differential Privacy for Mobility Services. - Ferdinando Fioretto; Chansoo Lee; Pascal Van Hentenryck (AAMAS, 2018)

Analysis of Perturbation Techniques in Online Learning. - Chansoo Lee (2018)

Online Learning via the Differential Privacy Lens. - Jacob D. Abernethy; Young Hun Jung; Chansoo Lee; Audra McMillan; Ambuj Tewari (NeurIPS, 2019)

Gradientless Descent: High-Dimensional Zeroth-Order Optimization. - Daniel Golovin; John Karro; Greg Kochanski; Chansoo Lee; Xingyou Song; Qiuyi (Richard) Zhang (ICLR, 2020)

Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers. - Zi Wang; George E. Dahl; Kevin Swersky; Chansoo Lee; Zelda Mariet; Zachary Nado; Justin Gilmer; Jasper Snoek; Zoubin Ghahramani (CoRR, 2021)

Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization. - Xingyou Song; Sagi Perel; Chansoo Lee; Greg Kochanski; Daniel Golovin (AutoML, 2022)

Towards Learning Universal Hyperparameter Optimizers with Transformers. - Yutian Chen 001; Xingyou Song; Chansoo Lee; Zi Wang; Richard Zhang 003; David Dohan; Kazuya Kawakami; Greg Kochanski; Arnaud Doucet; Marc'Aurelio Ranzato; Sagi Perel; Nando de Freitas (NeurIPS, 2022)

Pre-training helps Bayesian optimization too. - Zi Wang; George E. Dahl; Kevin Swersky; Chansoo Lee; Zelda Mariet; Zachary Nado; Justin Gilmer; Jasper Snoek; Zoubin Ghahramani (CoRR, 2022)

Task Selection for AutoML System Evaluation. - Jonathan Lorraine; Nihesh Anderson; Chansoo Lee; Quentin de Laroussilhe; Mehadi Hassen (CoRR, 2022)

Blackbox optimization of unimodal functions. - Ashok Cutkosky; Abhimanyu Das; Weihao Kong; Chansoo Lee; Rajat Sen (UAI, 2023)

Position: Leverage Foundational Models for Black-Box Optimization. - Xingyou Song; Yingtao Tian; Robert Tjarko Lange; Chansoo Lee; Yujin Tang; Yutian Chen 001 (ICML, 2024)

The Vizier Gaussian Process Bandit Algorithm. - Xingyou Song; Qiuyi Zhang 001; Chansoo Lee; Emily Fertig; Tzu-Kuo Huang; Lior Belenki; Greg Kochanski; Setareh Ariafar; Srinivas Vasudevan; Sagi Perel; Daniel Golovin (CoRR, 2024)

Predicting from Strings: Language Model Embeddings for Bayesian Optimization. - Tung Nguyen; Qiuyi Zhang 001; Bangding Yang; Chansoo Lee; Jörg Bornschein; Yingjie Miao; Sagi Perel; Yutian Chen 001; Xingyou Song (CoRR, 2024)

Pre-trained Gaussian Processes for Bayesian Optimization. - Zi Wang; George E. Dahl; Kevin Swersky; Chansoo Lee; Zachary Nado; Justin Gilmer; Jasper Snoek; Zoubin Ghahramani (J. Mach. Learn. Res., 2024)

OmniPred: Language Models as Universal Regressors. - Xingyou Song; Oscar Li; Chansoo Lee; Bangding Yang; Daiyi Peng; Sagi Perel; Yutian Chen 001 (Trans. Mach. Learn. Res., 2024)