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Conference and Workshop Papers
- 2024
[c6]Youngseog Chung, Ian Char, Jeff Schneider:
Sampling-based Multi-dimensional Recalibration. ICML 2024- 2023
- [c5]Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark D. Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff G. Schneider:
Offline Model-Based Reinforcement Learning for Tokamak Control. L4DC 2023: 1357-1372 - [c4]Youngseog Chung, Aaron Rumack, Chirag Gupta:
Parity calibration. UAI 2023: 413-423 - 2021
- [c3]Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson
, Mark D. Boyer, Egemen Kolemen, Jeff G. Schneider:
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CDC 2021: 3735-3742 - [c2]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. NeurIPS 2021: 10971-10984 - 2019
- [c1]Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen:
Offline Contextual Bayesian Optimization. NeurIPS 2019: 4629-4640
Informal and Other Publications
- 2024
- [i13]Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton:
Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing. CoRR abs/2402.08025 (2024) - [i12]Ian Char, Youngseog Chung, Joseph Abbate, Egemen Kolemen, Jeff G. Schneider:
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks. CoRR abs/2404.12416 (2024) - [i11]Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, Jeff G. Schneider, Eduard H. Hovy, Roger B. Grosse, Eric P. Xing:
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions. CoRR abs/2405.13954 (2024) - [i10]Youngseog Chung, Dhruv Malik, Jeff G. Schneider, Yuanzhi Li, Aarti Singh:
Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts. CoRR abs/2409.00879 (2024) - 2023
- [i9]Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour, Pannag Sanketi, Shixiang Shane Gu, Igor Mordatch:
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning. CoRR abs/2303.14870 (2023) - [i8]Youngseog Chung, Aaron Rumack, Chirag Gupta:
Parity Calibration. CoRR abs/2305.18655 (2023) - [i7]Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton:
MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions. CoRR abs/2311.08488 (2023) - [i6]Jacob Tyo, Youngseog Chung, Motolani Olarinre, Zachary C. Lipton:
Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset. CoRR abs/2311.09256 (2023) - 2022
- [i5]Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider:
How Useful are Gradients for OOD Detection Really? CoRR abs/2205.10439 (2022) - 2021
- [i4]Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger:
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification. CoRR abs/2109.10254 (2021) - 2020
- [i3]Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Offline Contextual Bayesian Optimization for Nuclear Fusion. CoRR abs/2001.01793 (2020) - [i2]Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider:
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction. CoRR abs/2006.12682 (2020) - [i1]Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider:
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification. CoRR abs/2011.09588 (2020)
Coauthor Index
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