Youngseog Chung

According to our database, Youngseog Chung authored at least 16 papers between 2019 and 2024.

Collaborative distances:

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

2024

Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts.

Youngseog Chung, Dhruv Malik, Jeff G. Schneider, Yuanzhi Li, Aarti Singh

CoRR, 2024

What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions.

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

CoRR, 2024

Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks.

Ian Char, Youngseog Chung, Joseph Abbate, Egemen Kolemen, Jeff G. Schneider

CoRR, 2024

Beyond the Mud: Datasets and Benchmarks for Computer Vision in Off-Road Racing.

Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton

CoRR, 2024

Sampling-based Multi-dimensional Recalibration.

Youngseog Chung, Ian Char, Jeff Schneider

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Reading Between the Mud: A Challenging Motorcycle Racer Number Dataset.

Jacob Tyo, Youngseog Chung, Motolani Olarinre, Zachary C. Lipton

CoRR, 2023

MUDD: A New Re-Identification Dataset with Efficient Annotation for Off-Road Racers in Extreme Conditions.

Jacob Tyo, Motolani Olarinre, Youngseog Chung, Zachary C. Lipton

CoRR, 2023

Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning.

Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour, Pannag Sanketi, Shixiang Shane Gu, Igor Mordatch

CoRR, 2023

Parity calibration.

Youngseog Chung, Aaron Rumack, Chirag Gupta

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Offline Model-Based Reinforcement Learning for Tokamak Control.

Ian Char, Joseph Abbate, Laszlo Bardoczi, Mark D. Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff G. Schneider

Proceedings of the Learning for Dynamics and Control Conference, 2023

2022

How Useful are Gradients for OOD Detection Really?

Conor Igoe, Youngseog Chung, Ian Char, Jeff Schneider

CoRR, 2022

2021

Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification.

Youngseog Chung, Ian Char, Han Guo, Jeff Schneider, Willie Neiswanger

CoRR, 2021

Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification.

Youngseog Chung, Willie Neiswanger, Ian Char, Jeff Schneider

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction.

Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff G. Schneider

Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021

2020

Offline Contextual Bayesian Optimization for Nuclear Fusion.

Youngseog Chung, Ian Char, Willie Neiswanger, Kirthevasan Kandasamy, Andrew Oakleigh Nelson, Mark D. Boyer, Egemen Kolemen, Jeff Schneider

CoRR, 2020

2019

Offline Contextual Bayesian Optimization.

Ian Char, Youngseog Chung, Willie Neiswanger, Kirthevasan Kandasamy, Oak Nelson, Mark D. Boyer, Egemen Kolemen

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019