41 citations · 82 across the 9 of their papers we have counts for
10 papers
Beyond Parameter Count: Implicit Bias in Soft Mixture of Experts
Youngseog Chung, Dhruv Malik, Jeff Schneider +2
The traditional viewpoint on Sparse Mixture of Experts (MoE) models is that instead of training a single large expert, which is computationally expensive, we can train many small e…
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence Functions
Sang Keun Choe, Hwijeen Ahn, Juhan Bae +11
Large language models (LLMs) are trained on a vast amount of human-written data, but data providers often remain uncredited. In response to this issue, data valuation (or data attr…
Full Shot Predictions for the DIII-D Tokamak via Deep Recurrent Networks
Ian Char, Youngseog Chung, Joseph Abbate +2
Although tokamaks are one of the most promising devices for realizing nuclear fusion as an energy source, there are still key obstacles when it comes to understanding the dynamics…
Parity Calibration
Youngseog Chung, Aaron Rumack, Chirag Gupta
In a sequential regression setting, a decision-maker may be primarily concerned with whether the future observation will increase or decrease compared to the current one, rather th…
Bi-Manual Block Assembly via Sim-to-Real Reinforcement Learning
Satoshi Kataoka, Youngseog Chung, Seyed Kamyar Seyed Ghasemipour +3
Most successes in robotic manipulation have been restricted to single-arm gripper robots, whose low dexterity limits the range of solvable tasks to pick-and-place, inser-tion, and…
How Useful are Gradients for OOD Detection Really?
Conor Igoe, Youngseog Chung, Ian Char +1
One critical challenge in deploying highly performant machine learning models in real-life applications is out of distribution (OOD) detection. Given a predictive model which is ac…
Uncertainty Toolbox: an Open-Source Library for Assessing, Visualizing, and Improving Uncertainty Quantification
Youngseog Chung, Ian Char, Han Guo +2
With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common…
Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification
Youngseog Chung, Willie Neiswanger, Ian Char +1
Among the many ways of quantifying uncertainty in a regression setting, specifying the full quantile function is attractive, as quantiles are amenable to interpretation and evaluat…
Neural Dynamical Systems: Balancing Structure and Flexibility in Physical Prediction
Viraj Mehta, Ian Char, Willie Neiswanger +5
We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinar…
Offline Contextual Bayesian Optimization for Nuclear Fusion
Youngseog Chung, Ian Char, Willie Neiswanger +5
Nuclear fusion is regarded as the energy of the future since it presents the possibility of unlimited clean energy. One obstacle in utilizing fusion as a feasible energy source is…