2 citations · 3 across the 7 of their papers we have counts for
6 papers · 1 filter
Expert Routing with Synthetic Data for Continual Learning
Yewon Byun, Sanket Vaibhav Mehta, Saurabh Garg +4
In many real-world settings, regulations and economic incentives permit the sharing of models but not data across institutional boundaries. In such scenarios, practitioners might h…
Utility-Directed Conformal Prediction: A Decision-Aware Framework for Actionable Uncertainty Quantification
Santiago Cortes-Gomez, Carlos Patiño, Yewon Byun +3
Interest has been growing in decision-focused machine learning methods which train models to account for how their predictions are used in downstream optimization problems. Doing s…
Decision-Focused Evaluation of Worst-Case Distribution Shift
Kevin Ren, Yewon Byun, Bryan Wilder
Distribution shift is a key challenge for predictive models in practice, creating the need to identify potentially harmful shifts in advance of deployment. Existing work typically…
ET tu, CLIP? Addressing Common Object Errors for Unseen Environments
Ye Won Byun, Cathy Jiao, Shahriar Noroozizadeh +2
We introduce a simple method that employs pre-trained CLIP encoders to enhance model generalization in the ALFRED task. In contrast to previous literature where CLIP replaces the v…
Auditing Fairness under Unobserved Confounding
Yewon Byun, Dylan Sam, Michael Oberst +2
Many definitions of fairness or inequity involve unobservable causal quantities that cannot be directly estimated without strong assumptions. For instance, it is particularly diffi…
Bayesian Neural Networks with Domain Knowledge Priors
Dylan Sam, Rattana Pukdee, Daniel P. Jeong +2
Bayesian neural networks (BNNs) have recently gained popularity due to their ability to quantify model uncertainty. However, specifying a prior for BNNs that captures relevant doma…