27 citations · 43 across the 9 of their papers we have counts for
9 papers
Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
Sajani Vithana, Sangwon Jung, Haoyang Hu +3
Differential privacy (DP) imposes fundamental trade-offs between privacy and statistical fidelity in synthetic data generation. While access to public data has been shown to improv…
Do Counterfactually Fair Image Classifiers Satisfy Group Fairness? -- A Theoretical and Empirical Study
Sangwon Jung, Sumin Yu, Sanghyuk Chun +1
The notion of algorithmic fairness has been actively explored from various aspects of fairness, such as counterfactual fairness (CF) and group fairness (GF). However, the exact rel…
Multi-Group Proportional Representation for Text-to-Image Models
Sangwon Jung, Alex Oesterling, Claudio Mayrink Verdun +3
Text-to-image (T2I) generative models can create vivid, realistic images from textual descriptions. As these models proliferate, they expose new concerns about their ability to rep…
Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
Heewoong Choi, Sangwon Jung, Hongjoon Ahn +1
In Reinforcement Learning (RL), designing precise reward functions remains to be a challenge, particularly when aligning with human intent. Preference-based RL (PbRL) was introduce…
Continual Learning in the Presence of Spurious Correlation
Donggyu Lee, Sangwon Jung, Taesup Moon
Most continual learning (CL) algorithms have focused on tackling the stability-plasticity dilemma, that is, the challenge of preventing the forgetting of previous tasks while learn…
Re-weighting Based Group Fairness Regularization via Classwise Robust Optimization
Sangwon Jung, Taeeon Park, Sanghyuk Chun +1
Many existing group fairness-aware training methods aim to achieve the group fairness by either re-weighting underrepresented groups based on certain rules or using weakly approxim…
Dataset Condensation with Contrastive Signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung +2
Recent studies have demonstrated that gradient matching-based dataset synthesis, or dataset condensation (DC), methods can achieve state-of-the-art performance when applied to data…
Learning Fair Classifiers with Partially Annotated Group Labels
Sangwon Jung, Sanghyuk Chun, Taesup Moon
Recently, fairness-aware learning have become increasingly crucial, but most of those methods operate by assuming the availability of fully annotated demographic group labels. We e…
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
Sangwon Jung, Hongjoon Ahn, Sungmin Cha +1
We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties. Ou…