Graduate students

Wonjoon Chang

PhD Student (21.09 ~ )

PhD Student, 2021 Fall – present MS Student, 2019 August at KAIST BS in Information and Industrial Engineering (Double major in Computer Science) from Yonsei University, 2019 (the highest-ranking graduate from department of Information and Industrial Engineering)

PhD Student, 2021 Fall – present
MS Student, 2019 August at KAIST
BS in Information and Industrial Engineering (Double major in Computer Science) from Yonsei University, 2019 (the highest-ranking graduate from department of Information and Industrial Engineering)

4 notes

Research

All research

2025
Causal Interactions for Payoff Allocation
A game-theoretic reformulation makes feature interactions explicit and improves attribution when negative interactions are present.
2025
Rethinking Shapley Value for Negative Interactions in Non-convex Games
We study causal interactions for payoff allocation in cooperative game theory, including quantifying feature attribution for deep learning models. Most feature attribution methods mainly stem from the criteria of the Shapley value, which assigns fair payoffs to players based on their expected contribution in a cooperative game. However, interactions between players in the game do not explicitly appear in the original formulation of the Shapley value. In this work, we reformulate the Shapley value to clarify the role of interactions and discuss implicit assumptions from a game-theoretical perspective. Our theoretical analysis demonstrates that when negative interactions exist—common in deep learning models—the efficiency axiom can lead to the undervaluation of attributions or payoffs. We suggest a new allocation rule that decomposes contributions into interactions and aggregates positive parts for non-convex games. Furthermore, we propose an approximation algorithm to reduce the cost of interaction computation which can be applied to differentiable functions such as deep learning models. Our approach mitigates counterintuitive attribution outcomes observed in existing methods, ensuring that features critical to a model’s decision receive appropriate attribution.
2024
Relaxed Decision Regions
Principal neuron subsets reveal coherent distributed concepts in the intermediate representations of image classifiers.
2024
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision
Understanding intermediate representations of the concepts learned by deep learning classifiers is indispensable for interpreting general model behaviors. In this paper, we propose a novel unsupervised method for discovering distributed representations of concepts by selecting a principal subset of neurons. Our empirical findings demonstrate that instances with similar neuron activation states tend to share coherent concepts. Based on the observations, the proposed method selects principal neurons that construct an interpretable region, namely a Relaxed Decision Region (RDR), encompassing instances with coherent concepts in the feature space.