Choi, Jaesik

40 publications

AAAI 2025 Diverse Rare Sample Generation with Pretrained GANs Subeen Lee, Jiyeon Han, Soyeon Kim, Jaesik Choi

CVPR 2025 Enhancing Creative Generation on Stable Diffusion-Based Models Jiyeon Han, Dahee Kwon, Gayoung Lee, Junho Kim, Jaesik Choi

ICCV 2025 Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations Dahee Kwon, Sehyun Lee, Jaesik Choi

ICML 2025 Local Manifold Approximation and Projection for Manifold-Aware Diffusion Planning Kyowoon Lee, Jaesik Choi

ICLR 2025 Neural ODE Transformers: Analyzing Internal Dynamics and Adaptive Fine-Tuning Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Duc Nguyen, Toan Tran, David Leo Wright Hall, Cheongwoong Kang, Jaesik Choi

ICLR 2025 Rethinking Shapley Value for Negative Interactions in Non-Convex Games Wonjoon Chang, Myeongjin Lee, Jaesik Choi

NeurIPS 2025 State-Covering Trajectory Stitching for Diffusion Planners Kyowoon Lee, Jaesik Choi

AAAI 2025 xPatch: Dual-Stream Time Series Forecasting with Exponential Seasonal-Trend Decomposition Artyom Stitsyuk, Jaesik Choi

IJCAI 2024 Memorizing Documents with Guidance in Large Language Models Bumjin Park, Jaesik Choi

AISTATS 2024 Pathwise Explanation of ReLU Neural Networks Seongwoo Lim, Won Jo, Joohyung Lee, Jaesik Choi

AAAI 2024 Towards Diverse Perspective Learning with Selection over Multiple Temporal Poolings Jihyeon Seong, Jungmin Kim, Jaesik Choi

IJCAI 2024 Towards Dynamic Trend Filtering Through Trend Point Detection with Reinforcement Learning Jihyeon Seong, Sekwang Oh, Jaesik Choi

AAAI 2024 Understanding Distributed Representations of Concepts in Deep Neural Networks Without Supervision Wonjoon Chang, Dahee Kwon, Jaesik Choi

ICCV 2023 Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling Giyoung Jeon, Haedong Jeong, Jaesik Choi

ICLR 2023 Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha, Jaesik Choi

NeurIPS 2023 Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans Kyowoon Lee, Seongun Kim, Jaesik Choi

ICML 2023 Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills Seongun Kim, Kyowoon Lee, Jaesik Choi

AAAI 2022 An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks Haedong Jeong, Jiyeon Han, Jaesik Choi

IJCAI 2022 Can We Find Neurons That Cause Unrealistic Images in Deep Generative Networks? Hwanil Choi, Wonjoon Chang, Jaesik Choi

NeurIPS 2022 Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network Giyoung Jeon, Haedong Jeong, Jaesik Choi

NeurIPS 2022 Learning Fractional White Noises in Neural Stochastic Differential Equations Anh Tong, Thanh Nguyen-Tang, Toan Tran, Jaesik Choi

CVPR 2021 Automatic Correction of Internal Units in Generative Neural Networks Ali Tousi, Haedong Jeong, Jiyeon Han, Hwanil Choi, Jaesik Choi

AAAI 2021 Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems Anh Tong, Jaesik Choi

ICML 2021 Conditional Temporal Neural Processes with Covariance Loss Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim, Jaesik Choi

AAAI 2021 Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee

AAAI 2021 Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior Anh Tong, Toan M. Tran, Hung Bui, Jaesik Choi

AAAI 2020 An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks Giyoung Jeon, Haedong Jeong, Jaesik Choi

AAAI 2020 Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf, Seong-Whan Lee

IJCAI 2019 Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes Jiyeon Han, Kyowoon Lee, Anh Tong, Jaesik Choi

ICML 2019 Discovering Latent Covariance Structures for Multiple Time Series Anh Tong, Jaesik Choi

ICML 2018 Deep Reinforcement Learning in Continuous Action Spaces: A Case Study in the Game of Simulated Curling Kyowoon Lee, Sol-A Kim, Jaesik Choi, Seong-Whan Lee

ICML 2016 Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series Yunseong Hwang, Anh Tong, Jaesik Choi

UAI 2016 Improving Imprecise Compressive Sensing Models Dongeun Lee, Rafael Lima, Jaesik Choi

IJCAI 2015 A Deterministic Partition Function Approximation for Exponential Random Graph Models Wen Pu, Jaesik Choi, Yunseong Hwang, Eyal Amir

AAAI 2015 Learning Relational Kalman Filtering Jaesik Choi, Eyal Amir, Tianfang Xu, Albert J. Valocchi

UAI 2012 Lifted Relational Variational Inference Jaesik Choi, Eyal Amir

AAAI 2011 Efficient Methods for Lifted Inference with Aggregate Factors Jaesik Choi, Rodrigo de Salvo Braz, Hung Hai Bui

IJCAI 2011 Lifted Relational Kalman Filtering Jaesik Choi, Abner Guzmán-Rivera, Eyal Amir

UAI 2010 Lifted Inference for Relational Continuous Models Jaesik Choi, Eyal Amir, David J. Hill

IJCAI 2009 Greedy Algorithms for Sequential Sensing Decisions Hannaneh Hajishirzi, Afsaneh Shirazi, Jaesik Choi, Eyal Amir