Publication
2024
Retrieval-Augmented Generation with Estimation of Source Reliability
Jeongyeon Hwang, Junyoung Park, Hyejin Park, Sangdon Park, Jungseul Ok
2024
[arXiv]
Holistic Unlearning Benchmark: A Multi-Faceted Evaluation for Text-to-Image Diffusion Model Unlearning
Saemi Moon*, Minjong Lee*, Sangdon Park, and Dongwoo Kim (* indicates equal contribution)
2024
[arXiv]
Selective Generation for Controllable Language Models
Minjae Lee*, Kyungmin Kim*, Taesoo Kim, and Sangdon Park (* indicates equal contribution)
Neural Information Processing Systems (NeurIPS) 2024
Spotlight Paper (top 2.08%)
[Paper]
TRAQ: Trustworthy Retrieval Augmented Question Answering via Conformal Prediction
Shuo Li, Sangdon Park, Insup Lee, and Osbert Bastani
North American Chapter of the Association for Computational Linguistics (NAACL) 2024
Best Paper Award at ICML23 TEACH Workshop
[arXiv], [ICML23 TEACH Workshop]
MedBN: Robust Test Time Adaptation against Malicious Test Samples
Hyejin Park, Jeongyeon Hwang, Sunung Mun, Sangdon Park, and Jungseul Ok
Computer Vision and Pattern Recognition (CVPR) 2024
[arXiv]
PAC Prediction Sets Under Label Shift
Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban, and Osbert Bastani
International Conference on Learning Representations (ICLR) 2024
[arXiv]
2023
Angelic Patches for Improving Third-Party Object Detector Performance
Wenwen Si, Shuo Li, Sangdon Park, Insup Lee, and Osbert Bastani
Computer Vision and Pattern Recognition (CVPR) 2023
[paper]
ACon²: Adaptive Conformal Consensus for Provable Blockchain Oracles
Sangdon Park, Osbert Bastani, and Taesoo Kim
USENIX Security Symposium (Security) 2023
[arXiv] [Paper] [Code]
CODiT: Conformal Out-of-distribution Detection in Time-series Data for Cyber-Physical Systems
Ramneet Kaur, Kaustubh Sridhar, Sangdon Park, Yahan Yang, Susmit Jha, Anirban Roy, Oleg Sokolsky, and Insup Lee
International Conference on Cyber-Physical Systems (ICCPS) 2023
Best Paper Award Finalist
[paper]
2022
Unsafe’s Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via Machine Learning
Sangdon Park, Xiang Cheng, and Taesoo Kim
2022
[arXiv]
PAC Prediction Sets for Meta-Learning
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani
Neural Information Processing Systems (NeurIPS) 2022
[arXiv] [Paper] [Code] [Video]
Sequential Covariate Shift Detection Using Classifier Two-Sample Tests
Sooyong Jang, Sangdon Park, Insup Lee, and Osbert Bastani
International Conference on Machine Learning (ICML) 2022
[paper]
Towards PAC Multi-Object Detection and Tracking
Shuo Li, Sangdon Park, Xiayan Ji, Insup Lee, Osbert Bastani
2022
[arXiv]
PAC Prediction Sets Under Covariate Shift
Sangdon Park, Edgar Dobriban, Insup Lee, and Osbert Bastani
International Conference on Learning Representations (ICLR) 2022
[arXiv] [Paper] [Code] [Video]
iDECODe: In-distribution Equivariance for Conformal Out-of-distribution Detection
Ramneet Kaur, Susmit Jha, Anirban Roy, Sangdon Park, Edgar Dobriban, Oleg Sokolsky, and Insup Lee
Association for the Advancement of Artificial Intelligence (AAAI) 2022
[arXiv] [Paper]
2021
Uncertainty Estimation Toward Safe AI
PhD thesis, UPenn, Aug. 2021
[Thesis]
PAC Confidence Predictions for Deep Neural Network Classifiers
Sangdon Park, Shuo Li, Insup Lee, and Osbert Bastani
International Conference on Learning Representations (ICLR) 2021
[arXiv] [Paper] [Code] [Video]
2020 and before
Calibrated Predictions with Covariate Shift via Unsupervised Domain Adaptation
Sangdon Park, Osbert Bastani, James Weimer, and Insup Lee
International Conference on Artificial Intelligence and Statistics (AISTATS) 2020
[arXiv] [Paper] [Code] [Video]
PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
Sangdon Park, Osbert Bastani, Nikolai Matni, and Insup Lee
International Conference on Learning Representations (ICLR) 2020
[arXiv] [Paper] [Code] [Video] [Short, ICML UDL20]
From Verification to Learning for Defense against Adversarial Examples in Neural Networks
Sangdon Park, Radoslav Ivanov, James Weimer, and Insup Lee
Korea Cyber-security Competition 2018
Best Paper Award
Resilient Linear Classification: An Approach to Deal with Attacks on Training Data
Sangdon Park, James Weimer, and Insup Lee
International Conference on Cyber-Physical Systems (ICCPS) 2017
[Paper] [arXiv] [BibTex] [DOI]
Integrated Intelligence for Human Robot Teams
Jean Oh, Thomas Howard, Matthew Walter, Daniel Barber, Menglong Zhu, Sangdon Park, Arne Suppe, Luis NavarroSerment, Felix Duvallet, Abdeslam Boularias, Oscar Romero, Jerry Vinokurov, Terence Keegan, Robert Dean, Craig Lennon, Barry Bodt, Marshal Childers, Jianbo Shi, Kostas Daniilidis, Nicholas Roy, Christian Lebiere, Martial Hebert, and Anthony Stentz
International Symposium on Experimental Robotics (ISER) 2016
[Paper] [DOI]
Abnormal Object Detection by Transformed-Canonical Scene Generation
MS thesis, Seoul National University, Aug. 2012
Distinguished Dissertation Award
Abnormal Object Detection by Canonical Scene-based Contextual Model
Sangdon Park, Wonsik Kim, and Kyoung Mu Lee
European Conference on Computer Vision (ECCV) 2012
[Project Page] [Paper] [BibTex] [Code] [Dataset]
Behavioral Intelligence for Crowd Avatar in 3D Virtual Worlds
BS thesis, Seoul National University, Feb. 2010