• Year  2024
• Year  2023
• Year  2022
• Year  2021
• Year  2020
• Year  2010~2019
• Year  2005~2009
• Preprint

[84] Luca Longo, Mario Brcic, Federico Cabitza, Jaesik Choi, Roberto Confalonieri, Javier Del Ser, Riccardo Guidotti, Yoichi Hayashi, Francisco Herrera, Andreas Holzinger, Richard Jiang, Hassan Khosravi, Freddy Lecue, Gianclaudio Malgieri, Andrés Páez, Wojciech Samek, Johannes Schneider, Timo Speith and Simone Stumpf,
Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions,
Information Fusion, 2024.

[83] Seongwoo Lim, Won Jo, Joohyung Lee and Jaesik Choi,
Pathwise Explanation of ReLU Neural Networks,
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024.

[82] Jihyeon Seong, Jungmin Kim and Jaesik Choi,
Towards Diverse Perspective Learning with Select over Multiple Temporal Poolings,
AAAI Conference on Artificial Intelligence (AAAI), 2024.

[81] Wonjoon Chang, Dahee Kwon and Jaesik Choi,
Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision,
AAAI Conference on Artificial Intelligence (AAAI), 2024.

[80] Cheongwoong Kang and Jaesik Choi,
Impact of Co-occurrence on Factual Knowledge of Large Language Models,
Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP), 2023 (pdf).

[79] Ye Eun Chun, Sunjae Kwon, Kyunghwan Sohn, Nakwon Sung, Junyoup Lee, Byoung Ki Seo, Kevin Compher, Seung-won Hwang and Jaesik Choi,
CR-COPEC: Causal Rationale of Corporate Performance Changes to learn from Financial Reports,
Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP), 2023.

[78] Anh Tong, Thanh Nguyen-Tang, Dongeun Lee, Toan Tran and Jaesik Choi,
SigFormer: Signature Transformers for Deep Hedging,
Association for Computing Machinery International Conference on Artificial Intelligence in Finance (ACM ICAIF), 2023.

[77] Kyowoon Lee*, Seongun Kim* and Jaesik Choi,
Refining Diffusion Planner for Reliable Behavior Synthesis by Automatic Detection of Infeasible Plans,
Conference on Neural Information Processing Systems (NeurIPS), 2023 (* contributed equally).

[76] Byeongchan Lee* and Sehyun Lee*,
Implicit Contrastive Representation Learning with Guided Stop-gradient,
Conference on Neural Information Processing Systems (NeurIPS), 2023 (* contributed equally).

[75] Soyeon Kim, Junho Choi, Yeji Choi, Subeen Lee, Artyom Stitsyuk, Minkyoung Park, Seongyeop Jeong, Youhyun Baek and Jaesik Choi,
Explainable AI-Based Interface System for Weather Forecasting Model,
International Conference on Human-Computer Interaction International (HCII), 2023.

[74] Giyoung Jeon, Haedong Jeong and Jaesik Choi,
Beyond Single Path Integrated Gradients for Reliable Input Attribution via Randomized Path Sampling,
International Conference on Computer Vision (ICCV), 2023.

[73] Seongun Kim*, Kyowoon Lee* and Jaesik Choi,
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills,
International Conference on Machine Learning (ICML), 2023 (* contributed equally).

[72] Jiyeon Han, Hwanil Choi, Yunjey Choi, Junho Kim, Jung-Woo Ha and Jaesik Choi,
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized Images,
International Conference on Learning Representations (ICLR), 2023.

[71] Kyowoon Lee*, Seongun Kim* and Jaesik Choi,
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning,
International Conference on Robotics and Automation (ICRA), 2023 (* contributed equally) (pdf).

[70] Jaesik Choi,
South Korea’s Response to Surging AI Use in the US and China,
Global Asia, 17 (4), 2022.

[69] Anh Tong, Thanh Nguyen-Tang, Toan Tran and Jaesik Choi,
Learning Fractional White Noises in Neural Stochastic Differential Equations,
Conference on Neural Information Processing Systems (NeurIPS), 2022.

[68] Giyoung Jeon*, Haedong Jeong* and Jaesik Choi,
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network,
Conference on Neural Information Processing Systems (NeurIPS), 2022 (* contributed equally).

[67] Hwanil Choi, Wonjoon Chang and Jaesik Choi,
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?,
International Joint Conference on Artificial Intelligence (IJCAI), 2022.

[66] Haedong Jeong, Jiyeon Han and Jaesik Choi,
An Unsupervised Way to Understand Artifact Generating Internal Units in Generative Neural Networks,
AAAI Conference on Artificial Intelligence (AAAI), 2022.

[65] Bumjin Park, Cheongwoong Kang, Jaesik Choi,
Cooperative Multi-Robot Task Allocation with Reinforcement Learning,
Applied Sciences, 12 (1), 2022.

[64] Cheongwoong Kang, Bumjin Park, Jaesik Choi,
Scheduling PID Attitude and Position Control Frequencies for Time-Optimal Quadrotor Waypoint Tracking under Unknown External Disturbances,
Sensors, 22 (1), 2022.

[63] Jaesik Choi, Kristian Kersting and Yuqiao Chen,
Lifted Inference for Hybrid Relational Models,
An Introduction to Lifted Probabilistic Inference, MIT Press, August 2021. [Book Chapter]

[62] Wannes Meert, Jaesik Choi, Jacek Kisynski, Hung Bui, Guy Van den Broeck, Adnan Darwiche, Rodrigo de Salvo Braz and David Poole,
Lifted Aggregation and Skolemization for Directed Models,
An Introduction to Lifted Probabilistic Inference, MIT Press, August 2021. [Book Chapter]

[61] Juhwan Kim, Geun Ho Gu, Juhwan Noh, Seongun Kim, Suji Gim, Jaesik Choi* and Yousung Jung*,
Predicting Potentially Hazardous Chemical Reactions Using Explainable Neural Network,
Chemical Science (Chem), 2021. [IF: 9.825]

[60] Seongun Kim and Jaesik Choi,
Explaining the Decisions of Deep Policy Networks for Robotic Manipulations,
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021.

[59] Qin Xie, Peng Zhang, Boseon Yu* and Jaesik Choi*,
Semi-Supervised Training of Deep Generative Models for High-Dimensional Anomaly Detection,
IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021. [IF: 10.451]

(* co-corresponding author)

[58] Sunjae Kwon, Dongsuk Oh and Youngjoong Ko
Word sense disambiguation based on context selection using knowledge-based word similarity,
Information Processing & Management, 2021.

[57] Sohee Cho*, Wonjoon Chang*, Ginkyeng Lee and Jaesik Choi,
Interpreting Internal Activation Patterns in Deep Temporal Neural Networks by Finding Prototypes,
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021 (* contributed equally).

[56] Boseon Yoo, Jiwoo Lee, Janghoon Ju, Seijun Chung, Soyeon Kim and Jaesik Choi,
Conditional Temporal Neural Process with Covariance Loss,
International Conference on Machine Learning (ICML), 2021.

[55] Ali Tousi*, Haedong Jeong*, Jiyeon Han, Hwanil Choi and Jaesik Choi,
Automatic Correction of Internal Units in Generative Neural Networks,
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 (* contributed equally).

[54] Anh Tong, Toan Tran, Hung Bui and Jaesik Choi,
Learning Compositional Sparse Gaussian Processes with a Shrinkage Prior,
AAAI Conference on Artificial Intelligence (AAAI), 2021.

[53] Anh Tong and Jaesik Choi,
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems,
AAAI Conference on Artificial Intelligence (AAAI), 2021.

[52] Woo Jeoung Nam, Jaesik Choi and Seong-Whan Lee,
Interpreting Deep Neural Networks with Relative Sectional Propagation by Analyzing Comparative Gradients and Hostile Activations,
AAAI Conference on Artificial Intelligence (AAAI), 2021.

[Thesis] Cheongwoong Kang
Why Do Masked Neural Language Models Still Need Semantic Knowledge in Question Answering?,
MS thesis, Ulsan National Institute of Science and Technology, March 2021.

[Thesis] Nguyen Thanh Nguyen
Imporving Abstractive Summarization by Understanding Hidden Representations and Guidance on Semantic Meaning,
MS thesis, Ulsan National Institute of Science and Technology, March 2021.

[Thesis] Youngjin Park
Improved Prediction of Deep Temporal Neural Networks with Trend Filtering,
MS thesis, Ulsan National Institute of Science and Technology, March 2021.

[51] Hyunbin Kim, Hae Nim Lee, Jaesik Choi*, Jihye Seong*,
Spatiotemporal characterization of GPCR activity and function during endosomal trafficking pathway,
ACS Analytical Chemistry, 2021. (IF: 6.78)

(* co-corresponding author)

[50] Heejung Kim, Hyunbin Kim, Jaesik Choi, Kyung-Soo Inn and Jihye Seong,
Visualization of autophagy progression by Red-Green-Blue autophagy sensor,
ACS Sensors, 2020. (IF: 7.33) [the cover article]

[Thesis] Alisher Abdulov
Deep Trajectory Prediction for Robotic Manipulation Under Unreliable Sensor Data,
MS thesis, Ulsan National Institute of Science and Technology, September 2020.

[Thesis] Jiwoo Lee
Learning Basis Functions of Deep Spatio-Temporal Neural Networks with Covariance Loss,
MS thesis, Ulsan National Institute of Science and Technology, September 2020.

[Thesis] Dongju Shin
A Convolutional Neural Network based Policy Inspired by the Cerebellum,
MS thesis, Ulsan National Institute of Science and Technology, September 2020.

[Thesis] Yeeun Chun
Learning to Explaining Causal Rationale of Stock Price Changes in Financial Reports,
MS thesis, Ulsan National Institute of Science and Technology, September 2020.

[49] Jaesik Choi
Interpreting and Explaining Deep Neural Networks: A Perspective on Time Series Data,
ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD), 2020 [tutorial link, video]

[48] Youngjin Park, Deokjun Eom, and Jaesik Choi,
Improved Predictive Deep Temporal Neural Networks with Trend Filtering,
ACM International Conference on AI in Finance (ICAIF-2020), 2020 [slides].

[47] Jay H. Park, Gyeongchan Yun, Chang M. Yi, Nguyen T. Nguyen, Seungmin Lee, Jaesik Choi, Sam H. Noh and Young-ri Choi,
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism,
USENIX Annual Technical Conference (USENIX ATC), 2020.

[46] YongHyeok Seo, Dongju Shin, Jaesik Choi, and Se Young Chun,
A Single Multi-Task Deep Neural Network with Post-Processing for Object Detection with Reasoning and Robotic Grasp Detection,
International Conference on Robotics and Automation (ICRA), 2020.

[45] Giyoung Jeon*, Haedong Jeong* and Jaesik Choi,
An Efficient Explorative Sampling Considering the Generative Boundaries of Deep Generative Neural Networks,
AAAI Conference on Artificial Intelligence (AAAI), 2020 (* contributed equally) (Oral).

[44] Woo-Jeoung Nam, Shir Gur, Jaesik Choi, Lior Wolf and Seong-Whan Lee,
Relative Attributing Propagation: Interpreting the Comparative Contributions of Individual Units in Deep Neural Networks,
AAAI Conference on Artificial Intelligence (AAAI), 2020.

[Thesis] Jiyeon Han
Determining Changes in the Covariance Structure of Gaussian Processes,
MS thesis, Ulsan National Institute of Science and Technology, March 2020.

[Thesis] Sol-A Kim
Deep Reinforcement Learning in Multi-End Games,
MS thesis, Ulsan National Institute of Science and Technology, March 2020.

[Thesis] Sehyun Lee
A General Compositional Operation in Random Process,
MS thesis, Ulsan National Institute of Science and Technology, March 2020.

[Thesis] Ginkyeng Lee
Monte-Carlo Dropout based Uncertainty Analysis in Input Attributions of Multivariate Temporal Neural Networks,
MS thesis, Ulsan National Institute of Science and Technology, March 2020.

[43] David Gunning, Mark Stefik, Jaesik Choi, Timothy Miller, Simone Stumpf and Guang-Zhong Yang,
XAI—Explainable artificial intelligence,
Science Robotics, 4(37), 2019. (IF: 19.4)

[42] Thanh T. Nguyen and Jaesik Choi,
Markov Information Bottleneck to Improve Information Flow in Stochastic Neural Networks,
Entropy, 21(10), 976, 2019.

[41] Jiyeon Han*, Kyowoon Lee*, Anh Tong and Jaesik Choi,
Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes,
International Joint Conference on Artificial Intelligence (IJCAI), 2019 (* contributed equally).

[W] Anh Tong and Jaesik Choi,
Explain pathology in Deep Gaussian Process using Chaos Theory,
International Conference on Machine Learning (ICML) 2019 Workshop on Theoretical Physics for Deep Learning, 2019.

[40] Anh Tong and Jaesik Choi,
Discovering Explainable Latent Covariance Structure for Multiple Time Series,
International Conference on Machine Learning (ICML), 2019.

[39] Seongun Kim, Sol A Kim, Rafael de Lima, Jaesik Choi,
Implementation of End-to-End Training of Deep Visuomotor Policies for Manipulation of a Robotic Arm of Baxter Research Robot,
Journal of Korea Robotics Society, 2019.

[Thesis] Janghoon Ju
Deep Neural Networks to Learn Basis Functions with a Temporal Covariance Loss,
MS thesis, Ulsan National Institute of Science and Technology, September 2019.

[38] Kade Gibson, Dongeun Lee, Jaesik Choi and Alexander Sim,
Dynamic Online Performance Optimization in Streaming Data Compression,
IEEE International Conference on Big Data (IEEE Big Data), 2018.

[W] Subin Yi and Jaesik Choi,
Learning Group Structure of Deep Neural Networks with an Expectation Maximization Method,
Deep Learning and Clustering Workshop, collocated with International Conference on Data Mining (ICDM), 2018 (source codes).

[37] Dongsuk O*, Sunjae Kwon*, Kyungsun Kim and Youngjoong Ko,
Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph,
International Conference on Computational Linguistics (COLING), 2018 (* contributed equally).

[W] Anh Tong and Jaesik Choi,
Discovering Explainable Latent Covariance Structure for Multiple Time Series,
Statistical Relational AI Workshop, collocated with International Joint Conference on Artificial Intelligence (IJCAI), 2018.

[36] Kyowoon Lee*, Sol-A Kim*, Jaesik Choi and Seong-Whan Lee,
Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling,
International Conference on Machine Learning (ICML), 2018 (* contributed equally).

[35] Taehoon Kim, Jaesik Choi, Dongeun Lee, Alex Sim, C. Anna Spurlock, Annika Todd and Kesheng Wu,
Predicting baseline for analysis of electricity pricing, International Journal of Big Data Intelligence, Volume 5 (pdf).

[Thesis] Subin Yi
An Expectation Maximization Method to Learn the Group Structure of Deep Neural Network,
MS thesis, Ulsan National Institute of Science and Technology, March 2018.

[Thesis] Thanh Nguyen Tang
Parametric Information Bottleneck to Optimize Stochastic Neural Networks,
MS thesis, Ulsan National Institute of Science and Technology, March 2018.

[34] Ramesh Patel, Kallol Roy, Jaesik Choi and Ki Jin Han,
Generative Design of Electromagnetic Structures Through Bayesian Learning,
IEEE Transactions on Magnetics, 2017 (pdf).

[33] Dongeun Lee, Alex Sim, Jaesik Choi and John Wu,
Expanding Statistical Similarity Based Data Reduction to Capture Diverse Patterns,
Data Compression Conference (DCC), 2017 (pdf).

[32] Man-Ki Yoon, Sibin Mohan, Jaesik Choi, Mihai Christodorescu and Lui Sha,
Learning Execution Contexts from System Call Distribution for Anomaly Detection in Smart Embedded System,
The ACM/IEEE International Conference on Internet-of-Things Design and Implementation (IoTDI), 2017 (pdf).

[31] Dongeun Lee, Alex Sim, Jaesik Choi and John Wu,
Improving Statistical Similarity Based Data Reduction for Non-Stationary Data,
International Conference on Scientific and Statistical Database Management (SSDBM), 2017 (pdf).

[Thesis] Rafael de Lima
Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes,
MS thesis, Ulsan National Institute of Science and Technology, August 2017.

[30] Vladimir Nekrasov, Janghoon Ju and Jaesik Choi,
Global Deconvolutional Networks for Semantic Segmentation,
The British Machine Vision Conference (BMVC), 2016 (pdf, source codes).

[29] Dongeun Lee, Alex Sim, Jaesik Choi and John Wu,
Novel Data Reduction Based on Statistical Similarity,
International Conference on Scientific and Statistical Database Management (SSDBM), 2016 (pdf, source codes).

[W] Wonjun Yoon, Sol-A Kim and Jaesik Choi,
An End-to-End Robot Architecture to Manipulate Non-Physical State Changes of Objects,
The International Workshop on Cognitive Robotics (CogRob), 2016 (slides)

[W] Anh Tong and Jaesik Choi,
Automatic Generation of Probabilistic Programming from Time Series Data,
International Workshop on Statistical Relational AI (StarAI), 2016

[28] Dongeun Lee, Rafael de Lima and Jaesik Choi,
Improving Imprecise Compressive Sensing Models,
Conference on Uncertainty in Artificial Intelligence (UAI), 2016 (pdf).

[Thesis] Yunseong Hwang
The Automatic Statistician: A Relational Perspective,
MS thesis, Ulsan National Institute of Science and Technology, February 2016.

[27] Yunseong Hwang, Anh Tong and Jaesik Choi,
Automatic Construction of Nonparametric Relational Regression Models for Multiple Time Series,
International Conference on Machine Learning (ICML), 2016 (pdf, source codes).

[26] Taehoon Kim, Dongeun Lee, Jaesik Choi, Anna Spurlock, Alex Sim, Annika Todd and Kesheng Wu,
Extracting Baseline Electricity Usage Using Gradient Tree Boosting,
International Conference on Big Data Intelligence and Computing (DataCom), 2015 (Best Paper Award).

[25] Taehoon Kim and Jaesik Choi,
Reading Documents for Bayesian Online Change Point Detection,
Conference on Empirical Methods on Natural Language Processing (EMNLP), 2015. (pdf, source codes)

[24] Dongeun Lee, Jaesik Choi and Heonshik Shin,
A Scalable and Flexible Repository for Big Sensor Data,
IEEE Sensors Journal, 15(12), 2015, 7284-7294.

[23] Wen Pu, Jaesik Choi, Yunseong Hwang and Eyal Amir,
A Deterministic Partition Function Approximation for Exponential Random Graph Models,
International Joint Conference on Artificial Intelligence (IJCAI), 2015 (pdf, slides).

[22] Man-Ki Yoon, Sibin Mohan, Jaesik Choi and Lui Sha,
Memory Heat Map: Anomaly Detection in Real-Time Embedded Systems Using Memory Behavior,
Design Automation Conference (DAC), 2015 (pdf, BibTeX).

[21] Kejia Hu, Jaesik Choi, Alex Sim and Jiming Jiang,
Best Predictive Generalized Linear Mixed Model with Predictive Lasso for High-Speed Network Data Analysis,
International Journal of Statistics and Probability, 4(2), 2015, 132-148.

[20] Dongeun Lee and Jaesik Choi,
Learning Compressive Sensing Models for Big Spatio-Temporal Data,
2015 SIAM International Conference on Data Mining (SDM), 2015 (pdf).

[19] Jaesik Choi, Eyal Amir, Tianfan Xu and Albert Valocchi,
Learning Relational Kalman Filtering,
AAAI Conference on Artificial Intelligence (AAAI), 2015 (pdf, slides).

[18] Kyungjoong Jeong, Jaesik Choi and Giljin Jang,
Semi-Local Structure Patterns for Robust Face Detection,
IEEE Signal Processing Letters, 22(9), 2015, 1400-1403.

[P] Jaesik Choi and Alex Sim,
Efficient Data Reduction Method with Locally Exchangeable Measures,
US 0149495 (pending), May 28, 2015.

[17] Dongeun Lee, Jaesik Choi and Heonshik Shin,
Low-complexity compressive sensing with downsampling,
IEICE Electronic Express, 11(3), 2014, 20130947.

[16] Tianfang Xu, Albert J. Valocchi, Jaesik Choi and Eyal Amir,
Use of Machine Learning Methods to Reduce Predictive Error of Groundwater Models,
Groundwater, 52(3), 2014, 448-460.

[15] Dongeun Lee and Jaesik Choi,
Low Complexity Sensing for Big Spatio-Temporal Data,
IEEE 2014 International Conference on Big Data (IEEE BigData), 2014 (pdf).

[14] Jaesik Choi, Ziyu Wang, Sang-Chul Lee and Won J. Jeon,
A Spatio-Temporal Pyramid Matching for Video Retrieval,
Computer Vision and Image Understanding, 117(6), 2013, 660-669.

[13] Man-Ki Yoon, Sibin Mohan, Jaesik Choi, Jung-Eun Kim and Lui Sha,
SecureCore: A Multicore based Intrusion Detection Architecture for Real-time Embedded Systems,
IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), 2013.

[TR] William Gu, Jaesik Choi, Ming Gu, Horst Simon, Kesheng Wu, Fast Change Point Detection for Electricity Market Analysis,
Lawrence Berkeley National Laboratory, LBNL LBNL-6388E, 2013

[TR] Kejia Hu, Jaesik Choi, Jiming Jiang, Alex Sim, Best Predictive GLMM using LASSO with Application on High-Speed Network,
Lawrence Berkeley National Laboratory, LBNL-6327E, 2013.

[TR] Jaesik Choi, Kejia Hu, Alex Sim, Relational Dynamic Bayesian Networks with Locally Exchangeable Measures,
Lawrence Berkeley National Laboratory, LBNL-6341E, 2013

[Thesis] Jaesik Choi
Lifted Inference for Relational Hybrid Models,
PhD thesis, University of Illinois at Urbana-Champaign, 2012.

[12] Jaesik Choi and Eyal Amir,
Lifted Relational Variational Inference,
Conference on Uncertainty in Artificial Intelligence (UAI), 2012 (pdf).

[11] Tiangfang Xu, Albert J. Valocchi,Jaesik Choi and Eyal Amir,
Improving Groundwater Flow Model Prediction Using Complementary Data-Driven Models,
XIX International Conference on Computational Methods in Water Resources (CMWR), 2012.

[TR] Richard B. Vilim, Alex Heifetz, Young Soo Park, and Jaesik Choi,
Description of Fault Detection and Identification Algorithms for Sensor and Equipment Failures and Preliminary Tests Using Simulations,
Argonne National Laboratory – Nuclear Engineering Division, ANL/NE-12/57, 2012.

[TR] Jaesik Choi,
Realtime On-Road Vehicle Detection with Optical Flows and Haar-Like Feature Detector,
Computer Science Research and Tech Reports, University of Illinois at Urbana-Champaign, 2012.

[10] Jaesik Choi, Rodrigo de Salvo Braz and Hung Bui,
Efficient Methods for Lifted Inference with Aggregate Factors,
AAAI Conference on Artificial Intelligence (AAAI), 2011 (pdf,).

[9] Jaesik Choi, Abner Guzman Rivera and Eyal Amir,
Lifted Relational Kalman Filtering,
International Joint Conference on Artificial Intelligence (IJCAI), 2011 (pdf).

[8] Jaesik Choi, Eyal Amir and David J. Hill,
Lifted Inference for Relational Continuous Models,
Conference on Uncertainty in Artificial Intelligence (UAI), 2010 (pdf).

[W] Jaesik Choi, David J. Hill and Eyal Amir, Lifted Inference for Relational Continuous Models,
AAAI-10 Workshop on Statistical Relational AI, 2010 (An extended version: UAI paper pdf)

[W] Jaesik Choi and Eyal Amir, Combining Planning and Motion Planning: An Extended Abstract, ICAPS-10 Workshop: Combining Action and Motion Planning, 2010 (Invited Talk).

[P] Jaesik Choi, Jay Pujara, Vishwanth Tumkur Ramarao and Ke Wei,
Identifying IP Addresses For Spammers,
US Patent No. 7,849,146, 2010.

[7] Hannaneh Hajishirzi, Afsaneh Shirazi, Jaesik Choi and Eyal Amir,
Greedy Algorithms for Sequential Sensing Decisions, 
International Joint Conference on Artificial Intelligence (IJCAI), 2009 (pdf).

[6] Woojin Chung, Seokgyu Kim, Minki Choi, Jaesik Choi, Hoyeon Kim, Changbae Moon and Jae-bok Song,
Safe navigation of a mobile robot considering visibility of environment,
IEEE Transactions on Industrial Electronics, 56 (10), 2009.

[5] Jaesik Choi, Eyal Amir,
Combining Planning and Motion Planning, 
IEEE International Conference on Robotics and Automation (ICRA), 2009 (pdf).

[W] Jaesik Choi and Eyal Amir, Combining Planning and Motion Planning with an Action Formalism, Ninth International Symposium on Logical Formalizations of Commonsense Reasoning, 2009.

[P] Jaesik Choi, Ke Wei and Vishwanth Tumkur Ramarao,
Filter for Blocking Image-Based SPAM,
US Patent No. 8,055,078, 2009.

[4] Jaesik Choi, Won J. Jeon, Sang-Chul Lee,
Spatio-Temporal Pyramid Matching for Sports Videos, 
ACM International Conference on Multimedia Information Retrieval (MIR), 2008 (pdf).

[3] Yong Liu, David J. Hill, Tarek Abdelzaher, Jin Heo,Jaesik Choi, Barbara Minsker, David Fazio,
Virtual Sensor-Powered Spatiotemporal Aggregation and Transformation: A Case Study Analyzing Near-Real-Time NEXRAD and Precipitation Gage Data in a Digital Watershed,
Environmental Information Management (EIM), 2008.

[2] Jaesik Choi and Eyal Amir,
Factor-Guided Motion Planning for a Robot Arm,
IEEE International Conference on Intelligent Robots and Systems (IROS), 2007 (pdf).

[W] Jaesik Choi and Eyal Amir, Factored Planning for Controlling a Robotic Arm, AAAI 2006 Fall Symposium on Integrating Reasoning into Everyday Applications, 2006.

[W] Jaesik Choi and Eyal Amir, Factored Planning for Controlling a Robotic Arm: Theory, AAAI-06 Workshop: the 5th international workshop on cognitive robotics (CogRob’06), 2006.

[W] Woojin Chung, Seokgyu Kim, and Jaesik Choi, High speed navigation of a mobile robot based on experiences, The JSME Robotics and Mechatronics Conference (ROBOMEC 2006) , May 2006.

[1] Jaesik Choi, Woojin Chung, and Jae-Bok Song,
Efficient navigation of mobile robot based on the robot’s experience in human co-existing environment,
International Conference on Control, Automation and Systems (ICCAS), 2005.

Preprints

• Sunjae Kwon, Cheongwoong Kang, Jiyeon Han, Jaesik Choi,
  Why Do Neural Language Models Still Need Commonsense Knowledge to Handle Semantic Variations in Question Answering?,
  arXiv 2209.00599.

• Subin Yi, Janghoon Ju, Man-Ki Yoon, and Jaesik Choi,
  Grouped Convolutional Neural Networks for Multivariate Time Series,
  arXiv 1703.09938. (source codes)

• Rafael Lima and Jaesik Choi,
  Automatic Decomposition of Self-Triggering Kernels of Hawkes Processes,
  arXiv 1703.09068.