Videos

CVPR Talk, June 15th, 2025

Seong-Hyeon Hwang, Minsu Kim, and Steven Euijong Whang, "T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning," CVPR 2025

[Paper][Poster]

Lab Introduction, Jan. 15th, 2025

KAIST Data Intelligence Lab introduction (language: Korean)

KAIST EE-X TechTalk, Jan. 15th, 2025

Steven Euijong Whang

Title: Recent Advances in Data-centric Responsible AI

Microsoft Research Abstracts podcast, Dec. 13th, 2024

Jindong Wang and Steven Euijong Whang

We discuss “ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models," NeurIPS 2024 (Spotlight)

[Episode]

NeurIPS Talk, Dec. 13th, 2024

Jio Oh, Soyeon Kim, Junseok Seo, Jindong Wang, Ruochen Xu, Xing Xie, and Steven Euijong Whang, "ERBench: An Entity-Relationship based Automatically Verifiable Hallucination Benchmark for Large Language Models," NeurIPS 2024 (Spotlight)

[Paper]

ACM SIGKDD Talk, Aug. 29th, 2024

Seong-Hyeon Hwang, Minsu Kim, and Steven Euijong Whang, "RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks," ACM SIGKDD 2024

[Paper][Slides]

AAAI Talk, Feb. 24th, 2024

Minsu Kim, Seong-Hyeon Hwang, and Steven Euijong Whang, "Quilt: Robust Data Segment Selection against Concept Drifts," AAAI 2024

[Paper][Slides]

ICML Talk, July 25th, 2023

Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh, "Improving Fair Training under Correlation Shifts," ICML 2023

[Paper][Slides]

ACM SIGMOD Talk, Jun. 22nd, 2023

Hantian Zhang, Ki Hyun Tae, Jaeyoung Park, Xu Chu, and Steven Euijong Whang , "iFlipper: Label Flipping for Individual Fairness," ACM SIGMOD 2023

[Paper][Slides]

AAAI Talk, Feb. 11th, 2023

Hyunseung Hwang and Steven Euijong Whang, "XClusters: Explainability-first Clustering," AAAI 2023

[Paper] [Slides]

AAAI Talk, Feb. 10th, 2023

Geon Heo and Steven Euijong Whang, "Redactor: A Data-centric and Individualized Defense Against Inference Attacks," AAAI 2023

[Paper] [Slides]

AI & Fairness Panel Talk @ KAIST International Symposium on AI and Future Society, Dec. 10th, 2021

AI & Fairness Panel with Tulsee Doshi (Head of Product, Google), Kangwook Lee (Professor, Univ. Wisconsin Madison), Been Kim (Research Scientist, Google Brain), and Changho Suh (Professor, KAIST)

My Talk: "Responsible AI: Model-centric and Data-centric Approaches"

[Slides]

NeurIPS Talk, Dec. 9th, 2021

Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh, "Sample Selection for Fair and Robust Training," NeurIPS 2021

[Paper] [Slides]

VLDB Talk, Aug. 18th, 2021

G. Heo, Y. Roh, S. Hwang, D. Lee, and S. E. Whang, "Inspector Gadget: A Data Programming-based Labeling System for Industrial Images," VLDB 2021

[Paper] [Slides] [Poster]

ACM SIGKDD Tutorial, Aug. 14th, 2021

J. Lee, Y. Roh, H. Song, and S. E. Whang, "Machine Learning Robustness, Fairness, and their Convergence," KDD, 2021 (Recorded separately with permission)

[Extended Abstract][Slides][Homepage]

ACM SIGMOD Talk, June 22nd, 2021

Ki Hyun Tae and Steven Euijong Whang, "Slice Tuner: A Selective Data Acquisition Framework for Accurate and Fair Machine Learning Models," SIGMOD 2021 

[Paper] [Slides]

ICLR Talk, May 3rd, 2021

Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh, "FairBatch: Batch Selection for Model Fairness," ICLR 2021

[Paper] [Slides]

VLDB Tutorial, Sept. 2nd, 2020

Steven Euijong Whang and Jae-Gil Lee, "Data Collection and Quality Challenges for Deep Learning," PVLDB, 13(12): 3429-3432, 2020 

[Proposal] [Slides]

Google APAC TechTalk, Aug. 19th, 2020

Steven Euijong Whang, "Responsible AI Techniques for Model Training and Data Acquisition," Google APAC Academic Research Talk Series, 2020

[Slides]

ICML Talk, July 16th, 2020

Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh, "FR-Train: A Mutual Information-based Approach to Fair and Robust Training," ICML 2020

[Paper] [Slides]