College of Commerce and Economics

Research output

Research output per year 1990 2012 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

• 41459 Citations
• 91 h-index
• 1302 Article
• 70 Conference contribution
• 19 Conference article
• 19 Review article
• 50 More
  • 15 Chapter
  • 13 Comment/debate
  • 11 Paper
  • 6 Editorial
  • 4 Letter
  • 1 Short survey

Research output per year

Research output per year

Search results

• 2024

  Analysis of Using Sigmoid Loss for Contrastive Learning

  Lee, C., Chang, J. & Sohn, J. Y., 2024, In: Proceedings of Machine Learning Research. 238, p. 1747-1755 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Supervised Learning 100%
  • Single Variable 16%

• Enhancing Sufficient Dimension Reduction via Hellinger Correlation

  Hong, S., Kim, I. & Song, J., 2024, In: Proceedings of Machine Learning Research. 235, p. 18634-18647 14 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Numerical Experiment 100%
  • Deep Understanding 100%
  • Conditional Independence 100%
  • Complete Subspace 100%

• Memorization Capacity for Additive Fine-Tuning with Small ReLU Networks

  Sohn, J. Y., Kwon, D., An, S. & Lee, K., 2024, In: Proceedings of Machine Learning Research. 244, p. 3264-3278 15 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Network Layer 100%
  • Neural Network 100%
  • Trained Network 50%
  • Common Practice 50%
  • Machine Learning 50%

• On the Effectiveness of Supervision in Asymmetric Non-Contrastive Learning

  Oh, J. & Lee, K., 2024, In: Proceedings of Machine Learning Research. 235, p. 38541-38561 21 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Transfer of Learning 100%
  • Class Variance 11%

• Pre-trained Vision and Language Transformers are Few-Shot Incremental Learners

  Park, K. H., Song, K. & Park, G. M., 2024, In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. p. 23881-23890 10 p.

  Research output: Contribution to journal › Conference article › peer-review

  Open Access

  • Knowledge Transfer 100%
  • Semantic Knowledge 100%
  6 Citations (Scopus)
• 2023

  Looped Transformers as Programmable Computers

  Giannou, A., Rajput, S., Sohn, J. Y., Lee, K., Lee, J. D. & Papailiopoulos, D., 2023, In: Proceedings of Machine Learning Research. 202, p. 11398-11442 45 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Conditional Branch 100%
  • Punch Card 100%
  13 Citations (Scopus)

• SAAL: Sharpness-Aware Active Learning

  Kim, Y. Y., Cho, Y., Jang, J. H., Na, B., Kim, Y., Song, K., Kang, W. & Moon, I. C., 2023, In: Proceedings of Machine Learning Research. 202, p. 16424-16440 17 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Active Learning 100%
  • Image Classification 20%
  • Object Detection 20%
  • Image Segmentation 20%
  • Data Instance 20%
  3 Citations (Scopus)
• 2022

  Efficient Approximate Inference for Stationary Kernel on Frequency Domain

  Jung, Y., Song, K. & Park, J., 2022, In: Proceedings of Machine Learning Research. 162, p. 10502-10538 37 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Frequency Domain 100%
  • Spectral Density 100%
  • Spectral Point 60%
  • Inference Method 50%
  • Gaussian Mixture 50%
  3 Citations (Scopus)

• Finding Nearly Everything within Random Binary Networks

  Sreenivasan, K., Rajput, S., Sohn, J. Y. & Papailiopoulos, D., 2022, In: Proceedings of Machine Learning Research. 151, p. 3531-3541 11 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Neural Network 100%
  • Arbitrary Accuracy 33%
  8 Citations (Scopus)

• From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

  Bae, H. S., Shin, S., Na, B., Jang, J. H., Song, K. & Moon, I. C., 2022, In: Proceedings of Machine Learning Research. 162, p. 1277-1297 21 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Transition Matrix 100%
  • Generative Model 100%
  • Negative Effect 50%
  16 Citations (Scopus)

• GenLabel: Mixup Relabeling using Generative Models

  Sohn, J. Y., Shang, L., Chen, H., Moon, J., Papailiopoulos, D. & Lee, K., 2022, In: Proceedings of Machine Learning Research. 162, p. 20278-20313 36 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Generative Model 100%
  • Data Augmentation 50%
  • Prediction Performance 50%
  • Data Distribution 50%
  5 Citations (Scopus)

• Soft Truncation: A Universal Training Technique of Score-based Diffusion Model for High Precision Score Estimation

  Kim, D., Shin, S., Song, K., Kang, W. & Moon, I. C., 2022, In: Proceedings of Machine Learning Research. 162, p. 11201-11228 28 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Diffusion Model 100%
  • Art Performance 50%
  • Random Variable 25%
  • Image Synthesis 25%
  31 Citations (Scopus)
• 2020

  Attack of the tails: Yes, you really can backdoor federated learning

  Wang, H., Sreenivasan, K., Rajput, S., Vishwakarma, H., Agarwal, S., Sohn, J. Y., Lee, K. & Papailiopoulos, D., 2020, In: Advances in Neural Information Processing Systems. 2020-December

  Research output: Contribution to journal › Conference article › peer-review

  • Backdoors 100%
  • Federated Learning 45%
  • Simulation Mode 36%
  • Attack 36%
  • Machine Learning 9%
  312 Citations (Scopus)

• Election coding for distributed learning: Protecting SignSGD against byzantine attacks

  Sohn, J. Y., Han, D. J., Choi, B. & Moon, J., 2020, In: Advances in Neural Information Processing Systems. 2020-December

  Research output: Contribution to journal › Conference article › peer-review

  • Distance Education 100%
  • Attack 80%
  • Gradient Descent 60%
  • Learning Algorithm 40%
  • Learning Systems 40%
  21 Citations (Scopus)

• Randomized tests for high-dimensional regression: A more efficient and powerful solution

  Li, Y., Kim, I. & Wei, Y., 2020, In: Advances in Neural Information Processing Systems. 2020-December

  Research output: Contribution to journal › Conference article › peer-review

  • Random Projection 100%
  • Number 100%
  • Computation 50%
  • Synthetic Data 50%
  • Simulation Mode 50%
  1 Citation (Scopus)

• Validation of Approximate Likelihood and Emulator Models for Computationally Intensive Simulations

  Dalmasso, N., Lee, A. B., Izbicki, R., Pospisil, T., Kim, I. & Lin, C. A., 2020, In: Proceedings of Machine Learning Research. 108, p. 3349-3361 13 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Approximates 100%
  • Test Statistic 50%
  • Goodness of Fit Test 50%
  • Inferential Statistics 50%
  • Gaussian Distribution 50%
  4 Citations (Scopus)
• 2018

  A simple unified framework for detecting out-of-distribution samples and adversarial attacks

  Lee, K., Lee, K., Lee, H. & Shin, J., 2018, In: Advances in Neural Information Processing Systems. 2018-December, p. 7167-7177 11 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Classes 100%
  • Classification (Machine Learning) 66%
  • Simulation Mode 66%
  • Sample Distribution 66%
  • Machine Learning 33%
  1266 Citations (Scopus)
• 2017

  Time-dependent spatially varying graphical models, with application to brain fMRI data analysis

  Greenewald, K., Park, S., Zhou, S. & Giessing, A., 2017, In: Advances in Neural Information Processing Systems. 2017-December, p. 5833-5841 9 p.

  Research output: Contribution to journal › Conference article › peer-review

  • Space-Time 100%
  • Human Brain 100%
  • Additive Model 100%
  • Covariance Matrix 100%
  • Spatial Structure 100%
  9 Citations (Scopus)
• 2014

  Identifying the evolution of disasters and responses with network-text analysis

  Song, K., Kim, D. H., Shin, S. J. & Moon, I. C., 2014, In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. 2014-January, January, p. 664-671 8 p., 6973985.

  Research output: Contribution to journal › Conference article › peer-review

  • Networks 100%
  • Significance Test 50%
  • Statistical Significance 50%
  • Technological Advance 50%
  • Text Mining 50%
  2 Citations (Scopus)