Research Interests

• Deep learning

  • How does a machine learn? How can we improve it?

    • Optimization and Generalization [NeurIPS 22]

    • Learning Algorithms [ICML 23]

      • Implicit Bias of SGD [ICLR 23, ICML 23]

    • Neural Networks

      • Overparameterized Models

      • Transformers

      • Large Language Models (LLMs)

  • How can we build a robust and reliable machine?

    • Trustworthy AI, AI Safety and Alignment 

    • Robustness

• Adversarial Robustness [TPAMI 23]

  • Certified Defense [NeurIPS 20, NeurIPS 21]

• Distribution Shift

Selected Ongoing Works

• Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors
  SL, Sokbae Lee
  arxiv

Selected Publications

• Implicit Jacobian regularization weighted with impurity of probability output
  SL, Jinseong Park, Jaewook Lee
  ICML 2023
  paper/poster/slides

We show that SGD has an implicit regularization effect on the logit-weight Jacobian norm in neural networks. This regularization effect is weighted with the impurity of the probability output, and thus it is active in a certain phase of training. Based on these findings,  we propose a novel optimization method which leads to similar performance as other SOTA sharpness-aware optimization methods such as SAM and ASAM.

• A New Characterization of the Edge of Stability Based on a Sharpness Measure Aware of Batch Gradient Distribution
  SL, Cheongjae Jang
  ICLR 2023
  paper/poster/slides

We provide a clearer characterization of the Edge of Stability and extend it to general mini-batch SGD. Moreover, based on the analysis, we propose a new scaling rule, LSSR (Linear and Saturation Scaling Rule), between learning rate and batch size.

• A Reparametrization-Invariant Sharpness Measure Based on Information Geometry
  Cheongjae Jang, SL, Yung-Kyun Noh, Frank C. Park
  NeurIPS 2022
  paper/poster

We provide a reparametrization-invariant sharpness measure based on information geometry.

• GradDiv: Adversarial Robustness of Randomized Neural Networks via Gradient Diversity Regularization
  SL, Hoki Kim, Jaewook Lee
  IEEE TPAMI (Feb. 2023)
  paper/code
  
  The effect of proxy-gradients-based adversarial attacks on randomized neural networks highly relies on the directional distribution of the loss-input gradients. We propose Gradient Diversity (GradDiv) regularizations that minimize the concentration of the gradients to weaken these proxy-gradients-based attacks.
  
  

• Towards Better Understanding of Training Certifiably Robust Models to Adversarial Examples
  SL, Woojin Lee, Jinseong Park, Jaewook Lee
  NeurIPS 2021
  paper/code/poster/slides
  
  We identify smoothness of the objective loss landscape as an important factor in building certifiably robust model against adversarial attacks.
  
  

• Lipschitz-Certifiable Training with a Tight Outer Bound
  SL, Jaewook Lee, Saerom Park
  NeurIPS 2020
  paper/code/poster/slides
  
  Certifiable training minimizes an upper bound on the worst-case loss over valid adversarial perturbations, and thus the tightness of the upper bound is crucial. We propose a certified defense method with a tight upper bound.