Quanquan Gu
Associate Professor
Recent Talks

On the Convergence of Monte Carlo Methods with Stochastic Gradients
Simons Institute Workshop on Sampling Algorithms and Geometries on Probability Distributions, October, 2021.[slides]

Stochastic VarianceReduced Highorder Optimization for Nonconvex Optimization
ICML 2021 Workshop on Beyond first order methods in machine learning systems, July, 2021.[slides]

On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate
UCSD Halıcıoğlu Data Science Institute Seminar, April, 2021.[slides]

Stochastic VarianceReduced Cubic Regularized Newton Methods for Nonconvex
Optimization
SIAM CSE Minisymposium on beyond firstorder algorithms in modern machine learning systems, March, 2021.[slides]

Understanding Overparameterized Deep Neural Networks: From Optimization To
Generalization
IJCAI Early Career Talk, January 2021.[slides]

Learning Wide Neural Networks: Polylogarithmic Overparameterization and A Mean Field Perspective
Northwesern University IDEAL Theory of Deep Learning Seminar, October, 2020. [slides]

Epidemic Model Guided Machine Learning for COVID19 Forecasts
ICLR 2021 Workshop on Machine Learning for Preventing and Combating Pandemics, May, 2021. [slides]
UCSB Second Annual Responsible Machine Learning Summit, October, 2020. [slides]
D. E. Shaw Technical Talk Forum, June, 2020. [slides]
Institute for Digital Research and Education (IDRE), June, 2020. [slides]
UCLA Computer Science Department Seminar, May, 2020. [slides]
AI for COVID19 in LA Symposium, May, 2020. [slides]

Understanding, Improving and Evaluating Adversarial Robustness in Deep Learning
Johns Hopkins University Machine Learning Seminar, September, 2020. [slides]
KDD 2020 Workshop on Adversarial Learning Methods for Machine Learning and Data Mining, August, 2020. [slides]

A Generalized Neural Tangent Kernel Analysis for Twolayer Neural Networks Simons Institute Deep Learning Reunion Workshop, August, 2020. [slides]

Learning Overparameterized Neural Networks: From Neural Tangent Kernel to Meanfield Analysis IPAM Workshop on PDE and Inverse Problem Methods in Machine Learning, April, 2020. [slides]
UCSD AI Seminar, February, 2020. [slides]

On the Optimization and Generalization of Neural Networks: A MeanField Perspective Information Theory and Applications Workshop, February, 2020. [slides]

Towards Understanding Overparameterized Deep Neural Networks: From Optimization To Generalization TTIC Workshop on "Recent Trends in Clustering and Classification", September, 2019. [slides]
Machine Learning Theory Workshop at Peking University, June, 2019. [slides]

Two facets of stochastic optimization: continuoustime dynamics and discretetime algorithms Workshop on "Interplay between Control, Optimization, and Machine Learning" at American Control Conference, July, 2019. [slides]

New Variance Reduction Algorithms for Nonconvex FiniteSum Optimization USC Machine Learning Seminar, November, 2018. [slides]

Closing the Generalization Gap of Adaptive Gradient Methods in Training Deep Neural Networks USC Information Science Institute AI Seminar, November, 2018. [slides]
Tutorials
