Advancing the theoretical foundations of machine learning through rigorous mathematical analysis, optimization theory, and algorithmic innovation
Our research in machine learning foundations provides the theoretical underpinnings that drive algorithmic innovations and practical breakthroughs in AI.
Developing theoretical frameworks for understanding generalization, sample complexity, and learning guarantees in machine learning.
Advancing optimization algorithms for machine learning, including non-convex optimization and distributed learning.
Bridging statistics and machine learning through probabilistic models, Bayesian methods, and uncertainty quantification.
Understanding the theoretical properties of deep neural networks, including expressivity, trainability, and generalization.
Our theoretical research has led to fundamental insights published in top-tier machine learning and theory conferences.
Novel theoretical analysis of generalization capabilities in overparameterized neural networks.
Theoretical guarantees for convergence in heterogeneous federated learning settings.
Fundamental limits and achievable bounds for few-shot learning algorithms.
Characterization of loss landscapes and their implications for training dynamics.
Our research leverages advanced mathematical tools from various fields to develop rigorous theoretical frameworks for machine learning.
Our foundational research projects advance the theoretical understanding of machine learning algorithms and their properties.
Fundamental research on generalization bounds and optimization landscapes in deep neural networks.
Developing theoretical frameworks for convergence guarantees in heterogeneous federated learning.
Sample complexity analysis and algorithmic development for few-shot learning systems.
Our theoretical research advances the mathematical foundations of machine learning through rigorous analysis and novel algorithmic insights.
We provide novel PAC-Bayesian generalization bounds for deep neural networks that are tighter than existing bounds and offer new insights into the role of network depth and width in generalization.
A comprehensive theoretical analysis of federated learning convergence under non-IID data distributions, providing tight convergence rates and practical algorithmic improvements.
We establish fundamental sample complexity bounds for meta-learning algorithms and show how these bounds translate to practical improvements in few-shot learning scenarios.
A geometric analysis of optimization landscapes in deep networks, proving new results about the connectivity of local minima and proposing improved training algorithms.
Our theoretical research appears in premier machine learning theory venues
Meet our machine learning theory research team.
Team component coming soon...