Aryan Mokhtari

Assistant Professor, Dept. of Electrical & Computer Engineering (ECE), UT Austin
Jack Kilby/Texas Instruments Endowed Faculty Fellow in Computer Engineering
Affiliated with:
- - NSF AI Institute for Foundations of Machine Learning (IFML)
  - NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE)
Google Scholar / Twitter

News

- Sept. ’24: Papers at NeurIPS 2024:
  - Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search [pdf] (Spotlight)
  - In-Context Learning with Transformers: Softmax Attention Adapts to Function Lipschitzness [pdf] (Spotlight)
  - Adaptive and Optimal Second-order Optimistic Methods for Minimax Optimization [pdf]
  - An Accelerated Gradient Method for Simple Bilevel Optimization with Convex Lower-level Problem [pdf]
  - Stochastic Newton Proximal Extragradient Method [pdf]
- July ’24: Qiujiang (joining Goldman Sachs), Isidoros (joining Meta), Liam (joining Snap Research), and Devyani (joining Goldman Sachs) have graduated! Wishing them all the best as they embark on their new journeys!
- May ’24: Paper at ICML 2024:
  - Provable Multi-Task Representation Learning by Two-Layer ReLU Neural Networks [pdf] (Oral Presentation)
- Apr. ’24: Google Research Scholar Award. (Announcement)
- Apr. ’24: New results on Global Convergence Analysis of BFGS:
  - Inexact line search: “Non-asymptotic Global Convergence Analysis of BFGS with the Armijo-Wolfe Line Search” [pdf]
  - Exact line search: “Non-asymptotic Global Convergence Rates of BFGS with Exact Line Search” [pdf]
- Jan. ’24: NSF CAREER Award. (Announcement)
- Jan. ’24: Paper at AISTATS 2024:
  - Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate [pdf]

Support

My research has been supported by an NSF CAREER Award CCF-2338846, NSF Grants CCF-2007668 and ECCS-2127697, an ARO Early Career Program (ECP) Award, a Google Research Scholar Award, the NSF AI Institute for Foundations of Machine Learning (IFML), the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), and the Machine Learning Laboratory at UT Austin.