演讲人： Kaja Gruntkowska KAUST
时间： 2025-04-16 11:00-2025-04-16 12:00
地点：FIT 1-222
内容：

In practical distributed systems, workers are typically not homogeneous, and due to differences in hardware configurations and network conditions, can have highly varying processing times. We consider smooth nonconvex finite-sum (empirical risk minimization) problems in this setup and introduce a new parallel method, Freya PAGE, designed to handle arbitrarily heterogeneous and asynchronous computations. By being robust to "stragglers" and adaptively ignoring slow computations, Freya PAGE offers significantly improved time complexity guarantees compared to all previous methods, including Asynchronous SGD, Rennala SGD, SPIDER, and PAGE, while requiring weaker assumptions. The algorithm relies on novel generic stochastic gradient collection strategies with theoretical guarantees that can be of interest on their own, and may be used in the design of future optimization methods. Furthermore, we establish a lower bound for smooth nonconvex finite-sum problems in the asynchronous setup, providing a fundamental time complexity limit. This lower bound is tight and demonstrates the optimality of Freya PAGE in the large-scale regime.

个人简介:

Kaja Gruntkowska is a PhD student in Optimization for Machine Learning at KAUST, advised by Prof. Peter Richtárik. Her research focuses on developing the algorithmic and mathematical foundations of randomized optimization, with a particular emphasis on distributed computing. She works on designing practically motivated algorithms with provable convergence guarantees, bridging theory and real-world applications to advance scalable machine learning. She completed her Bachelor's in Mathematics and Statistics at the University of Warwick and earned a Master's in Statistical Science from the University of Oxford.