| Sep 30, 2026 | Our group gathered at Parkhotel Weiskirchen from September 28–30 for our group retreat. It was a chance to exchange research ideas, discuss ongoing work, and spend time together outside the office. |
| Sep 27, 2026 | Three papers from our group and collaborators have been accepted at NeurIPS 2026: Forgetting Has Neighbors: Localized Collateral Forgetting in Machine Unlearning, Efficient Gradient Methods for Distributed Saddle Problems, and What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity. |
| Jul 05, 2026 | This week, we are presenting two papers at the International Conference on Machine Learning (ICML) in Seoul, Korea. In Enhancing LLM Training via Spectral Clipping [CODE], we propose SPECTRA, a simple wrapper that consistently improves coordinate-wise optimizers (AdamW, Signum, etc.), leading to better convergence and generalization. In Improved Convergence Analysis of Topology Dependence in Decentralized SGD, we develop a tighter convergence analysis of decentralized SGD, offering a more precise understanding of how network topology affects its convergence rate. |
| May 08, 2026 | We were honored to take part in the ELLIIT Symposium in Lund. ELLIIT’s focus period is a fantastic initiative, launched in 2017 with its first program on Large-Scale and Distributed Optimization, and this year centers on Optimization for Learning. We are especially grateful that Yuan Gao was invited to stay on to participate in the focus period and collaborate with other visiting scholars. |
| Apr 23, 2026 | Xiaowen Jiang presents oral paper at ICLR 2026 |
| Jan 12, 2026 | We were honored with the 2023 Charles Broyden Prize for our paper Stochastic distributed learning with gradient quantization and double-variance reduction. The Charles Broyden Prize is an annual international award honoring the best paper published in the journal Optimization Methods and Software (OMS) during the preceding year. The work highlights how fundamental optimization research enables more efficient, scalable machine learning systems. Most of the results where derived durin a reserach visit at KAUST, and in joint work with Samuel Horváth, Dmitry Kovalev, Konstantin Mishchenko, Peter Richtárik. |
| Dec 02, 2025 | Ali Zindari is presenting his NeurIPS paper with new upper and lower bounds on the convergence of local SGD at EurIPS. |
