Optimization & Control

Clément Hongler, Franck Gabriel, Valentin Hartmann et al.

This work addresses the open problem of developing general AI capabilities through automated curriculum learning, potentially impacting the entire field of artificial intelligence if successful.

Dmitry Kovalev

This work addresses incremental improvements in optimization algorithms for machine learning, specifically for matrix optimization problems.

Mohammad Javad Khojasteh, Massimo Franceschetti, Gireeja Ranade

This work addresses the challenge of event-triggering control with zero-payload rate for systems like phone call networks, generalizing previous strategies and revealing fundamental limits in using timing information for stabilization.

Yuhan Chen, Tao Liu, Jie Huang

This solves a long-standing challenge in control theory for multi-agent systems, enabling applications like robotics and autonomous vehicles with unstable dynamics, though it is a foundational breakthrough rather than incremental.

Tianshu Chu, Dachuan Xu, Wei Yao et al.

This resolves a theoretical gap in machine learning optimization, providing efficient algorithms for large-scale nested problems, though it is incremental as it adapts an existing method to a specific setting.

Jianghao Lin, Zi Ling, Chenyu Zhou et al.

For practitioners needing reliable optimization modeling from natural language, Agora-Opt provides a practical and extensible foundation that improves over existing methods.

Bo Wang, Miroslav Krstic

This work provides explicit constructive alternatives to CLF-CBF quadratic programming for scalar-input nonlinear systems, which is incremental as it builds on existing universal stabilizer formulas.

Liviu Aolaritei, Ricky Huang, Michael I. Jordan et al.

This work provides insights into the computational landscape of robust graph optimization under a topology-aware uncertainty model, which is relevant for researchers and practitioners in networked systems like transportation and logistics.

Jon Arrizabalaga, Zachary Manchester

This addresses a key limitation in solving large-scale quadratic programs to high precision, offering potential improvements in computational efficiency and accuracy.

Wenwen Li, Daniel Sanz-Alonso

For practitioners of data assimilation, this work provides theoretical guarantees for using machine learning surrogates in nudging algorithms, addressing model error when true dynamics are unknown or expensive.

Tim Tsz-Kit Lau, Weijie Su

This work addresses the geometric disparity between neural network symmetries and coordinate-wise optimizers, offering a principled framework for designing layerwise optimizers that improve training of large language models.

Xiaoyang Wang, Xin Chen

For power system operators, this work provides a scalable, distributed solution to frequency control in inverter-dominated grids, addressing a critical challenge in renewable integration.

Ding Zhang, Di Zhao, Philipp Braun et al.

Provides theoretical foundations for robust stability analysis of MIMO LTI systems under symmetric uncertainties, offering a unified framework that generalizes existing conditions.

Lesi Chen, Jing Xu, Jingzhao Zhang

For researchers in bilevel optimization, this work clarifies the boundary between tractable and intractable problems and provides optimal algorithms for a practically relevant class of nonconvex-nonconvex problems.

Yu Hin Au, Levent Tunçel

For researchers in combinatorial optimization and semidefinite programming, this resolves a long-standing open problem about the exact size of graphs achieving a given LS_+ rank.

Zijian Wang, Jeremy G. Hoskins, Manas Rachh et al.

This addresses a theoretical problem in mathematical physics for researchers studying heat diffusion and spectral geometry, with incremental analytical support for the conjecture.

Jiayu Zhang, Tianyi Lin

For researchers designing optimizers for deep neural networks, this work provides theoretical foundations and practical algorithms for scale-invariant methods under realistic heavy-tailed noise.

Iasson Karafyllis, Dionysios Theodosis, Miroslav Krstic

This work addresses stability in complex biological systems like chemostats, providing rigorous mathematical tools for researchers in mathematical biology and control theory, though it is incremental as it builds on existing models with a new analytical approach.

Shaohui Liu

This work addresses the emerging problem of efficient dispatch and pricing for geographically distributed AI service infrastructure, providing a foundational market-clearing framework for future competitive markets.

Boyang Zhang, Zhiguo Wang, Ya-Feng Liu

This work provides a novel solution to the likelihood score intractability in score-based diffusion models for inverse problems, benefiting applications in imaging and scientific computing.