On this recent article in the Quanta magazine, Alex Dixon, who wrote in Haskell the first solver for the problem, commented:

For the past 50 years, Vector Addition Systems—a simple but powerful computational model—have been a topic of great interest in theoretical CS. The reachability problem in that model asks whether we can get from some configuration to another.

The problem sounds relatively easy on a first glance, and an exponential lower bound held firm for over 40 years. Work by excellent theoreticians, including familiar names from Warwick DCS, finally closed the difficulty of the problem in 2021, concluding that it is very, very difficult indeed.

We are seeking PhD candidates in the topic of Multiagent Systems and related areas, with particular emphasis on one or more of: computational social choice, algorithmic game theory, multiagent learning, and social and economic networks. The multiagent systems researchers at University of Warwick include Markus BrillLink opens in a new windowLink opens in a new window, Ramanujan SridharanLink opens in a new windowLink opens in a new window, Long Tran-ThanhLink opens in a new windowLink opens in a new window, Debmalya Mandal and Paolo TurriniLink opens in a new window

Seven papers authored by Computer Science researchers from Warwick have been accepted for publication at the 37th Conference on Neural Information Processing Systems, the leading international venue for machine learning research, which will be held on 10-16 December 2023 in New Orleans, Louisiana, USA:

• EV-Eye: Rethinking High-frequency Eye Tracking through the Lenses of Event Cameras, by Guangrong Zhao, Yurun Yang, Jingwei Liu, Ning Chen, Yiran Shen, Hongkai Wen, and Guohao Lan
• Fully Dynamic k-Clustering in Õ(k) Update Time, by Sayan Bhattacharya, Martin Costa, Silvio Lattanzi, and Nikos Parotsidis
• Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks, by Jiayuan Ye, Zhenyu Zhu, Fanghui Liu, Reza Shokri, and Volkan Cevher
• Learning a Neuron by a Shallow ReLU Network: Dynamics and Implicit Bias for Correlated Inputs, by Dmitry Chistikov, Matthias Englert, and Ranko Lazic
• On the Convergence of Shallow Transformers, by Yongtao Wu, Fanghui Liu, Grigorios Chrysos, and Volkan Cevher
• Towards Data-Agnostic Pruning At Initialization: What Makes a Good Sparse Mask? by Hoang Pham, The Anh Ta, Shiwei Liu, Lichuan Xiang, Dung Le, Hongkai Wen, and Long Tran-Thanh
• Towards Unbounded Machine Unlearning, by Meghdad Kurmanji, Peter Triantafillou, and Eleni Triantafillou