activity
20162022
most citedCustomizing ML Predictions for Online Algorithms

14 citations · 25 across the 14 of their papers we have counts for

collaborators

22 papers

cs.DS2022

Steiner Connectivity Augmentation and Splitting-off in Poly-logarithmic Maximum Flows

Ruoxu Cen, William He, Jason Li +1

We give an almost-linear time algorithm for the Steiner connectivity augmentation problem: given an undirected graph, find a smallest (or minimum weight) set of edges whose additio…

cs.LG20222 cited

A Regression Approach to Learning-Augmented Online Algorithms

Keerti Anand, Rong Ge, Amit Kumar +1

The emerging field of learning-augmented online algorithms uses ML techniques to predict future input parameters and thereby improve the performance of online algorithms. Since the…

cs.LG202214 cited

Customizing ML Predictions for Online Algorithms

Keerti Anand, Rong Ge, Debmalya Panigrahi

A popular line of recent research incorporates ML advice in the design of online algorithms to improve their performance in typical instances. These papers treat the ML algorithm a…

cs.DS2022

Edge Connectivity Augmentation in Near-Linear Time

Ruoxu Cen, Jason Li, Debmalya Panigrahi

We give an -time algorithm for the edge connectivity augmentation problem and the closely related edge splitting-off problem. This is optimal up to lower order terms…

cs.DS20211 cited

Augmenting Edge Connectivity via Isolating Cuts

Ruoxu Cen, Jason Li, Debmalya Panigrahi

We give an algorithm for augmenting the edge connectivity of an undirected graph by using the isolating cuts framework (Li and Panigrahi, FOCS '20). Our algorithm uses poly-logarit…

cs.DS20213 cited

Approximate Gomory-Hu Tree Is Faster Than Max-Flows

Jason Li, Debmalya Panigrahi

The Gomory-Hu tree or cut tree (Gomory and Hu, 1961) is a classic data structure for reporting mincuts (and by duality, the values of maxflows) for all pairs of ver…