no code implementations • 27 Feb 2024 • Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger, Sammy Jerome, Vahab Mirrokni, David Saulpic, David Woodruff, Michael Wunder
We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model.
no code implementations • 18 Jul 2023 • Monika Henzinger, Jalaj Upadhyay, Sarvagya Upadhyay
We give a constructive proof for an almost exact upper bound on the $\gamma_2$ and $\gamma_F$ norm and an almost tight lower bound on the $\gamma_2$ norm for a large class of lower-triangular matrices.
no code implementations • 7 Jul 2023 • Max Dupré la Tour, Monika Henzinger, David Saulpic
We consider the problem of clustering privately a dataset in $\mathbb{R}^d$ that undergoes both insertion and deletion of points.
no code implementations • 9 Nov 2022 • Monika Henzinger, Jalaj Upadhyay, Sarvagya Upadhyay
Our lower bound for any continual counting mechanism is the first tight lower bound on continual counting under approximate differential privacy.
no code implementations • 23 Feb 2022 • Hendrik Fichtenberger, Monika Henzinger, Jalaj Upadhyay
Finally, we note that our result can be used to get a fine-grained error bound for non-interactive local learning {and the first lower bounds on the additive error for $(\epsilon,\delta)$-differentially-private counting under continual observation.}
no code implementations • 22 Feb 2021 • Kathrin Hanauer, Monika Henzinger, Christian Schulz
In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms.
Data Structures and Algorithms
1 code implementation • 13 Jan 2021 • Monika Henzinger, Alexander Noe, Christian Schulz
We present a practically efficient algorithm for maintaining a global minimum cut in large dynamic graphs under both edge insertions and deletions.
Data Structures and Algorithms
1 code implementation • 24 Apr 2020 • Monika Henzinger, Alexander Noe, Christian Schulz
We give an improved branch-and-bound solver for the multiterminal cut problem, based on the recent work of Henzinger et al.. We contribute new, highly effective data reduction rules to transform the graph into a smaller equivalent instance.
Data Structures and Algorithms Combinatorics
1 code implementation • 17 Feb 2020 • Monika Henzinger, Alexander Noe, Christian Schulz, Darren Strash
We present a practically efficient algorithm that finds all global minimum cuts in huge undirected graphs.
Data Structures and Algorithms
no code implementations • 12 Aug 2019 • Monika Henzinger, Alexander Noe, Christian Schulz
We introduce the fastest known exact algorithm~for~the multiterminal cut problem with k terminals.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
2 code implementations • 16 Aug 2018 • Monika Henzinger, Alexander Noe, Christian Schulz
State-of-the-art algorithms like the algorithm of Padberg and Rinaldi or the algorithm of Nagamochi, Ono and Ibaraki identify edges that can be contracted to reduce the graph size such that at least one minimum cut is maintained in the contracted graph.
Data Structures and Algorithms
no code implementations • 19 Apr 2018 • Krishnendu Chatterjee, Wolfgang Dvořák, Monika Henzinger, Alexander Svozil
For the sequential target problem, we present a linear-time algorithm for graphs, a sub-quadratic algorithm for MDPs, and a quadratic conditional lower bound for games on graphs.
1 code implementation • 20 Feb 2018 • Sonja Biedermann, Monika Henzinger, Christian Schulz, Bernhard Schuster
It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches.
2 code implementations • 21 Aug 2017 • Monika Henzinger, Alexander Noe, Christian Schulz, Darren Strash
The minimum cut problem for an undirected edge-weighted graph asks us to divide its set of nodes into two blocks while minimizing the weight sum of the cut edges.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
no code implementations • ICML 2017 • Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao
As an application, we use our CRD Process to develop an improved local algorithm for graph clustering.
no code implementations • 19 Jun 2017 • Di Wang, Kimon Fountoulakis, Monika Henzinger, Michael W. Mahoney, Satish Rao
Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier.