Search Results for author:
Found 16 papers, 6 papers with code
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
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.
A Unifying Framework for Differentially Private Sums under Continual Observation
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.
Differential Privacy for Clustering Under Continual Observation
We consider the problem of clustering privately a dataset in $\mathbb{R}^d$ that undergoes both insertion and deletion of points.
Almost Tight Error Bounds on Differentially Private Continual Counting
Our lower bound for any continual counting mechanism is the first tight lower bound on continual counting under approximate differential privacy.
Constant matters: Fine-grained Complexity of Differentially Private Continual Observation
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.}
Recent Advances in Fully Dynamic Graph Algorithms
In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms.
Data Structures and Algorithms
Practical Fully Dynamic Minimum Cut Algorithms
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
Faster Parallel Multiterminal Cuts
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
Finding All Global Minimum Cuts In Practice
We present a practically efficient algorithm that finds all global minimum cuts in huge undirected graphs.
Data Structures and Algorithms
Shared-Memory Branch-and-Reduce for Multiterminal Cuts
We introduce the fastest known exact algorithm~for~the multiterminal cut problem with k terminals.
Data Structures and Algorithms Distributed, Parallel, and Cluster Computing
Shared-memory Exact Minimum Cuts
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
Algorithms and Conditional Lower Bounds for Planning Problems
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.
Memetic Graph Clustering
It is common knowledge that there is no single best strategy for graph clustering, which justifies a plethora of existing approaches.
Practical Minimum Cut Algorithms
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
Capacity Releasing Diffusion for Speed and Locality.
As an application, we use our CRD Process to develop an improved local algorithm for graph clustering.
Capacity Releasing Diffusion for Speed and Locality
Thus, our CRD Process is the first local graph clustering algorithm that is not subject to the well-known quadratic Cheeger barrier.
