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20172022
most citedImproved Deep Neural Network Generalization Using m-Sharpness-Aware Minimization

3 citations · 3 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.DS2019

Parallel Batch-Dynamic Graphs: Algorithms and Lower Bounds

David Durfee, Laxman Dhulipala, Janardhan Kulkarni +3

In this paper we study the problem of dynamically maintaining graph properties under batches of edge insertions and deletions in the massively parallel model of computation. In thi…

cs.DS2019

Fully Dynamic Spectral Vertex Sparsifiers and Applications

David Durfee, Yu Gao, Gramoz Goranci +1

We study \emph{dynamic} algorithms for maintaining spectral vertex sparsifiers of graphs with respect to a set of terminals of our choice. Such objects preserve pairwise resist…

cs.DS2019

Efficient Second-Order Shape-Constrained Function Fitting

David Durfee, Yu Gao, Anup B. Rao +1

We give an algorithm to compute a one-dimensional shape-constrained function that best fits given data in weighted- norm. We give a single algorithm that works for a va…

cs.DS2018

Fully Dynamic Effective Resistances

David Durfee, Yu Gao, Gramoz Goranci +1

In this paper we consider the \emph{fully-dynamic} All-Pairs Effective Resistance problem, where the goal is to maintain effective resistances on a graph among any pair of quer…

cs.DS2018

Individual Sensitivity Preprocessing for Data Privacy

Rachel Cummings, David Durfee

The sensitivity metric in differential privacy, which is informally defined as the largest marginal change in output between neighboring databases, is of substantial significance i…

cs.DS2017

Determinant-Preserving Sparsification of SDDM Matrices with Applications to Counting and Sampling Spanning Trees

David Durfee, John Peebles, Richard Peng +1

We show variants of spectral sparsification routines can preserve the total spanning tree counts of graphs, which by Kirchhoff's matrix-tree theorem, is equivalent to determinant o…