9 papers
Tree Search With Distributional Predictions
Michael Dinitz, Bob Dong
Learning-augmented algorithms use machine-learned predictions to improve classical algorithmic guarantees when the predictions are accurate, while retaining rigorous performance gu…
Ski Rental with Distributional Predictions of Unknown Quality
Qiming Cui, Michael Dinitz
We revisit the central online problem of ski rental in the "algorithms with predictions" framework from the point of view of distributional predictions. Ski rental was one of the f…
Learning-Augmented Online Algorithms for Nonclairvoyant Joint Replenishment Problem with Deadlines
Michael Dinitz, Jeremy T. Fineman, Seeun William Umboh
This paper considers using predictions in the context of the online Joint Replenishment Problem with Deadlines (JRP-D). Prior work includes asymptotically optimal competitive ratio…
Controlling tail risk in two-slope ski rental
Qiming Cui, Michael Dinitz
We study the optimal solution to a general two-slope ski rental problem with a tail risk, i.e., the chance of the competitive ratio exceeding a value is bounded by . This ex…
A Generalized Binary Tree Mechanism for Differentially Private Approximation of All-Pair Distances
Michael Dinitz, Chenglin Fan, Jingcheng Liu +2
We study the problem of approximating all-pair distances in a weighted undirected graph with differential privacy, introduced by Sealfon [Sea16]. Given a publicly known undirected…
Edge-Private Matching Kernels Through Local Decoding
Michael Dinitz, George Z. Li, Quanquan C. Liu +1
A matching is a set of edges with no shared endpoints. We study how participants can retain useful matching structure when each knows its own relationships but those relationships…
Noise is All You Need: Private Second-Order Convergence of Noisy SGD
Dmitrii Avdiukhin, Michael Dinitz, Chenglin Fan +1
Private optimization is a topic of major interest in machine learning, with differentially private stochastic gradient descent (DP-SGD) playing a key role in both theory and practi…
Parallel Set Cover and Hypergraph Matching via Uniform Random Sampling
Laxman Dhulipala, Michael Dinitz, Jakub Łącki +1
The SetCover problem has been extensively studied in many different models of computation, including parallel and distributed settings. From an approximation point of view, there a…
Differentially Private Algorithms for Graph Cuts: A Shifting Mechanism Approach and More
Rishi Chandra, Michael Dinitz, Chenglin Fan +1
In this paper, we address the challenge of differential privacy in the context of graph cuts, specifically focusing on the multiway cut and the minimum -cut. We introduce edge-d…