Zobrazeno 1 - 10
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pro vyhledávání: '"Musco, A"'
We study algorithms for approximating the spectral density of a symmetric matrix $A$ that is accessed through matrix-vector product queries. By combining a previously studied Chebyshev polynomial moment matching method with a deflation step that appr
Externí odkaz:
http://arxiv.org/abs/2410.21690
Autor:
Musco, Christopher, Witter, R. Teal
Originally introduced in game theory, Shapley values have emerged as a central tool in explainable machine learning, where they are used to attribute model predictions to specific input features. However, computing Shapley values exactly is expensive
Externí odkaz:
http://arxiv.org/abs/2410.01917
Autor:
Mullins, Brett, Fuentes, Miguel, Xiao, Yingtai, Kifer, Daniel, Musco, Cameron, Sheldon, Daniel
Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that re
Externí odkaz:
http://arxiv.org/abs/2410.01091
Autor:
Musco, Fabio, Barth, Andrea
This work considers stochastic Galerkin approximations of linear elliptic partial differential equations with stochastic forcing terms and stochastic diffusion coefficients, that cannot be bounded uniformly away from zero and infinity. A traditional
Externí odkaz:
http://arxiv.org/abs/2409.08063
Autor:
Witter, R. Teal, Musco, Christopher
Estimating the effect of treatments from natural experiments, where treatments are pre-assigned, is an important and well-studied problem. We introduce a novel natural experiment dataset obtained from an early childhood literacy nonprofit. Surprising
Externí odkaz:
http://arxiv.org/abs/2409.04500
We study the problem of approximately recovering a probability distribution given noisy measurements of its Chebyshev polynomial moments. We sharpen prior work, proving that accurate recovery in the Wasserstein distance is possible with more noise th
Externí odkaz:
http://arxiv.org/abs/2408.12385
Autor:
Chen, Tyler, Keles, Feyza Duman, Halikias, Diana, Musco, Cameron, Musco, Christopher, Persson, David
Publikováno v:
SIAM Symposium on Discrete Algorithms (SODA 2025)
We describe a randomized algorithm for producing a near-optimal hierarchical off-diagonal low-rank (HODLR) approximation to an $n\times n$ matrix $\mathbf{A}$, accessible only though matrix-vector products with $\mathbf{A}$ and $\mathbf{A}^{\mathsf{T
Externí odkaz:
http://arxiv.org/abs/2407.04686
Suppose Alice has a distribution $P$ and Bob has a distribution $Q$. Alice wants to generate a sample $a\sim P$ and Bob a sample $b \sim Q$ such that $a = b$ with has as high of probability as possible. It is well-known that, by sampling from an opti
Externí odkaz:
http://arxiv.org/abs/2408.07978
Autor:
Daneshvaramoli, Mohammadreza, Karisani, Helia, Lechowicz, Adam, Sun, Bo, Musco, Cameron, Hajiesmaili, Mohammad
In the online knapsack problem, the goal is to pack items arriving online with different values and weights into a capacity-limited knapsack to maximize the total value of the accepted items. We study \textit{learning-augmented} algorithms for this p
Externí odkaz:
http://arxiv.org/abs/2406.18752
We consider the problem of estimating the spectral density of the normalized adjacency matrix of an $n$-node undirected graph. We provide a randomized algorithm that, with $O(n\epsilon^{-2})$ queries to a degree and neighbor oracle and in $O(n\epsilo
Externí odkaz:
http://arxiv.org/abs/2406.07521