Zobrazeno 1 - 10
of 344
pro vyhledávání: '"Drineas P"'
Autor:
Drineas, Petros, Ipsen, Ilse C. F.
Stochastic Rounding is a probabilistic rounding mode that is surprisingly effective in large-scale computations and low-precision arithmetic. Its random nature promotes error cancellation rather than error accumulation, resulting in slower growth of
Externí odkaz:
http://arxiv.org/abs/2410.10517
Motivated by the popularity of stochastic rounding in the context of machine learning and the training of large-scale deep neural network models, we consider stochastic nearness rounding of real matrices $\mathbf{A}$ with many more rows than columns.
Externí odkaz:
http://arxiv.org/abs/2403.12278
Sketching algorithms have recently proven to be a powerful approach both for designing low-space streaming algorithms as well as fast polynomial time approximation schemes (PTAS). In this work, we develop new techniques to extend the applicability of
Externí odkaz:
http://arxiv.org/abs/2310.19068
Autor:
Boutsikas, Christos, Drineas, Petros, Mertzanidis, Marios, Psomas, Alexandros, Verma, Paritosh
We consider the problem of a revenue-maximizing seller with a large number of items $m$ for sale to $n$ strategic bidders, whose valuations are drawn independently from high-dimensional, unknown prior distributions. It is well-known that optimal and
Externí odkaz:
http://arxiv.org/abs/2310.07874
We present novel bounds for coreset construction, feature selection, and dimensionality reduction for logistic regression. All three approaches can be thought of as sketching the logistic regression inputs. On the coreset construction front, we resol
Externí odkaz:
http://arxiv.org/abs/2303.14284
We perturb a real matrix $A$ of full column rank, and derive lower bounds for the smallest singular values of the perturbed matrix, in terms of normwise absolute perturbations. Our bounds, which extend existing lower-order expressions, demonstrate th
Externí odkaz:
http://arxiv.org/abs/2303.03547
Linear programming (LP) is an extremely useful tool which has been successfully applied to solve various problems in a wide range of areas, including operations research, engineering, economics, or even more abstract mathematical areas such as combin
Externí odkaz:
http://arxiv.org/abs/2209.08722
Interior point methods (IPMs) are a common approach for solving linear programs (LPs) with strong theoretical guarantees and solid empirical performance. The time complexity of these methods is dominated by the cost of solving a linear system of equa
Externí odkaz:
http://arxiv.org/abs/2202.01756
Models in which the covariance matrix has the structure of a sparse matrix plus a low rank perturbation are ubiquitous in data science applications. It is often desirable for algorithms to take advantage of such structures, avoiding costly matrix com
Externí odkaz:
http://arxiv.org/abs/2201.13156
We study the problem of approximating the eigenspectrum of a symmetric matrix $\mathbf A \in \mathbb{R}^{n \times n}$ with bounded entries (i.e., $\|\mathbf A\|_{\infty} \leq 1$). We present a simple sublinear time algorithm that approximates all eig
Externí odkaz:
http://arxiv.org/abs/2109.07647