Zobrazeno 1 - 10
of 174
pro vyhledávání: '"UDELL, MADELEINE"'
Survival analysis is a classic problem in statistics with important applications in healthcare. Most machine learning models for survival analysis are black-box models, limiting their use in healthcare settings where interpretability is paramount. Mo
Externí odkaz:
http://arxiv.org/abs/2411.05923
We introduce a framework to accelerate the convergence of gradient-based methods with online learning. The framework learns to scale the gradient at each iteration through an online learning algorithm and provably accelerates gradient-based methods a
Externí odkaz:
http://arxiv.org/abs/2411.01803
Autor:
Tyser, Keith, Segev, Ben, Longhitano, Gaston, Zhang, Xin-Yu, Meeks, Zachary, Lee, Jason, Garg, Uday, Belsten, Nicholas, Shporer, Avi, Udell, Madeleine, Te'eni, Dov, Drori, Iddo
Automatic reviewing helps handle a large volume of papers, provides early feedback and quality control, reduces bias, and allows the analysis of trends. We evaluate the alignment of automatic paper reviews with human reviews using an arena of human p
Externí odkaz:
http://arxiv.org/abs/2408.10365
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the art solvers because the expertise required to f
Externí odkaz:
http://arxiv.org/abs/2407.19633
Kernel ridge regression (KRR) is a fundamental computational tool, appearing in problems that range from computational chemistry to health analytics, with a particular interest due to its starring role in Gaussian process regression. However, it is c
Externí odkaz:
http://arxiv.org/abs/2407.10070
We present a new algorithm for convex separable quadratic programming (QP) called Nys-IP-PMM, a regularized interior-point solver that uses low-rank structure to accelerate solution of the Newton system. The algorithm combines the interior point prox
Externí odkaz:
http://arxiv.org/abs/2404.14524
Autor:
Van Ness, Mike, Udell, Madeleine
Survival analysis is widely used as a technique to model time-to-event data when some data is censored, particularly in healthcare for predicting future patient risk. In such settings, survival models must be both accurate and interpretable so that u
Externí odkaz:
http://arxiv.org/abs/2404.14689
Optimization problems are pervasive in sectors from manufacturing and distribution to healthcare. However, most such problems are still solved heuristically by hand rather than optimally by state-of-the-art solvers because the expertise required to f
Externí odkaz:
http://arxiv.org/abs/2402.10172
This paper explores challenges in training Physics-Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in minimizing the PINN loss function, particularly due to ill-conditionin
Externí odkaz:
http://arxiv.org/abs/2402.01868
We consider the problem of finding the optimal diagonal preconditioner for a positive definite matrix. Although this problem has been shown to be solvable and various methods have been proposed, none of the existing approaches are scalable to matrice
Externí odkaz:
http://arxiv.org/abs/2312.15594