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of 1 263
pro vyhledávání: '"Bertsimas, Dimitris"'
Autor:
Bertsimas, Dimitris, Peroni, Matthew
As a multitude of capable machine learning (ML) models become widely available in forms such as open-source software and public APIs, central questions remain regarding their use in real-world applications, especially in high-stakes decision-making.
Externí odkaz:
http://arxiv.org/abs/2405.20486
Autor:
Bertsimas, Dimitris, Zeng, Cynthia
The escalating frequency and severity of natural disasters, exacerbated by climate change, underscore the critical role of insurance in facilitating recovery and promoting investments in risk reduction. This work introduces a novel Adaptive Robust Op
Externí odkaz:
http://arxiv.org/abs/2405.07068
Autor:
Bertsimas, Dimitris, Ma, Yu
Developing an integrated many-to-many framework leveraging multimodal data for multiple tasks is crucial to unifying healthcare applications ranging from diagnoses to operations. In resource-constrained hospital environments, a scalable and unified m
Externí odkaz:
http://arxiv.org/abs/2404.18975
We consider the task of retraining machine learning (ML) models when new batches of data become available. Existing methods focus largely on greedy approaches to find the best-performing model for each batch, without considering the stability of the
Externí odkaz:
http://arxiv.org/abs/2403.19871
When training predictive models on data with missing entries, the most widely used and versatile approach is a pipeline technique where we first impute missing entries and then compute predictions. In this paper, we view prediction with missing data
Externí odkaz:
http://arxiv.org/abs/2402.01543
Autor:
Gurnee, Wes, Horsley, Theo, Guo, Zifan Carl, Kheirkhah, Tara Rezaei, Sun, Qinyi, Hathaway, Will, Nanda, Neel, Bertsimas, Dimitris
A basic question within the emerging field of mechanistic interpretability is the degree to which neural networks learn the same underlying mechanisms. In other words, are neural mechanisms universal across different models? In this work, we study th
Externí odkaz:
http://arxiv.org/abs/2401.12181
Autor:
Bertsimas, Dimitris, Ma, Yu
We propose a new formulation of robust regression by integrating all realizations of the uncertainty set and taking an averaged approach to obtain the optimal solution for the ordinary least-squared regression problem. We show that this formulation s
Externí odkaz:
http://arxiv.org/abs/2311.06960
Many approaches for addressing Global Optimization problems typically rely on relaxations of nonlinear constraints over specific mathematical primitives. This is restricting in applications with constraints that are black-box, implicit or consist of
Externí odkaz:
http://arxiv.org/abs/2311.01742
We propose a prognostic stratum matching framework that addresses the deficiencies of Randomized trial data subgroup analysis and transforms ObservAtional Data to be used as if they were randomized, thus paving the road for precision medicine. Our ap
Externí odkaz:
http://arxiv.org/abs/2311.01681
The increase of renewables in the grid and the volatility of the load create uncertainties in the day-ahead prices of electricity markets. Adaptive robust optimization (ARO) and stochastic optimization have been used to make commitment and dispatch d
Externí odkaz:
http://arxiv.org/abs/2309.08162