Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Digalakis Jr., Vassilis"'
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
We present BackboneLearn: an open-source software package and framework for scaling mixed-integer optimization (MIO) problems with indicator variables to high-dimensional problems. This optimization paradigm can naturally be used to formulate fundame
Externí odkaz:
http://arxiv.org/abs/2311.13695
Owing to their inherently interpretable structure, decision trees are commonly used in applications where interpretability is essential. Recent work has focused on improving various aspects of decision trees, including their predictive power and robu
Externí odkaz:
http://arxiv.org/abs/2305.17299
We present our collaboration with the OCP Group, one of the world's largest producers of phosphate and phosphate-based products, to reduce OCP's carbon emissions significantly. We study the problem of decarbonizing OCP's electricity supply by install
Externí odkaz:
http://arxiv.org/abs/2209.06341
Differential privacy is a rigorous definition for privacy that guarantees that any analysis performed on a sensitive dataset leaks no information about the individuals whose data are contained therein. In this work, we develop new differentially priv
Externí odkaz:
http://arxiv.org/abs/2111.08784
We present the framework of slowly varying regression under sparsity, allowing sparse regression models to exhibit slow and sparse variations. The problem of parameter estimation is formulated as a mixed-integer optimization problem. We demonstrate t
Externí odkaz:
http://arxiv.org/abs/2102.10773
Autor:
Bertsimas, Dimitris, Digalakis Jr., Vassilis, Jacquillat, Alexander, Li, Michael Lingzhi, Previero, Alessandro
The outbreak of COVID-19 led to a record-breaking race to develop a vaccine. However, the limited vaccine capacity creates another massive challenge: how to distribute vaccines to mitigate the near-end impact of the pandemic? In the United States in
Externí odkaz:
http://arxiv.org/abs/2102.07309
We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to ma
Externí odkaz:
http://arxiv.org/abs/2007.09261
Autor:
Bertsimas, Dimitris, Boussioux, Léonard, Wright, Ryan Cory, Delarue, Arthur, Digalakis Jr., Vassilis, Jacquillat, Alexandre, Kitane, Driss Lahlou, Lukin, Galit, Li, Michael Lingzhi, Mingardi, Luca, Nohadani, Omid, Orfanoudaki, Agni, Papalexopoulos, Theodore, Paskov, Ivan, Pauphilet, Jean, Lami, Omar Skali, Stellato, Bartolomeo, Bouardi, Hamza Tazi, Carballo, Kimberly Villalobos, Wiberg, Holly, Zeng, Cynthia
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to s
Externí odkaz:
http://arxiv.org/abs/2006.16509
We present the backbone method, a generic framework that enables sparse and interpretable supervised machine learning methods to scale to ultra-high dimensional problems. We solve sparse regression problems with $10^7$ features in minutes and $10^8$
Externí odkaz:
http://arxiv.org/abs/2006.06592