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pro vyhledávání: '"Chen, Ruidi"'
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of distributions
Externí odkaz:
http://arxiv.org/abs/2210.08198
Despite their empirical success, most existing listwiselearning-to-rank (LTR) models are not built to be robust to errors in labeling or annotation, distributional data shift, or adversarial data perturbations. To fill this gap, we introduce a new li
Externí odkaz:
http://arxiv.org/abs/2109.12803
We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of distributions
Externí odkaz:
http://arxiv.org/abs/2109.12772
Autor:
Chen, Ruidi, Paschalidis, Ioannis Ch.
This monograph develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. Beginning with fundamental properties o
Externí odkaz:
http://arxiv.org/abs/2108.08993
Autor:
Chen, Ruidi
This dissertation develops a comprehensive statistical learning framework that is robust to (distributional) perturbations in the data using Distributionally Robust Optimization (DRO) under the Wasserstein metric. The learning problems that are studi
Externí odkaz:
https://hdl.handle.net/2144/38236
Autor:
Chen, Ruidi, Paschalidis, Ioannis Ch.
We propose a Distributionally Robust Optimization (DRO) formulation with a Wasserstein-based uncertainty set for selecting grouped variables under perturbations on the data for both linear regression and classification problems. The resulting model o
Externí odkaz:
http://arxiv.org/abs/2006.06094
Autor:
Chen, Ruidi, Paschalidis, Ioannis Ch.
We develop Distributionally Robust Optimization (DRO) formulations for Multivariate Linear Regression (MLR) and Multiclass Logistic Regression (MLG) when both the covariates and responses/labels may be contaminated by outliers. The DRO framework uses
Externí odkaz:
http://arxiv.org/abs/2006.06090
We consider the process of bidding by electricity suppliers in a day-ahead market context where each supplier bids a linear non-decreasing function of her generating capacity with the goal of maximizing her individual profit given other competing sup
Externí odkaz:
http://arxiv.org/abs/1811.06113
Autor:
Chen, Ruidi, Paschalidis, Ioannis
We develop a prediction-based prescriptive model for learning optimal personalized treatments for patients based on their Electronic Health Records (EHRs). Our approach consists of: (i) predicting future outcomes under each possible therapy using a r
Externí odkaz:
http://arxiv.org/abs/1811.06083