2020 ◽  
Vol 34 (04) ◽  
pp. 5248-5255
Author(s):  
Harikrishna Narasimhan ◽  
Andrew Cotter ◽  
Maya Gupta ◽  
Serena Wang

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.


2020 ◽  
Author(s):  
Horacio Vasquez ◽  
Robert Freeman ◽  
Gerhart Hanson

2002 ◽  
Vol 45 (4-5) ◽  
pp. 255-262 ◽  
Author(s):  
J. Harmand ◽  
F. Miens ◽  
T. Conte ◽  
P. Gras ◽  
P. Buffière ◽  
...  

This paper presents the use of nonlinear constrained optimization techniques in order to detect and evaluate the degree of clogging in an anaerobic fixed bed reactor. First, experimental results show that the validity of a mass balance model can degrade over the time. Using the available model of the process and nonlinear constrained optimization tools, it is established that these changes can be due to the decrease of the liquid volume into the reactor while the mean values of biomass concentrations increase, leading to the clogging of the reactor. These theoretical results are confirmed experimentally in evaluating the hydraulic retention time of the reactor using a tracer.


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