Manuel Gomez Rodriguez
Manuel Gomez Rodriguez is a faculty at the Max Planck Institute for Software Systems. Manuel develops human-centered machine learning models and algorithms for the analysis, modeling and control of social, information and networked systems. He has received several recognitions for his research, including an outstanding paper award at NeurIPS’13 and a best research paper honorable mention at KDD’10 and WWW’17. He has served as track chair for FAT* 2020 and as area chair for every major conference in machine learning, data mining and the Web. Manuel has co-authored over 50 publications in top-tier conferences (NeurIPS, ICML, WWW, KDD, WSDM, AAAI) and journals (PNAS, Nature Communications, JMLR, PLOS Computational Biology). Manuel holds a BS in Electrical Engineering from Carlos III University, a MS and PhD in Electrical Engineering from Stanford University, and has received postdoctoral training at the Max Planck Institute for Intelligent Systems. You can find more about him at http://learning.mpi-sws.org.
Learning Under Algorithmic Triage
Under algorithmic triage, a machine learning model does not predict all instances but instead defers some of them to human experts. The motivation that underpins learning under algorithmic triage is the observation that, while there are high-stake tasks where machine learning models have matched, or even surpassed, the average performance of human experts, they are still less accurate than human experts on some instances, where they make far more errors than average. The main promise is that, by working together, human experts and machine learning models are likely to achieve a considerably better performance than each of them would achieve on their own. In this talk, I will present several algorithms to learn under algorithmic triage that we have developed in recent years, discuss their theoretical properties, and present a variety of experimental results demonstrating their potential in improving medical diagnosis, content moderation and scientific discovery.