DeLTA Seminar by Krikamol Muandet
Title
Toward Reliable Machine Learning with Instruments
Speaker
Krikamol Muanded, Max Planck Institute for Intelligent Systems
Abstract
Society is made up of a set of diverse individuals, demographic groups, and institutions. Learning and deploying algorithmic models across these heterogeneous environments face a set of various trade-offs. In order to develop reliable machine learning algorithms that can interact successfully with the real world, it is necessary to deal with such heterogeneity. In this talk, I will focus on how to employ an instrumental variable (IV) to alleviate the impact of unobserved confounders on the credibility of algorithmic decision-making and the reliability of machine learning models that are learned from observational and heterogeneous data. Lastly, I will argue that a better understanding of the ways in which our data are generated and how our models can influence them will be crucial for reliable machine learning systems, especially when gaining full information about data may not be possible.
Bio
Krikamol Muandet is currently a research group leader in the Empirical Inference Department at the Max Planck Institute for Intelligent Systems (MPI-IS), Tübingen, Germany. Previously, he was a lecturer in the Department of Mathematics at Mahidol University, Bangkok, Thailand. He received his Ph.D. in computer science from the University of Tübingen in 2015 working mainly with Prof. Bernhard Schölkopf. He received his master's degree in machine learning from University College London (UCL), the United Kingdom where he worked mostly with Prof. Yee Whye Teh at Gatsby Computational Neuroscience Unit. He served as a publication chair of AISTATS 2021 and as an area chair for AISTATS 2022, NeurIPS 2021, NeurIPS 2020, NeurIPS 2019, and ICML 2019, among others.
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DeLTA is a research group affiliated with the Department of Computer Science at the University of Copenhagen studying diverse aspects of Machine Learning Theory and its applications, including, but not limited to Reinforcement Learning, Online Learning and Bandits, PAC-Bayesian analysis