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Designing and Analyzing Machine Learning Algorithms in the Presence of Strategic Behavior
Speaker: Hanrui ZHANG Carnegie Mellon University Title: "Designing and Analyzing Machine Learning Algorithms in the Presence of Strategic Behavior" Date: 30 Jan 2023 Time: 10:00am - 11:00am Zoom link: https://hkust.zoom.us/j/465698645?pwd=aVRaNWs2RHNFcXpnWGlkR05wTTk3UT09 Meeting ID: 465 698 645 Passcode: 20222023 Abstract: Machine learning algorithms now play a major part in all kinds of decision-making scenarios. When the stakes are high, self-interested agents --- about whom decisions are being made --- are increasingly tempted to manipulate the machine learning algorithm, in order to better fulfill their own goals, which are generally different from the decision maker's. This highlights the importance of making machine learning algorithms robust against manipulation. In this talk, I will discuss some of the most important meta-problems in machine learning in the presence of such strategic behavior: 1. Empirical risk minimization and generalization in classification problems: Traditional wisdom suggests that a classifier trained on historical observations (i.e, an empirical risk minimizer) usually also works well on future data points to be classified. Is this still true in the presence of strategic manipulation? 2. Distinguishing distributions with samples: Due to various constraints, often we have to judge the quality of a data point based on a few samples (e.g., screening job candidates based on a few representative papers). How should we calibrate our judgment when these samples are strategically selected or transformed? 3. Planning in Markov decision processes: Dynamic decision-making problems (traditionally modeled using Markov decision processes) can be solved efficiently when the decision maker always has complete and reliable information about the state of the world, as well as full control over which actions to take. What happens when the state of the world is reported by a strategic agent, or when a self-interested agent may interfere with the actions taken? ******************** Biography: Hanrui Zhang is a PhD student at Carnegie Mellon University, advised by Vincent Conitzer. He was named a finalist for the 2021 Facebook Fellowship. His work won the Best Student Paper Award at the European Symposia on Algorithms (ESA), and a Honorable Mention for Best Paper Award at the AAAI Conference on Human Computation and Crowdsourcing (HCOMP). He received his bachelor's degree in Yao's Class, Tsinghua University, where he won the Outstanding Undergraduate Thesis Award.