Tuesday, September 27, 2022 - 12:00pm to 1:30pm
Location:In Person and Virtual - ET Reddy Conference Room, Gates Hillman 4405 and Zoom
Speaker:HANRUI ZHANG, Ph.D. StudentComputer Science DepartmentCarnegie Mellon University
Designing and Analyzing Machine Learning Algorithms in the Presence of Strategic Manipulation
Machine learning algorithms now play a major part in all kinds of decision-making scenarios, such as college admissions, credit approval, and resume screening. When the stakes are high, self-interested agents --- about which 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. The main focus of my research is on designing and analyzing machine learning algorithms that are robust against strategic manipulation, which is different from the relatively well-studied notion of adversarial robustness.
My research sets the foundations for some of the most important meta-problems in machine learning in the presence of strategic behavior:
1. 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?
2. 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?
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?
Thesis Committee: Vincent Conitzer Nina Balcan Tuomas Sandholm Nika Haghtalab (University of California, Berkeley) Vahab Mirrokni (Google Research) Renato Paes Leme (Google Research) In Person and Zoom Participation. See announcement.