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?


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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.