Schedule is tentative and a work-in-progress. It may also be adjusted as the course proceeds.

#	Date	Topics and Readings	Extra Readings	Presenter
1	9/5	Introduction	Slides	Yiling
Background and Early Models
2	9/7	Incentive-Compatible Machine Learning Incentive compatible regression learning, 2010.	Perote and Perote-Peña 2004, Chen et al. 2018 Slides	Yiling
3	9/12	Strategic Classification Strategic Classification	Slides	Yiling
4	9/14	Strategic Online Learning Strategic Classification from Revealed Preferences	Slides Chen, Liu & Podimata 2020, Ahmadi, Beyhaghi, Blum & Naggita, 2021.	Yiling
More Realistic Considerations for Incentive-Aware Machine Learning
5	9/19	Incentivize effort How Do Classifiers Induce Agents to Invest Effort Strategically?	Slides	Yiling
6	9/21	Manipulation or Improvement? On Classification of Strategic Agents Who Can Both Game and Improve	Slides	Matt Tengtrakool and Andrew Lu
7	9/26	Benefit from Manipulation Gaming Helps! Learning from Strategic Interactions in Natural Dynamics	Slides	Jessica Chen and Gary Wu
8	9/28	Field Test Training Machine Learning to Anticipate Manipulation	Slides	Evan Jiang and Cas Li
9	10/3	Unknown decision rule Information Discrepancy in Strategic Learning Strategic classification in the dark		Guest Speaker: Chara Podimata
Collaborative Learning
10	10/5	Game-theoretic modeling of federated learning One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning	Blum et. al. 2017 Slides	Orhan Eren Akgun and Daniel Graces
11	10/10	Incentivize data sharing Mechanisms that Incentivize Data Sharing in Federated Learning	Slides	Jason Tang and William Shi
12	10/12	Sample complexity On-Demand Sampling: Learning Optimally from Multiple Distributions	Slides	Shi Feng and Hanlin Zhang
Economic Perspective of Statistical Inference
13	10/17	Introduction to principal agent problem and contract design Contract Theory Robustness and Linear Contracts	Dutting, Roughgarden and Talgam-Cohen 2019, Dutting, Roughgarden and Talgam-Cohen 2021, Walton and Carroll 2022	Yiling
14	10/19	Contracting statistical testing Principal-Agent Hypothesis Testing Incentive-Theoretic Bayesian Inference for Collaborative Science		Chen Jin and Sadie Zhao
15	10/24	Testing by betting Testing by betting: A strategy for statistical and scientific communication Also read some discussion contributions of the above paper	Slides	Arpit Bhate and Alan Tu
AI for Mechanism Design and Economics
16	10/26	Deep learning and mechanism design Optimal Auctions through Deep Learning Deep Learning for Two-Sided Matching		Guest Speaker: Sai Srivasta
17	10/31	AI for Policy The AI Economist: Taxation policy design via two-level deep multiagent reinforcement learning	Slides	Jeffrey Ma and Eddie Zhang
18	11/2	Econometrics and Machine Learning The Impact of Machine Learning on Economics Stable Learning Establishes Some Common Ground between Causal Inference and Machine Learning		Lukas Fesser and Carina Peng
Calibration and Games
19	11/7	Calibration and Contract Design Calibrated Incentive Contracts	Haghtalab, Podimata, and Yang 2023 Slides	Jingpeng Hong and Raffi Sanna
20	11/9	Game Dynamics for Multi-Calibration A Unifying Perspective on Multi-Calibration: Unleashing Game Dynamics for Multi-Objective Learning		Sophia Cho and Andrew Zhang
Other
21	11/14	Performative prediction Performative prediction Alternative Microfoundations for Strategic Classification		Cassandra Marcussen and Shirley Zhang
22	11/16	Distribution shift due to participation dynamics Emergent Segmentation from Participation Dynamics and Multi-learner Retaining		Yang Hu and Sara Fish
23	11/21	Project office hours
	11/23	Thanksgiving (No Class)
24	11/28	Human-AI systems How Does Predictive Information Affect Human Ethical Preferences? Strategic Adversarial Attacks in AI-assisted Decision Making to Reduce Human Trust and Reliance		Daniela Shuman and Jeff Jiang
25	11/30	Project presentations
26	12/5	Project presentations