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Workshop
Tue Dec 14 07:00 AM -- 02:15 PM (PST)
Learning and Decision-Making with Strategic Feedback (StratML'21)
Yahav Bechavod · Hoda Heidari · Eric Mazumdar · Celestine Mendler-Dünner · Tijana Zrnic





Workshop Home Page

Classical treatments of machine learning rely on the assumption that the data, after deployment, resembles the data the model was trained on. However, as machine learning models are increasingly used to make consequential decisions about people, individuals often react strategically to the deployed model. These strategic behaviors---which effectively invalidate the predictive models---have opened up new avenues of research and added new challenges to the deployment of machine learning algorithms in the real world.

Different aspects of strategic behavior have been studied by several communities both within and outside of machine learning. For example, the growing literature on strategic classification studies algorithms for finding strategy-robust decision rules, as well as the properties of such rules. Behavioral economics aims to understand and model people’s strategic responses. Recent works on learning in games study optimization algorithms for finding meaningful equilibria and solution concepts in competitive environments.

This workshop aims to create a dialogue between these different communities, all studying aspects of decision-making and learning with strategic feedback. The goal is to identify common points of interest and open problems in the different subareas, as well as to encourage cross-disciplinary collaboration.

Opening remarks (Introduction)
Analysis and interventions in large network games: graphon games and graphon contagion (Invited Talk)
Closing the loop in Machine Learning: Learning to optimize with decision dependent data (Invited Talk)
Strategic Classification and the Quest for the Holy Grail (Invited Talk)
Panel 1 (Discussion Panel)
The Platform Design Problem (Contributed talk)
Learning Losses for Strategic Classification (Contributed talk)
Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents (Contributed talk)
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps (Contributed talk)
Poster Session (Poster Session, social & break)
Revisiting Dynamics in Strategic ML (Invited Talk)
Improving Information from Manipulable Data (Invited Talk)
Microfoundations of Algorithmic decisions (Invited Talk)
Panel 2 (Discussion Panel)
Algorithmic Monoculture and Social Welfare (Invited Talk)
Leveraging strategic interactions for causal discovery (Invited Talk)
Online intermediation in legacy industries: The effect of reservation platforms on restaurants’ prices and survival (Invited Talk)
Panel 3 (Discussion Panel)
The Strategic Perceptron (Poster)
Models of fairness in federated learning (Poster)
Estimation of Standard Asymmetric Auction Models (Poster)
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning (Poster)
Improving Fairness in Credit Lending Models using Subgroup Threshold Optimization (Poster)
Strategic Classification in the Dark (Poster)
Game Redesign in No-regret Game Playing (Poster)
Scoring Rules for Performative Binary Prediction (Poster)
Strategic Classification Made Practical (Poster)
Pessimistic Offline Reinforcement Learning with Multiple Agents (Poster)
Efficient Competitions and Online Learning with Strategic Forecasters (Poster)
The Platform Design Problem (Poster)
The Price of Incentivizing Exploration: A Characterization via Thompson Sampling and Sample Complexity (Poster)
Bayesian Persuasion for Algorithmic Recourse (Poster)
Alternative Microfoundations for Strategic Classification (Poster)
Information Discrepancy in Strategic Learning (Poster)
Price Discovery and Efficiency in Waiting Lists: A Connection to Stochastic Gradient Descent (Poster)
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments (Poster)
Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations (Poster)
Learning Losses for Strategic Classification (Poster)
Gaming Helps! Learning from Strategic Interactions in Natural Dynamics (Poster)
When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits (Poster)
Bounded Rationality for Multi-Agent Motion Planning and Behavior Learning (Poster)
Strategic clustering (Poster)
Test-optional Policies: Overcoming Strategic Behavior and Informational Gaps (Poster)
Global Convergence of Multi-Agent Policy Gradient in Markov Potential Games (Poster)
Exploration and Incentives in Reinforcement Learning (Poster)
Interactive Robust Policy Optimization for Multi-Agent Reinforcement Learning (Poster)
Nash Convergence of Mean-Based Learning Algorithms in First Price Auctions (Poster)
Timing is Money: The Impact of Arrival Order in Beta-Bernoulli Prediction Markets (Poster)
Promoting Resilience of Multi-Agent Reinforcement Learning via Confusion-Based Communication (Poster)
On classification of strategic agents who can both game and improve (Poster)
Learning in Matrix Games can be Arbitrarily Complex (Poster)
Coopetition Against an Amazon (Poster)
Pseudo-Competitive Games and Algorithmic Pricing (Poster)
Near-Optimal No-Regret Learning in General Games (Poster)
Unfairness Despite Awareness: Group-Fair Classification with Strategic Agents (Poster)
Normative disagreement as a challenge for Cooperative AI (Poster)
Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality (Poster)
Learning through Recourse under Censoring (Poster)
Reward-Free Attacks in Multi-Agent Reinforcement Learning (Poster)
Approximating Bayes Nash Equilibria in Auction Games via Gradient Dynamics (Poster)
Regret, stability, and fairness in matching markets with bandit learners (Poster)
Negotiating networks in oligopoly markets for price sensitive products (Poster)