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Found 7 papers, 1 papers with code
User Strategization and Trustworthy Algorithms
The developers of these algorithms commonly adopt the assumption that the data generating process is exogenous: that is, how a user reacts to a given prompt (e. g., a recommendation or hiring suggestion) depends on the prompt and not on the algorithm that generated it.
A User-Driven Framework for Regulating and Auditing Social Media
In particular, we introduce the notion of a baseline feed: the content that a user would see without filtering (e. g., on Twitter, this could be the chronological timeline).
Matrix Estimation for Individual Fairness
Specifically, we show that using a popular ME method known as singular value thresholding (SVT) to pre-process the data provides a strong IF guarantee under appropriate conditions.
Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference
We propose an estimator, Network Synthetic Interventions (NSI), and show that it consistently estimates the mean outcomes for a unit under an arbitrary set of counterfactual treatments for the network.
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold'em poker, which has a significantly smaller game tree (on the order of $10^{164}$ nodes).
Regret, stability & fairness in matching markets with bandit learners
In this work, we study how competition affects the long-term outcomes of individuals as they learn.
Radar-only ego-motion estimation in difficult settings via graph matching
Radar detects stable, long-range objects under variable weather and lighting conditions, making it a reliable and versatile sensor well suited for ego-motion estimation.
