4 papers
The Stability of Online Algorithms in Performative Prediction
Gabriele Farina, Juan Carlos Perdomo
The use of algorithmic predictions in decision-making leads to a feedback loop where the models we deploy actively influence the data distributions we see, and later use to retrain…
An Efficient Black-Box Reduction from Online Learning to Multicalibration, and a New Route to -Regret Minimization
Gabriele Farina, Juan Carlos Perdomo
We give a Gordon-Greenwald-Marks (GGM) style black-box reduction from online learning to online multicalibration. Concretely, we show that to achieve high-dimensional multicalibrat…
Cautious Optimism: A Meta-Algorithm for Near-Constant Regret in General Games
Ashkan Soleymani, Georgios Piliouras, Gabriele Farina
We introduce Cautious Optimism, a framework for substantially faster regularized learning in general games. Cautious Optimism, as a variant of Optimism, adaptively controls the lea…
Faster Rates for No-Regret Learning in General Games via Cautious Optimism
Ashkan Soleymani, Georgios Piliouras, Gabriele Farina
We establish the first uncoupled learning algorithm that attains per-player regret in multi-player general-sum games, where is the number of players, …