1 citations · 1 across the 5 of their papers we have counts for
6 papers
Stabilizing Performative Feedback Loops with Minimal Model Deployments
Gabriele Farina, Juan Carlos Perdomo
When algorithmic predictions inform people's decisions, the models we deploy are performative and actively shape the data we see. This feedback loop between algorithms and their br…
Exact-Form Regret for Gradient Descent, Mirror Descent and Follow-the-Regularized-Leader
Ashkan Soleymani, Gabriele Farina, Patrick Jaillet
Online gradient descent is usually studied through external regret, where the learner competes with fixed alternatives. Recent work shows that first-order methods control richer ac…
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…
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…
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, …