25 citations · 125 across the 27 of their papers we have counts for
5 papers · 1 filter
Revisiting Design Choices in Proximal Policy Optimization
Chloe Ching-Yun Hsu, Celestine Mendler-Dünner, Moritz Hardt
Proximal Policy Optimization (PPO) is a popular deep policy gradient algorithm. In standard implementations, PPO regularizes policy updates with clipped probability ratios, and par…
Randomized Block-Diagonal Preconditioning for Parallel Learning
Celestine Mendler-Dünner, Aurelien Lucchi
We study preconditioned gradient-based optimization methods where the preconditioning matrix has block-diagonal form. Such a structural constraint comes with the advantage that the…
Differentially Private Stochastic Coordinate Descent
Georgios Damaskinos, Celestine Mendler-Dünner, Rachid Guerraoui +2
In this paper we tackle the challenge of making the stochastic coordinate descent algorithm differentially private. Compared to the classical gradient descent algorithm where updat…
Stochastic Optimization for Performative Prediction
Celestine Mendler-Dünner, Juan C. Perdomo, Tijana Zrnic +1
In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions. We initiate the stud…
Performative Prediction
Juan C. Perdomo, Tijana Zrnic, Celestine Mendler-Dünner +1
When predictions support decisions they may influence the outcome they aim to predict. We call such predictions performative; the prediction influences the target. Performativity i…