1 citations · 1 across the 7 of their papers we have counts for
8 papers
Distributional Adversarial Attacks and Training in Deep Hedging
Guangyi He, Tobias Sutter, Lukas Gonon
In this paper, we study the robustness of classical deep hedging strategies under distributional shifts by leveraging the concept of adversarial attacks. We first demonstrate that…
Asymptotic Optimality in Data-Driven Decision Making
Radek Salač, Michael Kupper, Tobias Sutter
Given data generated by an observable stochastic process, we study how to construct statistically optimal decisions for general stochastic optimization problems. Our setting encomp…
Reliability-Adjusted Prioritized Experience Replay
Leonard S. Pleiss, Tobias Sutter, Maximilian Schiffer
Experience replay enables data-efficient learning from past experiences in online reinforcement learning agents. Traditionally, experiences were sampled uniformly from a replay buf…
A Two-Timescale Primal-Dual Framework for Reinforcement Learning via Online Dual Variable Guidance
Axel Friedrich Wolter, Tobias Sutter
We study reinforcement learning by combining recent advances in regularized linear programming formulations with the classical theory of stochastic approximation. Motivated by the…
Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms
Felix Petersen, Christian Borgelt, Tobias Sutter +3
When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular appr…
Finding the DeepDream for Time Series: Activation Maximization for Univariate Time Series
Udo Schlegel, Daniel A. Keim, Tobias Sutter
Understanding how models process and interpret time series data remains a significant challenge in deep learning to enable applicability in safety-critical areas such as healthcare…
Regularized Q-learning through Robust Averaging
Peter Schmitt-Förster, Tobias Sutter
We propose a new Q-learning variant, called 2RA Q-learning, that addresses some weaknesses of existing Q-learning methods in a principled manner. One such weakness is an underlying…
Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces
Angeliki Kamoutsi, Peter Schmitt-Förster, Tobias Sutter +2
This work studies discrete-time discounted Markov decision processes with continuous state and action spaces and addresses the inverse problem of inferring a cost function from obs…