9 papers
Uncertainty in Physics and AI: Taxonomy, Quantification, and Validation
Manuel HauÃmann, Ramon Winterhalder, Maria Ubiali
Reliable uncertainty quantification is essential for the use of machine learning in physics, where scientific discoveries depend on validated probabilistic statements. We provide a…
A Measure-Theoretic Finite-Sample Theory for Adaptive-Data Fitted Q-Iteration
Manuel Haussmann, Mustafa Mert Ãelikok, Melih Kandemir
While reinforcement learning (RL) promises to revolutionize the control of complex nonlinear robotic systems, a profound gap persists between the heuristic success of model-free of…
Adaptive Ensemble Aggregation for Actor-Critics
Nicklas Werge, Yi-Shan Wu, Manuel Haussmann +2
Ensembles are ubiquitous in off-policy actor-critic learning, yet their efficacy depends critically on how they are aggregated. Current methods typically rely on static rules or ta…
Distributional Active Inference
Abdullah Akgül, Abdullah Akgül, Gulcin Baykal +5
Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted…
Deep Actor-Critics with Tight Risk Certificates
Bahareh Tasdighi, Manuel Haussmann, Yi-Shan Wu +2
Deep actor-critic algorithms have reached a level where they influence everyday life. They are a driving force behind continual improvement of large language models through user fe…
Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimization
Abdullah Akgül, Gulcin Baykal, Manuel HauÃmann +1
Continuous control of non-stationary environments is a major challenge for deep reinforcement learning algorithms. The time-dependency of the state transition dynamics aggravates t…