3 papers
cs.LG2025
ADDQ: Adaptive Distributional Double Q-Learning
Leif Döring, Benedikt Wille, Maximilian Birr +2
Bias problems in the estimation of -values are a well-known obstacle that slows down convergence of -learning and actor-critic methods. One of the reasons of the success of m…
stat.ML2024
Gradient Span Algorithms Make Predictable Progress in High Dimension
Felix Benning, Leif Döring
We prove that all 'gradient span algorithms' have asymptotically deterministic behavior on scaled Gaussian random functions as the dimension tends to infinity. This is a functional…
cs.LG2024
Almost sure convergence rates of stochastic gradient methods under gradient domination
Simon Weissmann, Sara Klein, Waïss Azizian +1
Stochastic gradient methods are among the most important algorithms in training machine learning problems. While classical assumptions such as strong convexity allow a simple analy…