1 citations · 2 across the 11 of their papers we have counts for
14 papers
Introduction to stochastic gradient methods
Simon Weissmann
These lecture notes provide an introduction to first-order optimization methods with a particular emphasis on stochastic gradient methods. We begin with deterministic gradient base…
Error Bounds for Importance Sampling with Estimated Proposal Distributions
Cathrine Aeckerle-Willems, Ilja Klebanov, Simon Weissmann
Importance sampling with data-driven proposal distributions is widely used in practice. A common workflow first generates an auxiliary sample of size from an approximation of t…
The Role of Target Update Frequencies in Q-Learning
Simon Weissmann, Tilman Aach, Benedikt Wille +2
The target network update frequency (TUF) is a central stabilization mechanism in (deep) Q-learning. However, their selection remains poorly understood and is often treated merely…
An Approximate Ascent Approach To Prove Convergence of PPO
Leif Doering, Daniel Schmidt, Moritz Melcher +4
Proximal Policy Optimization (PPO) is among the most widely used deep reinforcement learning algorithms, yet its theoretical foundations remain incomplete. Most importantly, conver…
Adaptive Kernel Selection for Stein Variational Gradient Descent
Moritz Melcher, Simon Weissmann, Ashia C. Wilson +1
A central challenge in Bayesian inference is efficiently approximating posterior distributions. Stein Variational Gradient Descent (SVGD) is a popular variational inference method…
Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
Sebastian Kassing, Simon Weissmann, Leif Döring
The present article studies the minimization of convex, L-smooth functions defined on a separable real Hilbert space. We analyze regularized stochastic gradient descent (reg-SGD),…
Clustered KL-barycenter design for policy evaluation
Simon Weissmann, Till Freihaut, Claire Vernade +2
In the context of stochastic bandit models, this article examines how to design sample-efficient behavior policies for the importance sampling evaluation of multiple target policie…
Structure Matters: Dynamic Policy Gradient
Sara Klein, Xiangyuan Zhang, Tamer Başar +2
In this work, we study -discounted infinite-horizon tabular Markov decision processes (MDPs) and introduce a framework called dynamic policy gradient (DynPG). The framework dire…
Polyak's Heavy Ball Method Achieves Accelerated Local Rate of Convergence under Polyak-Lojasiewicz Inequality
Sebastian Kassing, Simon Weissmann
In this work, we analyze the convergence of Polyak's heavy ball method in both continuous and discrete time for non-convex -objective functions satisfying the Polyak-Lojasiewi…
On the mean field limit of consensus based methods
Marvin Koß, Simon Weissmann, Jakob Zech
Consensus based optimization (CBO) employs a swarm of particles evolving as a system of stochastic differential equations (SDEs). Recently, it has been adapted to yield a derivativ…
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…
On the mean-field limit for Stein variational gradient descent: stability and multilevel approximation
Simon Weissmann, Jakob Zech
In this paper we propose and analyze a novel multilevel version of Stein variational gradient descent (SVGD). SVGD is a recent particle based variational inference method. For Baye…
Metropolis-adjusted interacting particle sampling
Björn Sprungk, Simon Weissmann, Jakob Zech
In recent years, various interacting particle samplers have been developed to sample from complex target distributions, such as those found in Bayesian inverse problems. These samp…
Beyond Stationarity: Convergence Analysis of Stochastic Softmax Policy Gradient Methods
Sara Klein, Simon Weissmann, Leif Döring
Markov Decision Processes (MDPs) are a formal framework for modeling and solving sequential decision-making problems. In finite-time horizons such problems are relevant for instanc…