2 citations · 3 across the 7 of their papers we have counts for
7 papers
DAWM: Diffusion Action World Models for Offline Reinforcement Learning via Action-Inferred Transitions
Zongyue Li, Xiao Han, Yusong Li +2
Diffusion-based world models have demonstrated strong capabilities in synthesizing realistic long-horizon trajectories for offline reinforcement learning (RL). However, many existi…
Dying Clusters Is All You Need -- Deep Clustering With an Unknown Number of Clusters
Collin Leiber, Niklas Strauß, Matthias Schubert +1
Finding meaningful groups, i.e., clusters, in high-dimensional data such as images or texts without labeled data at hand is an important challenge in data mining. In recent years,…
Autoregressive Policy Optimization for Constrained Allocation Tasks
David Winkel, Niklas Strauß, Maximilian Bernhard +3
Allocation tasks represent a class of problems where a limited amount of resources must be allocated to a set of entities at each time step. Prominent examples of this task include…
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning
David Winkel, Niklas Strauß, Matthias Schubert +1
Portfolio optimization tasks describe sequential decision problems in which the investor's wealth is distributed across a set of assets. Allocation constraints are used to enforce…
Efficient Parking Search using Shared Fleet Data
Niklas Strauß, Lukas Rottkamp, Sebatian Schmoll +1
Finding an available on-street parking spot is a relevant problem of day-to-day life. In recent years, cities such as Melbourne and San Francisco deployed sensors that provide real…
Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem
Niklas Strauß, Matthias Schubert
The traveling officer problem (TOP) is a challenging stochastic optimization task. In this problem, a parking officer is guided through a city equipped with parking sensors to fine…
MapFormer: Boosting Change Detection by Using Pre-change Information
Maximilian Bernhard, Niklas Strauß, Matthias Schubert
Change detection in remote sensing imagery is essential for a variety of applications such as urban planning, disaster management, and climate research. However, existing methods f…