30 citations · 30 across the 6 of their papers we have counts for
6 papers
Counterfactual Residual Data Augmentation for Regression
Hossein Mohebbi, Oliver Schulte, Ke Li +1
Data-driven modeling in real-world regression tasks often suffers from limited training samples, high collection costs, and noisy observations. Inspired by the impact of data augme…
TacticGen: Grounding Adaptable and Scalable Generation of Football Tactics
Sheng Xu, Guiliang Liu, Tarak Kharrat +12
Success in association football relies on both individual skill and coordinated tactics. While recent advancements in spatio-temporal data and deep learning have enabled predictive…
From Graph Diffusion to Graph Classification
Jia Jun Cheng Xian, Sadegh Mahdavi, Renjie Liao +1
Generative models such as diffusion models have achieved remarkable success in state-of-the-art image and text tasks. Recently, score-based diffusion models have extended their suc…
Why Online Reinforcement Learning is Causal
Oliver Schulte, Pascal Poupart
Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while t…
Implicit Causal Representation Learning via Switchable Mechanisms
Shayan Shirahmad Gale Bagi, Zahra Gharaee, Oliver Schulte +1
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation…
The CTU Prague Relational Learning Repository
Jan Motl, Oliver Schulte
The aim of the Prague Relational Learning Repository is to support machine learning research with multi-relational data. The repository currently contains 148 SQL databases hosted…