Zobrazeno 1 - 10
of 186
pro vyhledávání: '"Schulte, Oliver"'
Autor:
Schulte, Oliver, Poupart, Pascal
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 the goal of reinforcement learning is to select interventions that maxi
Externí odkaz:
http://arxiv.org/abs/2403.04221
Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning. Implicit learning of causal mechanisms typically involves tw
Externí odkaz:
http://arxiv.org/abs/2402.11124
Autor:
Schulte, Oliver
Estimating the expected value of a graph statistic is an important inference task for using and learning graph models. This note presents a scalable estimation procedure for expected motif counts, a widely used type of graph statistic. The procedure
Externí odkaz:
http://arxiv.org/abs/2305.01089
Conventional supervised learning methods typically assume i.i.d samples and are found to be sensitive to out-of-distribution (OOD) data. We propose Generative Causal Representation Learning (GCRL) which leverages causality to facilitate knowledge tra
Externí odkaz:
http://arxiv.org/abs/2302.08635
Autor:
Schulte, Oliver
This note describes a new approach to classifying graphs that leverages graph generative models (GGM). Assuming a GGM that defines a joint probability distribution over graphs and their class labels, I derive classification formulas for the probabili
Externí odkaz:
http://arxiv.org/abs/2302.07989
Autor:
Sun, Xiangyu, Schulte, Oliver
A fundamental problem of causal discovery is cause-effect inference, learning the correct causal direction between two random variables. Significant progress has been made through modelling the effect as a function of its cause and a noise term, whic
Externí odkaz:
http://arxiv.org/abs/2301.12930
Generative models for graph data are an important research topic in machine learning. Graph data comprise two levels that are typically analyzed separately: node-level properties such as the existence of a link between a pair of nodes, and global agg
Externí odkaz:
http://arxiv.org/abs/2210.16844
Autor:
Mar, Richard, Schulte, Oliver
Statistical-Relational Model Discovery aims to find statistically relevant patterns in relational data. For example, a relational dependency pattern may stipulate that a user's gender is associated with the gender of their friends. As with propositio
Externí odkaz:
http://arxiv.org/abs/2110.09767
We describe NTS-NOTEARS, a score-based structure learning method for time-series data to learn dynamic Bayesian networks (DBNs) that captures nonlinear, lagged (inter-slice) and instantaneous (intra-slice) relations among variables. NTS-NOTEARS utili
Externí odkaz:
http://arxiv.org/abs/2109.04286