A survey on causal reinforcement learning
While reinforcement learning (RL) achieves tremendous success in sequential decision-
making problems of many domains, it still faces key challenges of data inefficiency and the …
making problems of many domains, it still faces key challenges of data inefficiency and the …
Toward causal representation learning
The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
Causality for machine learning
B Schölkopf - Probabilistic and causal inference: The works of Judea …, 2022 - dl.acm.org
The machine learning community's interest in causality has significantly increased in recent
years. My understanding of causality has been shaped by Judea Pearl and a number of …
years. My understanding of causality has been shaped by Judea Pearl and a number of …
Causal reinforcement learning: A survey
Reinforcement learning is an essential paradigm for solving sequential decision problems
under uncertainty. Despite many remarkable achievements in recent decades, applying …
under uncertainty. Despite many remarkable achievements in recent decades, applying …
Independent mechanism analysis, a new concept?
Independent component analysis provides a principled framework for unsupervised
representation learning, with solid theory on the identifiability of the latent code that …
representation learning, with solid theory on the identifiability of the latent code that …
Causal influence detection for improving efficiency in reinforcement learning
Many reinforcement learning (RL) environments consist of independent entities that interact
sparsely. In such environments, RL agents have only limited influence over other entities in …
sparsely. In such environments, RL agents have only limited influence over other entities in …
[HTML][HTML] A reusable benchmark of brain-age prediction from M/EEG resting-state signals
Population-level modeling can define quantitative measures of individual aging by applying
machine learning to large volumes of brain images. These measures of brain age, obtained …
machine learning to large volumes of brain images. These measures of brain age, obtained …
Counterfactual data augmentation using locally factored dynamics
Many dynamic processes, including common scenarios in robotic control and reinforcement
learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not …
learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not …
Off-policy evaluation in partially observable environments
This work studies the problem of batch off-policy evaluation for Reinforcement Learning in
partially observable environments. Off-policy evaluation under partial observability is …
partially observable environments. Off-policy evaluation under partial observability is …
Nonlinear invariant risk minimization: A causal approach
Due to spurious correlations, machine learning systems often fail to generalize to
environments whose distributions differ from the ones used at training time. Prior work …
environments whose distributions differ from the ones used at training time. Prior work …