Updates the README file by adding a link to the journal version of our paper and adds missing information for the citation.

PiperOrigin-RevId: 338272837

causal_reasoning/README.md

@@ -0,0 +1,21 @@
+# Algorithms for Causal Reasoning in Probability Trees
+*By the AGI Safety Analysis Team @ DeepMind*
+
+Probability trees are one of the simplest models of causal generative processes.
+They possess clean semantics and are strictly more general than causal Bayesian
+networks, as they are able to e.g. represent causal relations that causal Bayesian
+networks cannot. Yet, they have received little attention from the AI and ML
+community. Here we present new algorithms for causal reasoning in discrete
+probability trees that cover the entire causal hierarchy (association, intervention,
+and counterfactuals), and operate on arbitrary propositional and causal events. Our
+work expands the domain of causal reasoning to a very general class of discrete
+stochastic processes.
+
+For details, see our paper [Algorithms for Causal Reasoning in Probability Trees]().
+
+To launch the accompanying notebook in Google colab, [click here](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/causal_reasoning/Causal_Reasoning_in_Probability_Trees.ipynb).
+
+If you use the code here please cite this paper.
+
+> Tim Genewein*, Tom McGrath*, Grégoire Delétang*, Vladimir Mikulik*, Miljan Martic, Shane Legg, Pedro A. Ortega. NeurIPS 2020. [\[arXiv\]]()
+

learned_free_energy_estimation/README.md

@@ -1,7 +1,8 @@
 # Targeted free energy estimation via learned mappings
 
 This repository contains supporting data for our publication
-([arXiv](https://arxiv.org/abs/2002.04913)). Here, we provide
+([journal](https://doi.org/10.1063/5.0018903), [arXiv](https://arxiv.org/abs/2002.04913)).
+Here, we provide
 - molecular dynamics (MD) datasets underlying the results reported in our paper,
 - a LAMMPS input script to generate these datasets, and
 - the data plotted in Fig. 5 of our paper to facilitate comparison.
@@ -96,9 +97,11 @@ If you find this repository helpful for your research, please cite our publicati
   title={Targeted free energy estimation via learned mappings},
   author={Wirnsberger, Peter and Ballard, Andrew J and Papamakarios, George and
           Abercrombie, Stuart and Racanière, Sébastien and Pritzel, Alexander and
-          Jimenez Rezende, Danilo and Blundell, Charles}
-  journal={Journal of Chemical Physics},
-  vol={153},
+          Jimenez Rezende, Danilo and Blundell, Charles},
+  journal={J. Chem. Phys.},
+  volume={153},
+  number={14},
+  pages={144112},
   year={2020},
   doi={10.1063/5.0018903}
 }