move hierarchichal SBI over ensembles to SBI section

HEPML.tex

@@ -177,7 +177,7 @@
 		\\\textit{These approaches learn the density or perform generative modeling using transformer-based networks.}
 		\item \textbf{Physics-inspired}~\cite{Andreassen:2018apy,Andreassen:2019txo,1808876,Lai:2020byl,Barenboim:2021vzh}
 		\\\textit{A variety of methods have been proposed to use machine learning tools (e.g. neural networks) combined with physical components.}
-		\item \textbf{Mixture Models}~\cite{Chen:2020uds,Burton:2021tsd,Graziani:2021vai,Liu:2022dem,Heinrich:2023bmt}
+		\item \textbf{Mixture Models}~\cite{Chen:2020uds,Burton:2021tsd,Graziani:2021vai,Liu:2022dem}
 		\\\textit{A mixture model is a superposition of simple probability densities.  For example, a Gaussian mixture model is a sum of normal probability densities.  Mixture density networks are mixture models where the coefficients in front of the constituent densities as well as the density parameters (e.g. mean and variances of Gaussians) are parameterized by neural networks.}
 		\item \textbf{Phase space generation}~\cite{Bendavid:2017zhk,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Klimek:2018mza,Carrazza:2020rdn,Nachman:2020fff,Chen:2020nfb,Verheyen:2020bjw,Backes:2020vka,Danziger:2021eeg,Yoon:2020zmb,Maitre:2022xle,Jinno:2022sbr,Heimel:2022wyj,Renteria-Estrada:2023buo,Singh:2023yvj}
 		\\\textit{Monte Carlo event generators integrate over a phase space that needs to be generated efficiently and this can be aided by machine learning methods.}
@@ -191,7 +191,7 @@
 \item \textbf{Simulation-based (`likelihood-free') Inference}
 \\\textit{Likelihood-based inference is the case where $p(x|\theta)$ is known and $\theta$ can be determined by maximizing the probability of the data.  In high energy physics, $p(x|\theta)$ is often not known analytically, but it is often possible to sample from the density implicitly using simulations.}
 	\begin{itemize}
-		\item \textbf{Parameter estimation}~\cite{Andreassen:2019nnm,Stoye:2018ovl,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2019xox,Brehmer:2018hga,Cranmer:2015bka,Andreassen:2020gtw,Coogan:2020yux,Flesher:2020kuy,Bieringer:2020tnw,Nachman:2021yvi,Chatterjee:2021nms,NEURIPS2020_a878dbeb,Mishra-Sharma:2021oxe,Barman:2021yfh,Bahl:2021dnc,Arganda:2022qzy,Kong:2022rnd,Arganda:2022zbs,Rizvi:2023mws}
+		\item \textbf{Parameter estimation}~\cite{Andreassen:2019nnm,Stoye:2018ovl,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2019xox,Brehmer:2018hga,Cranmer:2015bka,Andreassen:2020gtw,Coogan:2020yux,Flesher:2020kuy,Bieringer:2020tnw,Nachman:2021yvi,Chatterjee:2021nms,NEURIPS2020_a878dbeb,Mishra-Sharma:2021oxe,Barman:2021yfh,Bahl:2021dnc,Arganda:2022qzy,Kong:2022rnd,Arganda:2022zbs,Rizvi:2023mws,Heinrich:2023bmt}
 		\\\textit{This can also be viewed as a regression problem, but there the goal is typically to do maximum likelihood estimation in contrast to directly minimizing the mean squared error between a function and the target.}
 		\item \textbf{Unfolding}~\cite{Andreassen:2019cjw,Datta:2018mwd,Bellagente:2019uyp,Gagunashvili:2010zw,Glazov:2017vni,Martschei:2012pr,Lindemann:1995ut,Zech2003BinningFreeUB,1800956,Vandegar:2020yvw,Howard:2021pos,Baron:2021vvl,Andreassen:2021zzk,Komiske:2021vym,H1:2021wkz,Arratia:2021otl,Wong:2021zvv,Arratia:2022wny,Backes:2022vmn,Chan:2023tbf,Shmakov:2023kjj}
 		\\\textit{This is the task of removing detector distortions.  In contrast to parameter estimation, the goal is not to infer model parameters, but instead, the undistorted phase space probability density.  This is often also called deconvolution.}

README.md

@@ -1128,7 +1128,6 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples](https://arxiv.org/abs/2103.13416)
 * [A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme](https://arxiv.org/abs/2110.10259)
 * [Geometry-aware Autoregressive Models for Calorimeter Shower Simulations](https://arxiv.org/abs/2212.08233)
-* [Hierarchical Neural Simulation-Based Inference Over Event Ensembles](https://arxiv.org/abs/2306.12584)
 
 ###  Phase space generation
 
@@ -1283,6 +1282,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation](https://arxiv.org/abs/2210.01680)
 * [Machine-Learned Exclusion Limits without Binning](https://arxiv.org/abs/2211.04806)
 * [Learning Likelihood Ratios with Neural Network Classifiers](https://arxiv.org/abs/2305.10500)
+* [Hierarchical Neural Simulation-Based Inference Over Event Ensembles](https://arxiv.org/abs/2306.12584)
 
 ###  Unfolding
 

docs/index.md

@@ -1285,7 +1285,6 @@ const expandElements = shouldExpand => {
     * [Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples](https://arxiv.org/abs/2103.13416)
     * [A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme](https://arxiv.org/abs/2110.10259)
     * [Geometry-aware Autoregressive Models for Calorimeter Shower Simulations](https://arxiv.org/abs/2212.08233)
-    * [Hierarchical Neural Simulation-Based Inference Over Event Ensembles](https://arxiv.org/abs/2306.12584)
 
 
 ??? example "Phase space generation"
@@ -1463,6 +1462,7 @@ const expandElements = shouldExpand => {
     * [New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation](https://arxiv.org/abs/2210.01680)
     * [Machine-Learned Exclusion Limits without Binning](https://arxiv.org/abs/2211.04806)
     * [Learning Likelihood Ratios with Neural Network Classifiers](https://arxiv.org/abs/2305.10500)
+    * [Hierarchical Neural Simulation-Based Inference Over Event Ensembles](https://arxiv.org/abs/2306.12584)
 
 
 ??? example "Unfolding"