Adding papers missed by keyword search on inspire (#165)

HEPML.bib

@@ -184,6 +184,17 @@ @article{Buhmann:2023bwk
     year = "2023"
 }
 
+% May 8, 2023
+@article{Sengupta:2023xqy,
+    author = "Sengupta, Debajyoti and Klein, Samuel and Raine, John Andrew and Golling, Tobias",
+    title = "{CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation}",
+    eprint = "2305.04646",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ph",
+    month = "5",
+    year = "2023"
+}
+
 %May 7, 2023
 @article{Aguilar-Saavedra:2023pde,
     author = "Aguilar-Saavedra, J. A. and Arganda, E. and Joaquim, F. R. and Sand\'a Seoane, R. M. and Seabra, J. F.",
@@ -273,6 +284,18 @@ @article{Basak:2023wzq
     year = "2023"
 }
 
+%Apr. 28, 2023
+@article{Algren:2023qnb,
+    author = "Algren, Malte and Golling, Tobias and Guth, Manuel and Pollard, Chris and Raine, John Andrew",
+    title = "{Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting}",
+    eprint = "2304.14963",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ph",
+    month = "4",
+    year = "2023"
+}
+
+
 %Apr. 27, 2023
 @article{Nishimura:2023wdu,
     author = "Nishimura, Satsuki and Miyao, Coh and Otsuka, Hajime",
@@ -673,6 +696,16 @@ @inproceedings{Biro:2023kyx
     year = "2023"
 }
 
+@article{Leigh:2023toe,
+    author = "Leigh, Matthew and Sengupta, Debajyoti and Qu\'etant, Guillaume and Raine, John Andrew and Zoch, Knut and Golling, Tobias",
+    title = "{PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics}",
+    eprint = "2303.05376",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ph",
+    month = "3",
+    year = "2023"
+}
+
 %Mar. 8, 2023
 @article{Hirvonen:2023lqy,
     author = "Hirvonen, H. and Eskola, K. J. and Niemi, H.",

HEPML.tex

@@ -171,7 +171,7 @@
 		\\\textit{An autoencoder consists of two functions: one that maps $x$ into a latent space $z$ (encoder) and a second one that maps the latent space back into the original space (decoder).  The encoder and decoder are simultaneously trained so that their composition is nearly the identity.  When the latent space has a well-defined probability density (as in variational autoencoders), then one can sample from the autoencoder by applying the detector to a randomly chosen element of the latent space.}
 		\item \textbf{Normalizing flows}~\cite{Albergo:2019eim,1800956,Kanwar:2003.06413,Brehmer:2020vwc,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Nachman:2020lpy,Choi:2020bnf,Lu:2020npg,Bieringer:2020tnw,Hollingsworth:2021sii,Winterhalder:2021ave,Krause:2021ilc,Hackett:2021idh,Menary:2021tjg,Hallin:2021wme,NEURIPS2020_a878dbeb,Vandegar:2020yvw,Jawahar:2021vyu,Bister:2021arb,Krause:2021wez,Butter:2021csz,Winterhalder:2021ngy,Butter:2022lkf,Verheyen:2022tov,Leigh:2022lpn,Chen:2022ytr,Albandea:2022fky,Krause:2022jna,Cresswell:2022tof,Dolan:2022ikg,Backes:2022vmn,Heimel:2022wyj,Albandea:2023wgd,Rousselot:2023pcj,Diefenbacher:2023vsw,Nicoli:2023qsl,R:2023dcr,Nachman:2023clf}
 		\\\textit{Normalizing flows~\cite{pmlr-v37-rezende15} learn $p(x)$ explicitly by starting with a simple probability density and then applying a series of bijective transformations with tractable Jacobians.}
-		\item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov}
+		\item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Leigh:2023toe,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov}
 		\\\textit{These approaches learn the gradient of the density instead of the density directly.}
 		\item \textbf{Transformer Models}~\cite{Finke:2023veq,Butter:2023fov}
 		\\\textit{These approaches learn the density or perform generative modeling using transformer-based networks.}
@@ -186,7 +186,7 @@
 		\item \textbf{Other/hybrid}~\cite{Cresswell:2022tof,DiBello:2022rss,Li:2022jon,Butter:2023fov}
 		\\\textit{Architectures that combine different network elements or otherwise do not fit into the other categories.}
 	\end{itemize}
-\item \textbf{Anomaly detection}~\cite{DAgnolo:2018cun,Collins:2018epr,Collins:2019jip,DAgnolo:2019vbw,Farina:2018fyg,Heimel:2018mkt,Roy:2019jae,Cerri:2018anq,Blance:2019ibf,Hajer:2018kqm,DeSimone:2018efk,Mullin:2019mmh,1809.02977,Dillon:2019cqt,Andreassen:2020nkr,Nachman:2020lpy,Aguilar-Saavedra:2017rzt,Romao:2019dvs,Romao:2020ojy,knapp2020adversarially,collaboration2020dijet,1797846,1800445,Amram:2020ykb,Cheng:2020dal,Khosa:2020qrz,Thaprasop:2020mzp,Alexander:2020mbx,aguilarsaavedra2020mass,1815227,pol2020anomaly,Mikuni:2020qds,vanBeekveld:2020txa,Park:2020pak,Faroughy:2020gas,Stein:2020rou,Kasieczka:2021xcg,Chakravarti:2021svb,Batson:2021agz,Blance:2021gcs,Bortolato:2021zic,Collins:2021nxn,Dillon:2021nxw,Finke:2021sdf,Shih:2021kbt,Atkinson:2021nlt,Kahn:2021drv,Aarrestad:2021oeb,Dorigo:2021iyy,Caron:2021wmq,Govorkova:2021hqu,Kasieczka:2021tew,Volkovich:2021txe,Govorkova:2021utb,Hallin:2021wme,Ostdiek:2021bem,Fraser:2021lxm,Jawahar:2021vyu,Herrero-Garcia:2021goa,Aguilar-Saavedra:2021utu,Tombs:2021wae,Lester:2021aks,Mikuni:2021nwn,Chekanov:2021pus,dAgnolo:2021aun,Canelli:2021aps,Ngairangbam:2021yma,Bradshaw:2022qev,Aguilar-Saavedra:2022ejy,Buss:2022lxw,Alvi:2022fkk,Dillon:2022tmm,Birman:2022xzu,Raine:2022hht,Letizia:2022xbe,Fanelli:2022xwl,Finke:2022lsu,Verheyen:2022tov,Dillon:2022mkq,Caron:2022wrw,Park:2022zov,Kamenik:2022qxs,Hallin:2022eoq,Kasieczka:2022naq,Araz:2022zxk,Mastandrea:2022vas,Roche:2023int,Golling:2023juz}
+\item \textbf{Anomaly detection}~\cite{DAgnolo:2018cun,Collins:2018epr,Collins:2019jip,DAgnolo:2019vbw,Farina:2018fyg,Heimel:2018mkt,Roy:2019jae,Cerri:2018anq,Blance:2019ibf,Hajer:2018kqm,DeSimone:2018efk,Mullin:2019mmh,1809.02977,Dillon:2019cqt,Andreassen:2020nkr,Nachman:2020lpy,Aguilar-Saavedra:2017rzt,Romao:2019dvs,Romao:2020ojy,knapp2020adversarially,collaboration2020dijet,1797846,1800445,Amram:2020ykb,Cheng:2020dal,Khosa:2020qrz,Thaprasop:2020mzp,Alexander:2020mbx,aguilarsaavedra2020mass,1815227,pol2020anomaly,Mikuni:2020qds,vanBeekveld:2020txa,Park:2020pak,Faroughy:2020gas,Stein:2020rou,Kasieczka:2021xcg,Chakravarti:2021svb,Batson:2021agz,Blance:2021gcs,Bortolato:2021zic,Collins:2021nxn,Dillon:2021nxw,Finke:2021sdf,Shih:2021kbt,Atkinson:2021nlt,Kahn:2021drv,Aarrestad:2021oeb,Dorigo:2021iyy,Caron:2021wmq,Govorkova:2021hqu,Kasieczka:2021tew,Volkovich:2021txe,Govorkova:2021utb,Hallin:2021wme,Ostdiek:2021bem,Fraser:2021lxm,Jawahar:2021vyu,Herrero-Garcia:2021goa,Aguilar-Saavedra:2021utu,Tombs:2021wae,Lester:2021aks,Mikuni:2021nwn,Chekanov:2021pus,dAgnolo:2021aun,Canelli:2021aps,Ngairangbam:2021yma,Bradshaw:2022qev,Aguilar-Saavedra:2022ejy,Buss:2022lxw,Alvi:2022fkk,Dillon:2022tmm,Birman:2022xzu,Raine:2022hht,Letizia:2022xbe,Fanelli:2022xwl,Finke:2022lsu,Verheyen:2022tov,Dillon:2022mkq,Caron:2022wrw,Park:2022zov,Kamenik:2022qxs,Hallin:2022eoq,Kasieczka:2022naq,Araz:2022zxk,Mastandrea:2022vas,Roche:2023int,Golling:2023juz,Sengupta:2023xqy}
 \\\textit{The goal of anomaly detection is to identify abnormal events.  The abnormal events could be from physics beyond the Standard Model or from faults in a detector.  While nearly all searches for new physics are technically anomaly detection, this category is for methods that are mode-independent (broadly defined).  Anomalies in high energy physics tend to manifest as over-densities in phase space (often called `population anomalies') in contrast to off-manifold anomalies where you can flag individual examples as anomalous. }
 \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.}
@@ -195,7 +195,7 @@
 		\\\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.}
-		\item \textbf{Domain adaptation}~\cite{Rogozhnikov:2016bdp,Andreassen:2019nnm,Cranmer:2015bka,2009.03796,Nachman:2021opi,Camaiani:2022kul,Schreck:2023pzs}
+		\item \textbf{Domain adaptation}~\cite{Rogozhnikov:2016bdp,Andreassen:2019nnm,Cranmer:2015bka,2009.03796,Nachman:2021opi,Camaiani:2022kul,Schreck:2023pzs,Algren:2023qnb}
 		\\\textit{Morphing simulations to look like data is a form of domain adaptation.}
 		\item \textbf{BSM}~\cite{Andreassen:2020nkr,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2018hga,Brehmer:2019xox,Romao:2020ojy,deSouza:2022uhk,GomezAmbrosio:2022mpm,Castro:2022zpq,Anisha:2023xmh,Dennis:2023kfe}
 		\\\textit{This category is for parameter estimation when the parameter is the signal strength of new physics.}

README.md

@@ -1069,6 +1069,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 ###  Diffusion Models
 
 * [Score-based Generative Models for Calorimeter Shower Simulation](https://arxiv.org/abs/2206.11898)
+* [PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics](https://arxiv.org/abs/2303.05376)
 * [Fast Point Cloud Generation with Diffusion Models in High Energy Physics](https://arxiv.org/abs/2304.01266)
 * [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://arxiv.org/abs/2305.10399)
 * [CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation](https://arxiv.org/abs/2305.04847)
@@ -1216,6 +1217,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [Efficiently Moving Instead of Reweighting Collider Events with Machine Learning](https://arxiv.org/abs/2212.06155)
 * [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836)
 * [The Mass-ive Issue: Anomaly Detection in Jet Physics](https://arxiv.org/abs/2303.14134)
+* [CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation](https://arxiv.org/abs/2305.04646)
 
 ##  Simulation-based (`likelihood-free') Inference
 ###  Parameter estimation
@@ -1276,6 +1278,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [Neural Conditional Reweighting](https://arxiv.org/abs/2107.08979)
 * [Model independent measurements of Standard Model cross sections with Domain Adaptation](https://arxiv.org/abs/2207.09293)
 * [Mimicking non-ideal instrument behavior for hologram processing using neural style translation](https://arxiv.org/abs/2301.02757) [[DOI](https://doi.org/10.1364/OE.486741)]
+* [Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting](https://arxiv.org/abs/2304.14963)
 
 ###  BSM
 

docs/index.md

@@ -1211,6 +1211,7 @@ const expandElements = shouldExpand => {
     </div>
 
     * [Score-based Generative Models for Calorimeter Shower Simulation](https://arxiv.org/abs/2206.11898)
+    * [PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics](https://arxiv.org/abs/2303.05376)
     * [Fast Point Cloud Generation with Diffusion Models in High Energy Physics](https://arxiv.org/abs/2304.01266)
     * [End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics](https://arxiv.org/abs/2305.10399)
     * [CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation](https://arxiv.org/abs/2305.04847)
@@ -1391,6 +1392,7 @@ const expandElements = shouldExpand => {
     * [Efficiently Moving Instead of Reweighting Collider Events with Machine Learning](https://arxiv.org/abs/2212.06155)
     * [Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays](https://arxiv.org/abs/2304.03836)
     * [The Mass-ive Issue: Anomaly Detection in Jet Physics](https://arxiv.org/abs/2303.14134)
+    * [CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation](https://arxiv.org/abs/2305.04646)
 
 ##  Simulation-based (`likelihood-free') Inference
 
@@ -1466,6 +1468,7 @@ const expandElements = shouldExpand => {
     * [Neural Conditional Reweighting](https://arxiv.org/abs/2107.08979)
     * [Model independent measurements of Standard Model cross sections with Domain Adaptation](https://arxiv.org/abs/2207.09293)
     * [Mimicking non-ideal instrument behavior for hologram processing using neural style translation](https://arxiv.org/abs/2301.02757) [[DOI](https://doi.org/10.1364/OE.486741)]
+    * [Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting](https://arxiv.org/abs/2304.14963)
 
 
 ??? example "BSM"

docs/recent.md

@@ -29,6 +29,7 @@ This is an automatically compiled list of papers which have been added to the li
 * [Neural Network predictions of inclusive electron-nucleus cross sections](https://arxiv.org/abs/2305.08217)
 * [Is infrared-collinear safe information all you need for jet classification?](https://arxiv.org/abs/2305.08979)
 * [CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation](https://arxiv.org/abs/2305.04847)
+* [CURTAINs Flows For Flows: Constructing Unobserved Regions with Maximum Likelihood Estimation](https://arxiv.org/abs/2305.04646)
 * [Gradient Boosting MUST taggers for highly-boosted jets](https://arxiv.org/abs/2305.04957)
 * [Searching for dark jets with displaced vertices using weakly supervised machine learning](https://arxiv.org/abs/2305.04372)
 * [Tip of the Red Giant Branch Bounds on the Axion-Electron Coupling Revisited](https://arxiv.org/abs/2305.03113)
@@ -38,6 +39,7 @@ This is an automatically compiled list of papers which have been added to the li
 
 ## April 2023
 * [Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning](https://arxiv.org/abs/2305.00493)
+* [Flow Away your Differences: Conditional Normalizing Flows as an Improvement to Reweighting](https://arxiv.org/abs/2304.14963)
 * [Exploring the flavor structure of quarks and leptons with reinforcement learning](https://arxiv.org/abs/2304.14176)
 * [Machine learning method for $^{12}$C event classification and reconstruction in the active target time-projection chamber](https://arxiv.org/abs/2304.13233)
 * [A Modern Global Extraction of the Sivers Function](https://arxiv.org/abs/2304.14328)