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REFS — verified citations

Verification: every entry below was fetched on 2026-10-12 (America/New_York). arXiv entries: title, authors and first-version date read from the arXiv abstract page (citation_* meta tags); journal refs/DOIs are those shown on the arXiv page where present. Journal-only entries: title/authors/venue/year read from the Crossref API record for the DOI (https://api.crossref.org/works/) or from the publisher page. Nothing in this list is from memory alone.

Key used in LANDSCAPE.md is shown in brackets.

A. Verified via arXiv abstract page

• [Chernozhukov2007·0704.3649] Chernozhukov, Victor; Fernandez-Val, Ivan; Galichon, Alfred (2007, arXiv first version). Quantile and Probability Curves Without Crossing. https://arxiv.org/abs/0704.3649. Journal ref on arXiv page: Econometrica (2010) 78 (3): 1093-1125. DOI: 10.3982/ECTA7880.
• [Chernozhukov2009·0904.0951] Chernozhukov, Victor; Fernandez-Val, Ivan; Melly, Blaise (2009, arXiv first version). Inference on Counterfactual Distributions. https://arxiv.org/abs/0904.0951.
• [Hothorn2012·1201.5786] Hothorn, Torsten; Kneib, Thomas; Bühlmann, Peter (2012, arXiv first version). Conditional Transformation Models. https://arxiv.org/abs/1201.5786. Journal ref on arXiv page: Journal of the Royal Statistical Society, Series B (Methodology), 2014. DOI: 10.1111/rssb.12017.
• [Carlier2014·1406.4643] Carlier, Guillaume; Chernozhukov, Victor; Galichon, Alfred (2014, arXiv first version). Vector Quantile Regression: An Optimal Transport Approach. https://arxiv.org/abs/1406.4643.
• [Chernozhukov2014·1412.8434] Chernozhukov, Victor; Galichon, Alfred; Hallin, Marc; Henry, Marc (2014, arXiv first version). Monge-Kantorovich Depth, Quantiles, Ranks, and Signs. https://arxiv.org/abs/1412.8434.
• [Esfahani2015·1505.05116] Esfahani, Peyman Mohajerin; Kuhn, Daniel (2015, arXiv first version). Data-driven Distributionally Robust Optimization Using the Wasserstein Metric: Performance Guarantees and Tractable Reformulations. https://arxiv.org/abs/1505.05116.
• [Courty2015·1507.00504] Courty, Nicolas; Flamary, Rémi; Tuia, Devis; Rakotomamonjy, Alain (2015, arXiv first version). Optimal Transport for Domain Adaptation. https://arxiv.org/abs/1507.00504.
• [Hothorn2015·1508.06749] Hothorn, Torsten; Möst, Lisa; Bühlmann, Peter (2015, arXiv first version). Most Likely Transformations. https://arxiv.org/abs/1508.06749. Journal ref on arXiv page: Scandinavian Journal of Statistics (2018). DOI: 10.1111/sjos.12291.
• [Liero2015·1508.07941] Liero, Matthias; Mielke, Alexander; Savaré, Giuseppe (2015, arXiv first version). Optimal Entropy-Transport problems and a new Hellinger-Kantorovich distance between positive measures. https://arxiv.org/abs/1508.07941. Journal ref on arXiv page: WIAS preprint 2207. DOI: 10.1007/s00222-017-0759-8.
• [Gao2016·1604.02199] Gao, Rui; Kleywegt, Anton J. (2016, arXiv first version). Distributionally Robust Stochastic Optimization with Wasserstein Distance. https://arxiv.org/abs/1604.02199.
• [Lei2016·1604.04173] Lei, Jing; G'Sell, Max; Rinaldo, Alessandro; Tibshirani, Ryan J.; et al. (2016, arXiv first version). Distribution-Free Predictive Inference For Regression. https://arxiv.org/abs/1604.04173.
• [Chizat2016·1607.05816] Chizat, Lenaic; Peyré, Gabriel; Schmitzer, Bernhard; Vialard, François-Xavier (2016, arXiv first version). Scaling Algorithms for Unbalanced Transport Problems. https://arxiv.org/abs/1607.05816.
• [Petersen2016·1608.03012] Petersen, Alexander; Müller, Hans-Georg (2016, arXiv first version). Fréchet Regression for Random Objects with Euclidean Predictors. https://arxiv.org/abs/1608.03012.
• [Lundberg2017·1705.07874] Lundberg, Scott; Lee, Su-In (2017, arXiv first version). A Unified Approach to Interpreting Model Predictions. https://arxiv.org/abs/1705.07874.
• [Fasiolo2017·1707.03307] Fasiolo, M.; Wood, S. N.; Zaffran, M.; Nedellec, R.; et al. (2017, arXiv first version). Fast calibrated additive quantile regression. https://arxiv.org/abs/1707.03307. DOI: 10.1080/01621459.2020.1725521.
• [Bhatia2017·1712.01504] Bhatia, Rajendra; Jain, Tanvi; Lim, Yongdo (2017, arXiv first version). On the Bures-Wasserstein distance between positive definite matrices. https://arxiv.org/abs/1712.01504.
• [Gao2017·1712.06050] Gao, Rui; Chen, Xi; Kleywegt, Anton J. (2017, arXiv first version). Wasserstein Distributionally Robust Optimization and Variation Regularization. https://arxiv.org/abs/1712.06050.
• [Peyré2018·1803.00567] Peyré, Gabriel; Cuturi, Marco (2018, arXiv first version). Computational Optimal Transport. https://arxiv.org/abs/1803.00567. Journal ref on arXiv page: Foundations and Trends in Machine Learning, vol. 11, no. 5-6, pp. 355-607, 2019.
• [Li2018·1803.07033] Li, Wuchen; Montufar, Guido (2018, arXiv first version). Natural gradient via optimal transport. https://arxiv.org/abs/1803.07033.
• [Schlosser2018·1804.02921] Schlosser, Lisa; Hothorn, Torsten; Stauffer, Reto; Zeileis, Achim (2018, arXiv first version). Distributional Regression Forests for Probabilistic Precipitation Forecasting in Complex Terrain. https://arxiv.org/abs/1804.02921. Journal ref on arXiv page: The Annals of Applied Statistics, Volume 13, Number 3 (2019), 1564-1589. DOI: 10.1214/19-AOAS1247.
• [del Barrio2018·1806.01238] del Barrio, Eustasio; Cuesta-Albertos, Juan A.; Hallin, Marc; Matrán, Carlos (2018, arXiv first version). Center-Outward Distribution Functions, Quantiles, Ranks, and Signs in $\mathbb{R}^d$. https://arxiv.org/abs/1806.01238.
• [Panaretos2018·1806.05500] Panaretos, Victor M.; Zemel, Yoav (2018, arXiv first version). Statistical Aspects of Wasserstein Distances. https://arxiv.org/abs/1806.05500. Journal ref on arXiv page: Annual Review of Statistics and Its Application 2019 6:1, 405-431. DOI: 10.1146/annurev-statistics-030718-104938.
• [Genevay2018·1810.02733] Genevay, Aude; Chizat, Lénaic; Bach, Francis; Cuturi, Marco; et al. (2018, arXiv first version). Sample Complexity of Sinkhorn divergences. https://arxiv.org/abs/1810.02733.
• [Feydy2018·1810.08278] Feydy, Jean; Séjourné, Thibault; Vialard, François-Xavier; Amari, Shun-ichi; et al. (2018, arXiv first version). Interpolating between Optimal Transport and MMD using Sinkhorn Divergences. https://arxiv.org/abs/1810.08278.
• [Deshpande2019·1904.05877] Deshpande, Ishan; Hu, Yuan-Ting; Sun, Ruoyu; Pyrros, Ayis; et al. (2019, arXiv first version). Max-Sliced Wasserstein Distance and its use for GANs. https://arxiv.org/abs/1904.05877.
• [Tibshirani2019·1904.06019] Tibshirani, Ryan J.; Barber, Rina Foygel; Candes, Emmanuel J.; Ramdas, Aaditya (2019, arXiv first version). Conformal Prediction Under Covariate Shift. https://arxiv.org/abs/1904.06019.
• [Romano2019·1905.03222] Romano, Yaniv; Patterson, Evan; Candès, Emmanuel J. (2019, arXiv first version). Conformalized Quantile Regression. https://arxiv.org/abs/1905.03222.
• [Hütter2019·1905.05828] Hütter, Jan-Christian; Rigollet, Philippe (2019, arXiv first version). Minimax estimation of smooth optimal transport maps. https://arxiv.org/abs/1905.05828.
• [Mena2019·1905.11882] Mena, Gonzalo; Weed, Jonathan (2019, arXiv first version). Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem. https://arxiv.org/abs/1905.11882.
• [März2019·1907.03178] März, Alexander (2019, arXiv first version). XGBoostLSS -- An extension of XGBoost to probabilistic forecasting. https://arxiv.org/abs/1907.03178.
• [Henzi2019·1909.03725] Henzi, Alexander; Ziegel, Johanna F.; Gneiting, Tilmann (2019, arXiv first version). Isotonic Distributional Regression. https://arxiv.org/abs/1909.03725. DOI: 10.1111/rssb.12450.
• [Manole2019·1909.07862] Manole, Tudor; Balakrishnan, Sivaraman; Wasserman, Larry (2019, arXiv first version). Minimax Confidence Intervals for the Sliced Wasserstein Distance. https://arxiv.org/abs/1909.07862. Journal ref on arXiv page: Electronic Journal of Statistics 2022, Vol 16, No. 1, 2252-2345. DOI: 10.1214/22-EJS2001.
• [Chernozhukov2019·1909.07889] Chernozhukov, Victor; Wüthrich, Kaspar; Zhu, Yinchu (2019, arXiv first version). Distributional conformal prediction. https://arxiv.org/abs/1909.07889. Journal ref on arXiv page: PNAS November 30, 2021 118 (48) e2107794118.
• [Duan2019·1910.03225] Duan, Tony; Avati, Anand; Ding, Daisy Yi; Thai, Khanh K.; et al. (2019, arXiv first version). NGBoost: Natural Gradient Boosting for Probabilistic Prediction. https://arxiv.org/abs/1910.03225.
• [Arbel2019·1910.09652] Arbel, Michael; Gretton, Arthur; Li, Wuchen; Montufar, Guido (2019, arXiv first version). Kernelized Wasserstein Natural Gradient. https://arxiv.org/abs/1910.09652.
• [Dubey2019·1911.11864] Dubey, Paromita; Müller, Hans-Georg (2019, arXiv first version). Fréchet Change Point Detection. https://arxiv.org/abs/1911.11864.
• [Gunsilius2020·2001.06118] Gunsilius, Florian (2020, arXiv first version). Distributional synthetic controls. https://arxiv.org/abs/2001.06118.
• [Chapel2020·2002.08276] Chapel, Laetitia; Alaya, Mokhtar Z.; Gasso, Gilles (2020, arXiv first version). Partial Optimal Transport with Applications on Positive-Unlabeled Learning. https://arxiv.org/abs/2002.08276.
• [Nadjahi2020·2003.05783] Nadjahi, Kimia; Durmus, Alain; Chizat, Lénaïc; Kolouri, Soheil; et al. (2020, arXiv first version). Statistical and Topological Properties of Sliced Probability Divergences. https://arxiv.org/abs/2003.05783.
• [Ćevid2020·2005.14458] Ćevid, Domagoj; Michel, Loris; Näf, Jeffrey; Meinshausen, Nicolai; et al. (2020, arXiv first version). Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. https://arxiv.org/abs/2005.14458.
• [Chen2020·2006.09660] Chen, Yaqing; Lin, Zhenhua; Müller, Hans-Georg (2020, arXiv first version). Wasserstein Regression. https://arxiv.org/abs/2006.09660.
• [Moosmüller2020·2008.09165] Moosmüller, Caroline; Cloninger, Alexander (2020, arXiv first version). Linear Optimal Transport Embedding: Provable Wasserstein classification for certain rigid transformations and perturbations. https://arxiv.org/abs/2008.09165.
• [Budhathoki2021·2102.13384] Budhathoki, Kailash; Janzing, Dominik; Bloebaum, Patrick; Ng, Hoiyi (2021, arXiv first version). Why did the distribution change?. https://arxiv.org/abs/2102.13384.
• [Scetbon2021·2103.04737] Scetbon, Meyer; Cuturi, Marco; Peyré, Gabriel (2021, arXiv first version). Low-Rank Sinkhorn Factorization. https://arxiv.org/abs/2103.04737.
• [Ghodrati2021·2104.09418] Ghodrati, Laya; Panaretos, Victor M. (2021, arXiv first version). Distribution-on-Distribution Regression via Optimal Transport Maps. https://arxiv.org/abs/2104.09418.
• [Zhu2021·2105.05439] Zhu, Changbo; Müller, Hans-Georg (2021, arXiv first version). Autoregressive Optimal Transport Models. https://arxiv.org/abs/2105.05439.
• [Gibbs2021·2106.00170] Gibbs, Isaac; Candès, Emmanuel (2021, arXiv first version). Adaptive Conformal Inference Under Distribution Shift. https://arxiv.org/abs/2106.00170.
• [Manole2021·2107.12364] Manole, Tudor; Balakrishnan, Sivaraman; Niles-Weed, Jonathan; Wasserman, Larry (2021, arXiv first version). Plugin Estimation of Smooth Optimal Transport Maps. https://arxiv.org/abs/2107.12364.
• [Torous2021·2108.05858] Torous, William; Gunsilius, Florian; Rigollet, Philippe (2021, arXiv first version). An Optimal Transport Approach to Estimating Causal Effects via Nonlinear Difference-in-Differences. https://arxiv.org/abs/2108.05858.
• [Pooladian2021·2109.12004] Pooladian, Aram-Alexandre; Niles-Weed, Jonathan (2021, arXiv first version). Entropic estimation of optimal transport maps. https://arxiv.org/abs/2109.12004.
• [Barber2022·2202.13415] Barber, Rina Foygel; Candes, Emmanuel J.; Ramdas, Aaditya; Tibshirani, Ryan J. (2022, arXiv first version). Conformal prediction beyond exchangeability. https://arxiv.org/abs/2202.13415.
• [Kulinski2022·2210.10275] Kulinski, Sean; Inouye, David I. (2022, arXiv first version). Towards Explaining Distribution Shifts. https://arxiv.org/abs/2210.10275.
• [Zhang2022·2210.10769] Zhang, Haoran; Singh, Harvineet; Ghassemi, Marzyeh; Joshi, Shalmali (2022, arXiv first version). "Why did the Model Fail?": Attributing Model Performance Changes to Distribution Shifts. https://arxiv.org/abs/2210.10769.
• [Séjourné2022·2211.08775] Séjourné, Thibault; Peyré, Gabriel; Vialard, François-Xavier (2022, arXiv first version). Unbalanced Optimal Transport, from Theory to Numerics. https://arxiv.org/abs/2211.08775.
• [Cuturi2023·2302.04065] Cuturi, Marco; Klein, Michal; Ablin, Pierre (2023, arXiv first version). Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps. https://arxiv.org/abs/2302.04065.
• [Kuhn2024·2411.02549] Kuhn, Daniel; Shafiee, Soroosh; Wiesemann, Wolfram (2024, arXiv first version). Distributionally Robust Optimization. https://arxiv.org/abs/2411.02549. Journal ref on arXiv page: Acta Numerica 34 (2025), 579-804. DOI: 10.1017/S0962492924000084.

B. Verified via Crossref DOI record or publisher page

• [Gneiting2007] Gneiting; Raftery (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. JASA. https://doi.org/10.1198/016214506000001437
• [DiNardo1996] DiNardo; Fortin; Lemieux (1996). Labor Market Institutions and the Distribution of Wages, 1973-1992: A Semiparametric Approach. Econometrica. https://doi.org/10.2307/2171954
• [GneitingRanjan2011] Gneiting; Ranjan (2011). Comparing Density Forecasts Using Threshold- and Quantile-Weighted Scoring Rules. J. Business & Economic Statistics. https://doi.org/10.1198/jbes.2010.08110
• [AtheyImbens2006] Athey; Imbens (2006). Identification and Inference in Nonlinear Difference-in-Differences Models. Econometrica. https://doi.org/10.1111/j.1468-0262.2006.00668.x
• [Firpo2009] Firpo; Fortin; Lemieux (title/venue/year verified; Crossref record had no author list, so author list UNVERIFIED) (2009). Unconditional Quantile Regressions. Econometrica. https://doi.org/10.3982/ECTA6822
• [KoenkerBassett1978] Koenker; Bassett (1978). Regression Quantiles. Econometrica. https://doi.org/10.2307/1913643
• [Hosking1990] Hosking (1990). L-Moments: Analysis and Estimation of Distributions Using Linear Combinations of Order Statistics. JRSS-B. https://doi.org/10.1111/j.2517-6161.1990.tb01775.x
• [ZouYuan2008] Zou; Yuan (2008). Composite quantile regression and the oracle model selection theory. Annals of Statistics. https://doi.org/10.1214/07-AOS507
• [Jiang2013] Jiang; Wang; Bondell (2013). Interquantile Shrinkage in Regression Models. J. Computational and Graphical Statistics. https://doi.org/10.1080/10618600.2012.707454
• [LaioTamea2007] Laio; Tamea (2007). Verification tools for probabilistic forecasts of continuous hydrological variables. Hydrology and Earth System Sciences. https://doi.org/10.5194/hess-11-1267-2007
• [Gneiting2005] Gneiting; Raftery; Westveld; Goldman (2005). Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation. Monthly Weather Review. https://doi.org/10.1175/MWR2904.1
• [RigbyStasinopoulos2005] Rigby; Stasinopoulos (2005). Generalized Additive Models for Location, Scale and Shape. JRSS-C. https://doi.org/10.1111/j.1467-9876.2005.00510.x
• [Mayr2012] Mayr; Fenske; Hofner; Kneib; Schmid (2012). Generalized Additive Models for Location, Scale and Shape for High Dimensional Data—A Flexible Approach Based on Boosting. JRSS-C. https://doi.org/10.1111/j.1467-9876.2011.01033.x
• [Shimodaira2000] Shimodaira (2000). Improving predictive inference under covariate shift by weighting the log-likelihood function. J. Statistical Planning and Inference. https://doi.org/10.1016/S0378-3758(00)00115-4
• [MachadoMata2005] Machado; Mata (2005). Counterfactual decomposition of changes in wage distributions using quantile regression. J. Applied Econometrics. https://doi.org/10.1002/jae.788
• [SzekelyRizzo2013] Székely; Rizzo (2013). Energy statistics: A class of statistics based on distances. J. Statistical Planning and Inference. https://doi.org/10.1016/j.jspi.2013.03.018
• [CFM2013] Chernozhukov; Fernández-Val; Melly (per arXiv 0904.0951) (2013). Inference on Counterfactual Distributions. Econometrica. https://doi.org/10.3982/ECTA10582
• [Hallin2021] Hallin; del Barrio; Cuesta-Albertos; Matrán (2021). Distribution and quantile functions, ranks and signs in dimension d: A measure transportation approach. Annals of Statistics. https://doi.org/10.1214/20-AOS1996
• [PetersenMuller2019] Petersen; Müller (2019). Fréchet regression for random objects with Euclidean predictors. Annals of Statistics. https://doi.org/10.1214/17-AOS1624
• [ChenLinMuller2021] Chen; Lin; Müller (2021). Wasserstein Regression. JASA (Crossref issued 2021; often cited as 2023 print). https://doi.org/10.1080/01621459.2021.1956937
• [CGHH2017] Chernozhukov; Galichon; Hallin; Henry (2017). Monge–Kantorovich depth, quantiles, ranks and signs. Annals of Statistics. https://doi.org/10.1214/16-AOS1450
• [GaoKleywegt2023] Gao; Kleywegt (2023). Distributionally Robust Stochastic Optimization with Wasserstein Distance. Mathematics of Operations Research. https://doi.org/10.1287/moor.2022.1275
• [Bondell2010] Bondell; Reich; Wang (2010). Noncrossing quantile regression curve estimation. Biometrika. https://doi.org/10.1093/biomet/asq048
• [Meinshausen2006] Meinshausen (2006). Quantile Regression Forests. JMLR 7. https://www.jmlr.org/papers/v7/meinshausen06a.html (page title verified)
• [Cevid2022] Ćevid; Michel; Näf; Meinshausen; Bühlmann (2022). Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. JMLR 23. https://jmlr.org/papers/v23/21-0585.html (page title verified; arXiv 2005.14458 above)
• [KulinskiInouye2023] Kulinski; Inouye (2023). Towards Explaining Distribution Shifts. ICML 2023, PMLR 202. https://proceedings.mlr.press/v202/kulinski23a.html (page title verified)
• [SongMuller2026] Song; Müller (2026). ADOPT: Additive Optimal Transport Regression. AISTATS 2026, PMLR 300:1558–1566. https://proceedings.mlr.press/v300/song26c.html (page title verified)
• [Gretton2012] Gretton et al. (2012). A Kernel Two-Sample Test. JMLR 13. https://jmlr.org/papers/v13/gretton12a.html (page title verified)

C. UNVERIFIED / deliberately not cited

• Laio & Tamea 2007 is cited for the CRPS = 2∫pinball identity; title/venue verified but I did not check the equation number inside the paper. Treat the location of that identity in the paper as UNVERIFIED (the identity itself is standard and is re-derived in TRACE_DESIGN.md / THEORY.md).
• Vector quantile regression (Carlier, Chernozhukov, Galichon) journal version (Annals of Statistics 2016): the DOI I tried did not resolve to it; journal details UNVERIFIED, arXiv version verified.
• Mohajerin Esfahani & Kuhn journal version (Mathematical Programming 2018): UNVERIFIED; arXiv verified.
• Exact convergence rates of entropic/smooth map estimators are described only qualitatively in LANDSCAPE.md; I did not transcribe rate exponents from the papers.