Fetching the paper…
Reading the bibliography…
Data-driven approaches to predict-then-optimize decision-making problems seek to mitigate the risk of uncertainty region misspecification in safety-critical settings.
“The problem of optimum stratification”
Tore Dalenius · 1950
Earlier work this paper cites.
“The problem of optimum stratification. II”
Tore Dalenius and Margaret Gurney · 1951
Earlier work this paper cites.
“A note on a method for generating points uniformly on n-dimensional spheres”
Mervin Muller · 1959
Earlier work this paper cites.
“Quantizing for minimum distortion”
Joel Max · 1960
Earlier work this paper cites.
“Representing a large collection of curves: A case for principal points”
Bernard Flury and Thaddeus Tarpey · 1993
Earlier work this paper cites.
“The union of balls and its dual shape”
H. Edelsbrunner · 1995
Earlier work this paper cites.
“Vehicle routing problem with stochastic demands”
V“’aclav Koren“’ar · 2003
Earlier work this paper cites.
“The robust shortest path problem by means of robust linear optimization”
Diah Chaerani, Cornelis Roos and A Aman · 2005
Earlier work this paper cites.
“Robust optimization–a comprehensive survey”
Hans-Georg Beyer and Bernhard Sendhoff · 2007
Earlier work this paper cites.
“A (slightly) faster algorithm for Klee’s measure problem”
Timothy Chan · 2008
Earlier work this paper cites.
“A Tutorial on Conformal Prediction.”
Glenn Shafer and Vladimir Vovk · 2008
Earlier work this paper cites.
“Robust optimization”
Aharon Ben-Tal, Laurent El and Arkadi Nemirovski · 2009
Earlier work this paper cites.
“Continuous approximation model for the vehicle routing problem for emissions minimization at the strategic level”
Meead Saberi and “.I“”Omer Verbas · 2012
Earlier work this paper cites.
“Monte Carlo: concepts, algorithms, and applications”
George Fishman · 2013
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks”
Geoff Boeing · 2017
Earlier work this paper cites.
“Towards a rigorous science of interpretable machine learning”
Finale Doshi-Velez and Been Kim · 2017
Earlier work this paper cites.
“Deep learning for precipitation nowcasting: A benchmark and a new model”
Xingjian Shi et al · 2017
Earlier work this paper cites.
“Data-driven robust optimization”
Dimitris Bertsimas, Vishal Gupta and Nathan Kallus · 2018
Earlier work this paper cites.
“Machine learning for precipitation nowcasting from radar images”
Shreya Agrawal et al · 2019
Earlier work this paper cites.
“Forward amortized inference for likelihood-free variational marginalization”
Luca Ambrogioni et al · 2019
Earlier work this paper cites.
“Neural spline flows”
Conor Durkan, Artur Bekasov, Iain Murray and George Papamakarios · 2019
Cited alongside, same era.
“The right to explanation, explained”
Margot Kaminski · 2019
Cited alongside, same era.
“A metaheuristic approach to solve dynamic vehicle routing problem in continuous search space”
Micha Okulewicz and Jacek Ma“’ndziuk · 2019
Cited alongside, same era.
“Pytorch: An imperative style, high-performance deep learning library”
Adam Paszke et al · 2019
Cited alongside, same era.
“A data-driven robust optimization approach to scenario-based stochastic model predictive control”
Chao Shang and Fengqi You · 2019
Cited alongside, same era.
“RainNet v1. 0: a convolutional neural network for radar-based precipitation nowcasting”
Georgy Ayzel, Tobias Scheffer and Maik Heistermann · 2020
“Data-driven conditional robust optimization”
Abhilash Chenreddy, Nymisha Bandi and Erick Delage · 2022
Later among the works it cites.
“Smart “predict, then optimize””
Adam Elmachtoub and Paul Grigas · 2022
Later among the works it cites.
“Conformal prediction under feedback covariate shift for biomolecular design”
Clara Fannjiang et al · 2022
Later among the works it cites.
“Conffusion: Confidence intervals for diffusion models”
Eliahu Horwitz and Yedid Hoshen · 2022
Later among the works it cites.
“Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials”
Yuge Hu, Joseph Musielewicz, Zachary Ulissi and Andrew Medford · 2022
Later among the works it cites.
“Cd-split and hpd-split: Efficient conformal regions in high dimensions”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
“nflows: normalizing flows in PyTorch”
Conor Durkan, Artur Bekasov, Iain Murray and George Papamakarios · 2020
Cited alongside, same era.
“Decision trees for decision-making under the predict-then-optimize framework”
Adam Elmachtoub, Jason Cheuk Liang and Ryan McNellis · 2020
Cited alongside, same era.
“Precipitation nowcasting with orographic enhanced stacked generalization: Improving deep learning predictions on extreme events”
Gabriele Franch et al · 2020
Cited alongside, same era.
“A comparison of some conformal quantile regression methods”
Matteo Sesia and Emmanuel Cand“‘es · 2020
Cited alongside, same era.
“System optimal routing of traffic flows with user constraints using linear programming”
Enrico Angelelli, Valentina Morandi, Martin Savelsbergh and Maria Speranza · 2021
Cited alongside, same era.
“A gentle introduction to conformal prediction and distribution-free uncertainty quantification”
Anastasios Angelopoulos and Stephen Bates · 2021
Cited alongside, same era.
Rafael Izbicki, Gilson Shimizu and Rafael Stern · 2022
Later among the works it cites.
“GFlowNets and variational inference”
Nikolay Malkin et al · 2022
Later among the works it cites.
“Valid model-free spatial prediction”
Huiying Mao, Ryan Martin and Brian Reich · 2022
Later among the works it cites.
“Vehicle routing problems over time: a survey”
Andrea Mor and Maria Speranza · 2022
Later among the works it cites.
“Probabilistic conformal prediction using conditional random samples”
Zhendong Wang et al · 2022
Later among the works it cites.
“Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems”
Omer Belhasin et al · 2023
Closest in time.
“A Review of Representative Points of Statistical Distributions and Their Applications”
Kai-Tai Fang and Jianxin Pan · 2023
Closest in time.
“Calibrated multiple-output quantile regression with representation learning”
Shai Feldman, Stephen Bates and Yaniv Romano · 2023
Closest in time.
“GFlowNet-EM for learning compositional latent variable models”
Edward Hu et al · 2023
Closest in time.
Jussi Leinonen et al · 2023
Closest in time.
“Variational Inference with Coverage Guarantees”
Yash Patel et al · 2023
Closest in time.
“Contextual robust optimisation with uncertainty quantification”
Egon Persak and Miguel Anjos · 2023
Closest in time.
“A Survey of Contextual Optimization Methods for Decision Making under Uncertainty”
Utsav Sadana et al · 2023
Closest in time.
“Predict-then-Calibrate: A New Perspective of Robust Contextual LP”
Chunlin Sun, Linyu Liu and Xiaocheng Li · 2023
Closest in time.
“Physics constrained motion prediction with uncertainty quantification”
Renukanandan Tumu, Lars Lindemann, Truong Nghiem and Rahul Mangharam · 2023
Closest in time.