Establishes an explicit strong-convexity modulus for the barycentric variance functional on Alexandrov spaces, implying Hölder stability of barycenters and empirical consistency bounds without using linear structure.
Panaretos and Yoav Zemel
8 Pith papers cite this work, alongside 688 external citations. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
representative citing papers
Stability estimates show the k-plane transform on Radon measures is bi-Lipschitz equivalent to a Fourier metric and Hölder equivalent to Wasserstein distance, with a strong Sobolev equivalence for bounded-density measures.
Introduces minimum free energy randomized design and dynamic allocation algorithm for RCTs to balance covariate control with randomization robustness via thermodynamic analogy.
Experimentally derived mosquito extrinsic incubation period distributions delay and flatten dengue epidemic peaks, reduce peak intensity, and slightly prolong crisis duration compared to exponential assumptions, while leaving outbreak probability largely unchanged.
Sharp convergence rates and concentration bounds are established for empirical measures of point processes under a newly introduced Wasserstein distance on counting measures.
A centered swept-back multipolar magnetic field up to octupole order reproduces the bolometric thermal X-ray light curve of MSP J0030+0451.
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
CTGAN and LLMs generate synthetic student data that passes statistical and predictive utility checks for learning analytics.
citing papers explorer
-
Quantitative Stability of Wasserstein Barycenters over Alexandrov Spaces with Lower Curvature Bounds
Establishes an explicit strong-convexity modulus for the barycentric variance functional on Alexandrov spaces, implying Hölder stability of barycenters and empirical consistency bounds without using linear structure.
-
Stability Estimates for the $k$-plane Transform on Measures and a H\"older-Type Comparison Between Wasserstein and Max-Sliced Wasserstein Distances
Stability estimates show the k-plane transform on Radon measures is bi-Lipschitz equivalent to a Fourier metric and Hölder equivalent to Wasserstein distance, with a strong Sobolev equivalence for bounded-density measures.
-
Minimum free energy randomized design to improve covariate balance
Introduces minimum free energy randomized design and dynamic allocation algorithm for RCTs to balance covariate control with randomization robustness via thermodynamic analogy.
-
From lab to outbreak: experimental mosquito extrinsic incubation period distributions shape dengue epidemic dynamics
Experimentally derived mosquito extrinsic incubation period distributions delay and flatten dengue epidemic peaks, reduce peak intensity, and slightly prolong crisis duration compared to exponential assumptions, while leaving outbreak probability largely unchanged.
-
Wasserstein convergence rates for empirical measures of point processes
Sharp convergence rates and concentration bounds are established for empirical measures of point processes under a newly introduced Wasserstein distance on counting measures.
-
The swept-back multipolar magnetic field of neutron stars: Application to NICER MSP J0030+0451
A centered swept-back multipolar magnetic field up to octupole order reproduces the bolometric thermal X-ray light curve of MSP J0030+0451.
-
The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy
An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.
-
Creating Artificial Students that Never Existed: Leveraging Large Language Models and CTGANs for Synthetic Data Generation
CTGAN and LLMs generate synthetic student data that passes statistical and predictive utility checks for learning analytics.