Zobrazeno 1 - 10
of 292
pro vyhledávání: '"A. Stepaniants"'
Feature alignment methods are used in many scientific disciplines for data pooling, annotation, and comparison. As an instance of a permutation learning problem, feature alignment presents significant statistical and computational challenges. In this
Externí odkaz:
http://arxiv.org/abs/2311.13595
Autor:
Abadie, Alberto, Agarwal, Anish, Imbens, Guido, Jia, Siwei, McQueen, James, Stepaniants, Serguei
Business/policy decisions are often based on evidence from randomized experiments and observational studies. In this article we propose an empirical framework to estimate the value of evidence-based decision making (EBDM) and the return on the invest
Externí odkaz:
http://arxiv.org/abs/2306.13681
Autor:
Breeur, Marie, Stepaniants, George, Keski-Rahkonen, Pekka, Rigollet, Philippe, Viallon, Vivian
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-M
Externí odkaz:
http://arxiv.org/abs/2306.03218
Despite rapid progress in live-imaging techniques, many complex biophysical and biochemical systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an incomplete set
Externí odkaz:
http://arxiv.org/abs/2304.04818
Comparing the representations learned by different neural networks has recently emerged as a key tool to understand various architectures and ultimately optimize them. In this work, we introduce GULP, a family of distance measures between representat
Externí odkaz:
http://arxiv.org/abs/2210.06545
Publikováno v:
eLife, Vol 12 (2024)
Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-M
Externí odkaz:
https://doaj.org/article/30115f1a58ce442db8f042d039efe2b2
Autor:
Stepaniants, George
Publikováno v:
Journal of Machine Learning Research 24 (2023) 1-72
We propose a new data-driven approach for learning the fundamental solutions (Green's functions) of various linear partial differential equations (PDEs) given sample pairs of input-output functions. Building off the theory of functional linear regres
Externí odkaz:
http://arxiv.org/abs/2108.11580
Publikováno v:
Physical Review Research, Vol 6, Iss 4, p 043062 (2024)
Despite rapid progress in data acquisition techniques, many complex physical, chemical, and biological systems remain only partially observable, thus posing the challenge to identify valid theoretical models and estimate their parameters from an inco
Externí odkaz:
https://doaj.org/article/5084f6e0b0054d17aabf72dff2279977
Autor:
Mariéta Stepaniants
Publikováno v:
Reflexão, Vol 14, Iss 41 (2024)
A história da cultura espiritual dos povos mostra que sempre e em toda a parte, ao lado da disposição de observar as normas de conduta ditadas pela sociedade, existiu frequentemente uma tendência oposta. de regulação de comportamento humano em
Externí odkaz:
https://doaj.org/article/d994723107754f7698fcd46880415b5b
Autor:
Chewi, Sinho, Clancy, Julien, Gouic, Thibaut Le, Rigollet, Philippe, Stepaniants, George, Stromme, Austin J.
We propose a new method for smoothly interpolating probability measures using the geometry of optimal transport. To that end, we reduce this problem to the classical Euclidean setting, allowing us to directly leverage the extensive toolbox of spline
Externí odkaz:
http://arxiv.org/abs/2010.12101