Results 81 to 90 of about 6,417 (238)
On rate optimality for ill-posed inverse problems in econometrics [PDF]
In this paper, we clarify the relations between the existing sets of regularity conditions for convergence rates of nonparametric indirect regression (NPIR) and nonparametric instrumental variables (NPIV) regression models.
Xiaohong Chen, Markus Reiss
core
Transient Porosity During Fluid‐Mineral Interaction, Part 2: Reconstruction Using Generative AI
Journal of Geophysical Research: Solid Earth, Volume 131, Issue 6, June 2026.Abstract Quantifying fluid–rock interactions within the lithosphere is vital for both geological processes and applications such as CO2 ${\text{CO}}_{2}$ storage and geothermal energy development. Mineral replacement reactions generate transient pore networks that enhance fluid flow, yet many pores become isolated once reactions are completed, reducing
Hamed Amiri +5 more
wiley +1 more source
Journal of Applied Mathematics, 2012 By using minimax methods in critical point theory, a new existence theorem of infinitely many periodic solutions is obtained for a class of second-order p-Laplacian systems with impulsive effects. Our result generalizes many known works in the literature.
Wen-Zhen Gong +2 more
doaj +1 more source
A Generalization of the Minisum and Minimax Voting Methods
SIAM Undergraduate Research Online, 2018 11 ...
openaire +2 more sources
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.Abstract Atmosphere‐ocean‐land coupled forecasting systems, despite their comprehensiveness, face substantial challenges in the “predictability desert” at subseasonal to seasonal (S2S) timescales, particularly for precipitation—a variable crucial for socioeconomic activities yet of stunning spatiotemporal variance. Post‐processing methods developed for
Wen Shi +9 more
wiley +1 more source
How effective are minimax methods in mitigating sample selection bias?
, 2023 Sample selection bias is a well-known problem in machine learning, where the source and target data distributions differ, leading to biased predictions and difficulties in generalization.
Khan, Zeeshan (author)
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Decentralized Riemannian Algorithm for Nonconvex Minimax Problems
, 2023 The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and deep neural networks with orthogonal weights (Stiefel manifold).
Hu, Zhengmian, Huang, Heng, Wu, Xidong
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Ural Mathematical JournalThis paper deals with analytical and numerical methods for constructing a minimax (generalized) solution to the Dirichlet problem for the Hamilton–Jacobi equation.
Pavel D. Lebedev, Alexander A. Uspenskii
doaj +1 more source
Water Resources Research, Volume 62, Issue 6, June 2026.Abstract Generative adversarial networks (GANs) have proven effective in simulating complex reservoir environments, such as meandering channels and deltas. In classic GANs, the dimensionality of training data determines that of generated data: a 2D (or 3D) reservoir facies simulator (generator) requires training with corresponding 2D (or 3D) data sets.
Xun Hu +4 more
wiley +1 more source
Unsupervised Representation Learning with Minimax Distance Measures
, 2020 We investigate the use of Minimax distances to extract in a nonparametric way the features that capture the unknown underlying patterns and structures in the data.
Chehreghani, Morteza Haghir +1 more
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