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Jia-Jie Zhu

jjzhu
1 Stars

Decks

Oliver Tse (Eindhoven University of Technology, Netherlands) Variational Acceleration Methods in the Space of Probability Measures
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170
Olga Mula (Eindhoven University of Technology, Netherlands) Reduced Models in Wasserstein Spaces for Forward and Inverse Problems
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120
Chaoyue Zhao (University of Washington, Seattle, USA) Provably Convergent Policy Optimization via Metric-aware Trust Region Methods
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76
Guillaume Carlier (Université Paris Dauphine - PSL, France) Displacement Smoothness of Entropic Optimal Transport and Applications to some Evolution Equations and Systems
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140
Virginie Ehrlacher (ENPC & INRIA, Paris, France) Sparse Approximation of Multi-marginal Quantum Optimal Transport Problems with Moment Constraints: Application to Quantum Chemistry
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100
Gabriel Peyré (CNRS & ENS, Paris, France) Sparsistency for Inverse Optimal Transport
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140
Xue-Mei Li (École Polytechnique Fédérale de Lausanne, Switzerland) Coarse Extrinsic Curvature
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140
Mi Jung Park (Technical University of Denmark, Denmark) Privacy-preserving Data Generation in the Era of Foundation Models: Generative Transfer Learning with Differential Privacy
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94
Taiji Suzuki (University of Tokyo, Japan) Convergence of mean field Langevin dynamics and its application to neural network feature learning
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320
Wuchen Li (University of South Carolina, Columbia, USA) Information Gamma Calculus: Convexity Analysis for Stochastic Differential Equations
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210
Julie Delon (Université Paris-Cité, France) Optimal Transport with Invariances between Gaussian Mixture Models
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140
Jiaxin Shi (Google DeepMind, London, UK) Stein’s Method for Modern Machine Learning: From Gradient Estimation to Generative Modeling
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490
Laetitia Chapel (Université de Bretagne-Sud & Institut Agro, France) Fast Optimal Transport through Sliced Generalized Wasserstein Geodesics
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90
Massimiliano Pontil (IIT Genoa & UC London, Italy & UK) Transfer Operator Learning
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130
Claudia Totzeck (Bergische Universität Wuppertal, Germany) Adjoint-based Optimal Control with Wasserstein-2 Metric
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96
Hongseok Namkoong (Columbia University, New York, USA) On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets
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98
Emtiyaz Khan (RIKEN, Tokyo, Japan) The Bayesian Learning Rule
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280
Bernhard Schmitzer (Georg-August-Universität Göttingen, Germany) Entropic Transfer Operators for Data-driven Analysis of Dynamical Systems
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140

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