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Found 12 papers, 3 papers with code
FedCore: Straggler-Free Federated Learning with Distributed Coresets
Federated learning (FL) is a machine learning paradigm that allows multiple clients to collaboratively train a shared model while keeping their data on-premise.
Transportation Marketplace Rate Forecast Using Signature Transform
Freight transportation marketplace rates are typically challenging to forecast accurately.
Risk of Transfer Learning and its Applications in Finance
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
Transfer Learning for Portfolio Optimization
In particular, 1. a strong correlation between the transfer risk and the overall performance of transfer learning methods is established, underscoring the significance of transfer risk as a viable indicator of "transferability"; 2. transfer risk is shown to provide a computationally efficient way to identify appropriate source tasks in transfer learning, enhancing the efficiency and effectiveness of the transfer learning approach; 3. additionally, the numerical experiments offer valuable new insights for portfolio management across these different settings.
Feasibility of Transfer Learning: A Mathematical Framework
Transfer learning is a popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones.
Feasibility and Transferability of Transfer Learning: A Mathematical Framework
In this paper we build for the first time, to the best of our knowledge, a mathematical framework for the general procedure of transfer learning.
Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
Flow analysis carried out using phase contrast cardiac magnetic resonance imaging (PC-CMR) enables the quantification of important parameters that are used in the assessment of cardiovascular function.
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle.
Mean-Field Multi-Agent Reinforcement Learning: A Decentralized Network Approach
This paper proposes a framework of localized training and decentralized execution to study MARL with network of states.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adversarial Training for Gradient Descent: Analysis Through its Continuous-time Approximation
Adversarial training has gained great popularity as one of the most effective defenses for deep neural network and more generally for gradient-based machine learning models against adversarial perturbations on data points.
Mean-Field Controls with Q-learning for Cooperative MARL: Convergence and Complexity Analysis
Multi-agent reinforcement learning (MARL), despite its popularity and empirical success, suffers from the curse of dimensionality.
An efficient model reduction method for solving viscous G-equations in incompressible cellular flows
To facilitate the algorithm design and convergence analysis, we decompose the solution of the viscous G-equation into a mean-free part and a mean part, where their evolution equations can be derived accordingly.
Numerical Analysis 65M12, 70H20, 76F25, 78M34, 80A25
