Search Results for author:
Found 11 papers, 2 papers with code
Collaborative and Distributed Bayesian Optimization via Consensus: Showcasing the Power of Collaboration for Optimal Design
Empirically, through simulated datasets and a real-world collaborative material discovery experiment, we show that our framework can effectively accelerate and improve the optimal design process and benefit all participants.
Federated Data Analytics: A Study on Linear Models
In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression.
Self-scalable Tanh (Stan): Faster Convergence and Better Generalization in Physics-informed Neural Networks
To address this, a Self-scalable tanh (Stan) activation function is proposed for the PINNs.
Federated Gaussian Process: Convergence, Automatic Personalization and Multi-fidelity Modeling
In this paper, we propose \texttt{FGPR}: a Federated Gaussian process ($\mathcal{GP}$) regression framework that uses an averaging strategy for model aggregation and stochastic gradient descent for local client computations.
The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning
The Internet of Things (IoT) is on the verge of a major paradigm shift.
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings.
SALR: Sharpness-aware Learning Rate Scheduler for Improved Generalization
In an effort to improve generalization in deep learning and automate the process of learning rate scheduling, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.
SALR: Sharpness-aware Learning Rates for Improved Generalization
In an effort to improve generalization in deep learning, we propose SALR: a sharpness-aware learning rate update technique designed to recover flat minimizers.
Why Non-myopic Bayesian Optimization is Promising and How Far Should We Look-ahead? A Study via Rollout
We then provide both a theoretical and practical guideline to decide on the rolling horizon stagewise.
The Renyi Gaussian Process: Towards Improved Generalization
We introduce an alternative closed form lower bound on the Gaussian process ($\mathcal{GP}$) likelihood based on the R\'enyi $\alpha$-divergence.
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
We present a non-parametric prognostic framework for individualized event prediction based on joint modeling of both longitudinal and time-to-event data.
