Search Results for author:
Found 9 papers, 2 papers with code
CAP: A General Algorithm for Online Selective Conformal Prediction with FCR Control
To avoid devoting resources to unimportant units, a preliminary selection of the current individual before reporting its prediction interval is common and meaningful in online predictive tasks.
EPISODE: Episodic Gradient Clipping with Periodic Resampled Corrections for Federated Learning with Heterogeneous Data
In this paper, we design EPISODE, the very first algorithm to solve FL problems with heterogeneous data in the nonconvex and relaxed smoothness setting.
An Information-Theoretic Approach to Transferability in Task Transfer Learning
Task transfer learning is a popular technique in image processing applications that uses pre-trained models to reduce the supervision cost of related tasks.
Fast Composite Optimization and Statistical Recovery in Federated Learning
This paper investigates a class of composite optimization and statistical recovery problems in the FL setting, whose loss function consists of a data-dependent smooth loss and a non-smooth regularizer.
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees
This paper presents a learning- and scenario-based model predictive control (MPC) design approach for systems modeled in linear parameter-varying (LPV) framework.
Optimal Lighting Control in Greenhouses Using Bayesian Neural Networks for Sunlight Prediction
For evaluation, the new strategy is compared to: 1) a Markov-based prediction method, which solves the same optimization problem, assuming a Markov model for sunlight prediction; 2) a heuristic method which aims to supply a fixed amount of light.
A Deep Reinforcement Learning-based Sliding Mode Control Design for Partially-known Nonlinear Systems
This paper presents a sliding mode control (SMC) design approach for nonlinear systems with partially known dynamics by blending data-driven and model-based approaches.
Varying Coefficient Linear Discriminant Analysis for Dynamic Data
Linear discriminant analysis (LDA) is an important classification tool in statistics and machine learning.
An Information-Theoretic Metric of Transferability for Task Transfer Learning
An important question in task transfer learning is to determine task transferability, i. e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task.
