Search Results for author: Yongsheng Mei

Found 8 papers, 3 papers with code

Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions

1 code implementation21 Feb 2024 Jiayu Chen, Bhargav Ganguly, Yang Xu, Yongsheng Mei, Tian Lan, Vaneet Aggarwal

This work offers a hands-on reference for the research progress in deep generative models for offline policy learning, and aims to inspire improved DGM-based offline RL or IL algorithms.

Imitation Learning Offline RL

Bayesian Optimization through Gaussian Cox Process Models for Spatio-temporal Data

no code implementations25 Jan 2024 Yongsheng Mei, Mahdi Imani, Tian Lan

Bayesian optimization (BO) has established itself as a leading strategy for efficiently optimizing expensive-to-evaluate functions.

Bayesian Optimization

Real-time Network Intrusion Detection via Decision Transformers

no code implementations12 Dec 2023 Jingdi Chen, Hanhan Zhou, Yongsheng Mei, Gina Adam, Nathaniel D. Bastian, Tian Lan

Many cybersecurity problems that require real-time decision-making based on temporal observations can be abstracted as a sequence modeling problem, e. g., network intrusion detection from a sequence of arriving packets.

Decision Making Network Intrusion Detection +1

MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization

no code implementations21 Feb 2023 Yongsheng Mei, Hanhan Zhou, Tian Lan, Guru Venkataramani, Peng Wei

To this end, we propose MAC-PO, which formulates optimal prioritized experience replay for multi-agent problems as a regret minimization over the sampling weights of transitions.

Decision Making Multi-agent Reinforcement Learning +3

ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning

no code implementations11 Feb 2023 Yongsheng Mei, Hanhan Zhou, Tian Lan

Such an optimization problem can be relaxed and solved using the Lagrangian multiplier method to obtain the close-form optimal projection weights.

Decision Making reinforcement-learning +2

Exploiting Partial Common Information Microstructure for Multi-Modal Brain Tumor Segmentation

1 code implementation6 Feb 2023 Yongsheng Mei, Guru Venkataramani, Tian Lan

Our experimental results on the Multi-modal Brain Tumor Segmentation Challenge (BraTS) datasets outperform those of state-of-the-art segmentation baselines, with validation Dice similarity coefficients of 0. 920, 0. 897, 0. 837 for the whole tumor, tumor core, and enhancing tumor on BraTS-2020.

Brain Tumor Segmentation Image Segmentation +2

A Bayesian Optimization Framework for Finding Local Optima in Expensive Multi-Modal Functions

1 code implementation13 Oct 2022 Yongsheng Mei, Tian Lan, Mahdi Imani, Suresh Subramaniam

This joint distribution is used in the body of the BO acquisition functions to search for local optima during the optimization process.

Bayesian Optimization

Cannot find the paper you are looking for? You can Submit a new open access paper.