Search Results for author:
Found 8 papers, 4 papers with code
Knowledge Sharing and Transfer via Centralized Reward Agent for Multi-Task Reinforcement Learning
Reward shaping is effective in addressing the sparse-reward challenge in reinforcement learning by providing immediate feedback through auxiliary informative rewards.
Highly Efficient Self-Adaptive Reward Shaping for Reinforcement Learning
Reward shaping addresses the challenge of sparse rewards in reinforcement learning by constructing denser and more informative reward signals.
Decoupled Prompt-Adapter Tuning for Continual Activity Recognition
Action recognition technology plays a vital role in enhancing security through surveillance systems, enabling better patient monitoring in healthcare, providing in-depth performance analysis in sports, and facilitating seamless human-AI collaboration in domains such as manufacturing and assistive technologies.
Federated Causal Inference from Observational Data
In this article, we propose a framework to estimate causal effects from decentralized data sources.
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects
We propose a new causal inference framework to learn causal effects from multiple, decentralized data sources in a federated setting.
Adaptive Multi-Source Causal Inference
The proposed method can infer causal effects in the target population without prior knowledge of data discrepancy between the additional data sources and the target.
Federated Estimation of Causal Effects from Observational Data
Many modern applications collect data that comes in federated spirit, with data kept locally and undisclosed.
Causal Modeling with Stochastic Confounders
This work extends causal inference with stochastic confounders.
