3 accepted papers
Clinical reinforcement learning (RL) holds promise for treatment recommendation but remains hindered by black-box decision processes, limited safety guarantees, and lack of individualized reasoning. We introduce Delphi Engine, the first fully trainable neuro-symbolic causal RL framework for dynamic
Quantization-aware Training (QAT) enables deep models to adapt to precision loss by simulating quantization. However, existing methods often converge to sub-optimal solutions due to inadequate exploration of quantization solution space. To address this, we propose a novel QAT method, Allowing Oscill…
Federated Learning (FL) is an emerging direction in distributed machine learning that enables jointly training a model without sharing the data. However, as the size of datasets grows exponentially, computational costs of FL increase. In this paper, we propose the first Coreset Selection criterion f…