Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

no code implementations • 20 Feb 2024 • Hongtao Zhu, Sizhe Zhang, Yang Su, Zhenyu Zhao, Nan Chen

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects.

Active Learning Causal Inference

Automated Architecture Search for Brain-inspired Hyperdimensional Computing

no code implementations • 11 Feb 2022 • Junhuan Yang, Yi Sheng, Sizhe Zhang, Ruixuan Wang, Kenneth Foreman, Mikell Paige, Xun Jiao, Weiwen Jiang, Lei Yang

On the Clintox dataset, which tries to learn features from developed drugs that passed/failed clinical trials for toxicity reasons, the searched HDC architecture obtains the state-of-the-art ROC-AUC scores, which are 0. 80% higher than the manually designed HDC and 9. 75% higher than conventional neural networks.

Drug Discovery

HDCoin: A Proof-of-Useful-Work Based Blockchain for Hyperdimensional Computing

no code implementations • 7 Feb 2022 • Dongning Ma, Sizhe Zhang, Xun Jiao

We formulate the model development of HDC as a problem that can be used in blockchain mining.