29 citations · 50 across the 8 of their papers we have counts for
8 papers
Koopman Invertible Autoencoder: Leveraging Forward and Backward Dynamics for Temporal Modeling
Kshitij Tayal, Arvind Renganathan, Rahul Ghosh +2
Accurate long-term predictions are the foundations for many machine learning applications and decision-making processes. However, building accurate long-term prediction models rema…
The Future of Fundamental Science Led by Generative Closed-Loop Artificial Intelligence
Hector Zenil, Jesper Tegnér, Felipe S. Abrahão +17
Recent advances in machine learning and AI, including Generative AI and LLMs, are disrupting technological innovation, product development, and society as a whole. AI's contributio…
Time Series Predictions in Unmonitored Sites: A Survey of Machine Learning Techniques in Water Resources
Jared D. Willard, Charuleka Varadharajan, Xiaowei Jia +1
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing challenge for water resources science. The majority of the world's freshwater resources h…
Bayesian Federated Learning: A Survey
Longbing Cao, Hui Chen, Xuhui Fan +3
Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the ro…
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent Space
Jia Li, Xiang Li, Xiaowei Jia +2
Causal DAGs(Directed Acyclic Graphs) are usually considered in a 2D plane. Edges indicate causal effects' directions and imply their corresponding time-passings. Due to the natural…
Robust Inverse Framework using Knowledge-guided Self-Supervised Learning: An application to Hydrology
Rahul Ghosh, Arvind Renganathan, Kshitij Tayal +6
Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, buildin…
Phase Retrieval using Single-Instance Deep Generative Prior
Kshitij Tayal, Raunak Manekar, Zhong Zhuang +4
Several deep learning methods for phase retrieval exist, but most of them fail on realistic data without precise support information. We propose a novel method based on single-inst…
Incorporating Causal Effects into Deep Learning Predictions on EHR Data
Jia Li, Haoyu Yang, Xiaowei Jia +3
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, c…