10.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Harvard UniversityUS735 papers
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- University of ChicagoUS368 papers
- University of MichiganUS356 papers
- Lawrence Berkeley National LaboratoryUS354 papers
- The Ohio State UniversityUS350 papers
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- University of PittsburghUS317 papers
- Argonne National LaboratoryUS316 papers
- University College LondonGB312 papers
- University of PisaIT310 papers
10 papers · 2 filters
IRL for Restless Multi-Armed Bandits with Applications in Maternal and Child Health
Gauri Jain, Pradeep Varakantham, Haifeng Xu +3
Public health practitioners often have the goal of monitoring patients and maximizing patients' time spent in "favorable" or healthy states while being constrained to using limited…
Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
Shuchen Meng, Andi Chen, Chihang Wang +5
Accurate exchange rate prediction is fundamental to financial stability and international trade, positioning it as a critical focus in economic and financial research. Traditional…
Federated Learning Clients Clustering with Adaptation to Data Drifts
Minghao Li, Dmitrii Avdiukhin, Rana Shahout +3
Federated Learning (FL) trains deep models across edge devices without centralizing raw data, preserving user privacy. However, client heterogeneity slows down convergence and limi…
Towards Unsupervised Validation of Anomaly-Detection Models
Lihi Idan
Unsupervised validation of anomaly-detection models is a highly challenging task. While the common practices for model validation involve a labeled validation set, such validation…
Generative Learning of the Solution of Parametric Partial Differential Equations Using Guided Diffusion Models and Virtual Observations
Han Gao, Sebastian Kaltenbach, Petros Koumoutsakos
We introduce a generative learning framework to model high-dimensional parametric systems using gradient guidance and virtual observations. We consider systems described by Partial…
Beyond Throughput and Compression Ratios: Towards High End-to-end Utility of Gradient Compression
Wenchen Han, Shay Vargaftik, Michael Mitzenmacher +2
Gradient aggregation has long been identified as a major bottleneck in today's large-scale distributed machine learning training systems. One promising solution to mitigate such bo…
Coding schemes in neural networks learning classification tasks
Alexander van Meegen, Haim Sompolinsky
Neural networks posses the crucial ability to generate meaningful representations of task-dependent features. Indeed, with appropriate scaling, supervised learning in neural networ…
G-Transformer: Counterfactual Outcome Prediction under Dynamic and Time-varying Treatment Regimes
Hong Xiong, Feng Wu, Leon Deng +2
In the context of medical decision making, counterfactual prediction enables clinicians to predict treatment outcomes of interest under alternative courses of therapeutic actions g…
DeepMachining: Online Prediction of Machining Errors of Lathe Machines
Xiang-Li Lu, Hwai-Jung Hsu, Che-Wei Chou +3
We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on…
CADGL: Context-Aware Deep Graph Learning for Predicting Drug-Drug Interactions
Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam +2
Examining Drug-Drug Interactions (DDIs) is a pivotal element in the process of drug development. DDIs occur when one drug's properties are affected by the inclusion of other drugs.…