112 citations · 148 across the 8 of their papers we have counts for
8 papers
Incorporating Experts' Judgment into Machine Learning Models
Hogun Park, Aly Megahed, Peifeng Yin +3
Machine learning (ML) models have been quite successful in predicting outcomes in many applications. However, in some cases, domain experts might have a judgment about the expected…
Federated XGBoost on Sample-Wise Non-IID Data
Katelinh Jones, Yuya Jeremy Ong, Yi Zhou +1
Federated Learning (FL) is a paradigm for jointly training machine learning algorithms in a decentralized manner which allows for parties to communicate with an aggregator to creat…
SimPO: Simultaneous Prediction and Optimization
Bing Zhang, Yuya Jeremy Ong, Taiga Nakamura
Many machine learning (ML) models are integrated within the context of a larger system as part of a key component for decision making processes. Concretely, predictive models are o…
Towards a New Science of Disinformation
Claudio S. Pinhanez, German H. Flores, Marisa A. Vasconcelos +4
How can we best address the dangerous impact that deep learning-generated fake audios, photographs, and videos (a.k.a. deepfakes) may have in personal and societal life? We foresee…
Predicting Loss Risks for B2B Tendering Processes
Eelaaf Zahid, Yuya Jeremy Ong, Aly Megahed +1
Sellers and executives who maintain a bidding pipeline of sales engagements with multiple clients for many opportunities significantly benefit from data-driven insight into the hea…
Adaptive Histogram-Based Gradient Boosted Trees for Federated Learning
Yuya Jeremy Ong, Yi Zhou, Nathalie Baracaldo +1
Federated Learning (FL) is an approach to collaboratively train a model across multiple parties without sharing data between parties or an aggregator. It is used both in the consum…
IBM Federated Learning: an Enterprise Framework White Paper V0.1
Heiko Ludwig, Nathalie Baracaldo, Gegi Thomas +21
Federated Learning (FL) is an approach to conduct machine learning without centralizing training data in a single place, for reasons of privacy, confidentiality or data volume. How…
Temporal Tensor Transformation Network for Multivariate Time Series Prediction
Yuya Jeremy Ong, Mu Qiao, Divyesh Jadav
Multivariate time series prediction has applications in a wide variety of domains and is considered to be a very challenging task, especially when the variables have correlations a…