1 code implementation • 17 Apr 2024 • Changbin Li, Kangshuo Li, Yuzhe Ou, Lance M. Kaplan, Audun Jøsang, Jin-Hee Cho, Dong Hyun Jeong, Feng Chen
In this paper, we propose a novel framework called Hyper-Evidential Neural Network (HENN) that explicitly models predictive uncertainty due to composite class labels in training data in the context of the belief theory called Subjective Logic (SL).
1 code implementation • 21 Feb 2024 • Ho Lyun Jeong, Ziqi Wang, Colin Samplawski, Jason Wu, Shiwei Fang, Lance M. Kaplan, Deepak Ganesan, Benjamin Marlin, Mani Srivastava
Constantly locating moving objects, i. e., geospatial tracking, is essential for autonomous building infrastructure.
no code implementations • 19 Oct 2023 • Cai Davies, Marc Roig Vilamala, Alun D. Preece, Federico Cerutti, Lance M. Kaplan, Supriyo Chakraborty
In this paper, we empirically investigate the correlations between misclassification and evaluated uncertainty, and show that EDL's `evidential signal' is due to misclassification bias.
no code implementations • 19 Feb 2023 • Zhen Guo, Qi Zhang, Xinwei An, Qisheng Zhang, Audun Jøsang, Lance M. Kaplan, Feng Chen, Dong H. Jeong, Jin-Hee Cho
Distinguishing the types of fake news spreaders based on their intent is critical because it will effectively guide how to intervene to mitigate the spread of fake news with different approaches.
no code implementations • 13 Dec 2022 • Qisheng Zhang, Zhen Guo, Audun Jøsang, Lance M. Kaplan, Feng Chen, Dong H. Jeong, Jin-Hee Cho
Proximal Policy Optimization (PPO) is a highly popular policy-based deep reinforcement learning (DRL) approach.
no code implementations • 23 Aug 2022 • Pietro Baroni, Federico Cerutti, Massimiliano Giacomin, Lance M. Kaplan, Murat Sensoy
The sixth assessment of the international panel on climate change (IPCC) states that "cumulative net CO2 emissions over the last decade (2010-2019) are about the same size as the 11 remaining carbon budget likely to limit warming to 1. 5C (medium confidence)."
no code implementations • 16 Aug 2022 • Conrad D. Hougen, Lance M. Kaplan, Magdalena Ivanovska, Federico Cerutti, Kumar Vijay Mishra, Alfred O. Hero III
In second-order uncertain Bayesian networks, the conditional probabilities are only known within distributions, i. e., probabilities over probabilities.
no code implementations • 8 Aug 2022 • Conrad D. Hougen, Lance M. Kaplan, Federico Cerutti, Alfred O. Hero III
When the historical data are limited, the conditional probabilities associated with the nodes of Bayesian networks are uncertain and can be empirically estimated.
no code implementations • 12 Jun 2022 • Zhen Guo, Zelin Wan, Qisheng Zhang, Xujiang Zhao, Feng Chen, Jin-Hee Cho, Qi Zhang, Lance M. Kaplan, Dong H. Jeong, Audun Jøsang
We found that only a few studies have leveraged the mature uncertainty research in belief/evidence theories in ML/DL to tackle complex problems under different types of uncertainty.
1 code implementation • 22 Feb 2021 • Federico Cerutti, Lance M. Kaplan, Angelika Kimmig, Murat Sensoy
When collaborating with an AI system, we need to assess when to trust its recommendations.
no code implementations • 15 Aug 2019 • Mandana Saebi, Giovanni Luca Ciampaglia, Lance M. Kaplan, Nitesh V. Chawla
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and as a result, has enjoyed considerable success in recent years.
1 code implementation • 27 Dec 2017 • Jian Xu, Mandana Saebi, Bruno Ribeiro, Lance M. Kaplan, Nitesh V. Chawla
A major branch of anomaly detection methods relies on dynamic networks: raw sequence data is first converted to a series of networks, then critical change points are identified in the evolving network structure.
Social and Information Networks Physics and Society
no code implementations • 13 Mar 2017 • Meng Jiang, Jingbo Shang, Taylor Cassidy, Xiang Ren, Lance M. Kaplan, Timothy P. Hanratty, Jiawei Han
We propose an efficient framework, called MetaPAD, which discovers meta patterns from massive corpora with three techniques: (1) it develops a context-aware segmentation method to carefully determine the boundaries of patterns with a learnt pattern quality assessment function, which avoids costly dependency parsing and generates high-quality patterns; (2) it identifies and groups synonymous meta patterns from multiple facets---their types, contexts, and extractions; and (3) it examines type distributions of entities in the instances extracted by each group of patterns, and looks for appropriate type levels to make discovered patterns precise.
1 code implementation • 31 Oct 2016 • Jingbo Shang, Meng Qu, Jialu Liu, Lance M. Kaplan, Jiawei Han, Jian Peng
It models vertices as low-dimensional vectors to explore network structure-embedded similarity.