1 code implementation • 6 Sep 2024 • William Knottenbelt, Zeyu Gao, Rebecca Wray, Woody Zhidong Zhang, Jiashuai Liu, Mireia Crispin-Ortuzar
Evaluation on the 9 real datasets show that CoxKAN consistently outperforms the Cox proportional hazards model and achieves performance that is superior or comparable to that of tuned MLPs.
no code implementations • 21 Aug 2024 • Junliang Luo, Xihan Xiong, William Knottenbelt, Xue Liu
This study focuses on U. S. litigation against blockchain entities, particularly by the U. S. Securities and Exchange Commission (SEC) given its influence on global crypto regulation.
no code implementations • 20 Aug 2024 • Padmanaba Srinivasan, William Knottenbelt
This is an especially promising paradigm in offline reinforcement learning (RL) where data may be limited in quantity, in addition to being deficient in coverage and quality.
no code implementations • 25 Apr 2024 • Padmanaba Srinivasan, William Knottenbelt
Offline reinforcement learning (RL) algorithms are applied to learn performant, well-generalizing policies when provided with a static dataset of interactions.
no code implementations • 29 Nov 2023 • Xihan Xiong, Zhipeng Wang, Tianxiang Cui, William Knottenbelt, Michael Huth
The rise of blockchain and Decentralized Finance (DeFi) underscores this intertwined evolution of technology and finance.
no code implementations • 28 Nov 2023 • Xihan Xiong, Zhipeng Wang, Xi Chen, William Knottenbelt, Michael Huth
While this iterative process enhances financial returns, it also introduces potential risks.
no code implementations • 4 Oct 2023 • Zhipeng Wang, Nanqing Dong, Jiahao Sun, William Knottenbelt, Yike Guo
Federated learning (FL) is a machine learning paradigm, which enables multiple and decentralized clients to collaboratively train a model under the orchestration of a central aggregator.
no code implementations • 2 Jul 2023 • Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Kampffmeyer, William Knottenbelt, Eric Xing
In the era of deep learning, federated learning (FL) presents a promising approach that allows multi-institutional data owners, or clients, to collaboratively train machine learning models without compromising data privacy.
no code implementations • 27 Oct 2021 • Pratha Khandelwal, Philip Nadler, Rossella Arcucci, William Knottenbelt, Yi-Ke Guo
The nature of available economic data has changed fundamentally in the last decade due to the economy's digitisation.
4 code implementations • 23 Jul 2015 • Jack Kelly, William Knottenbelt
Energy disaggregation estimates appliance-by-appliance electricity consumption from a single meter that measures the whole home's electricity demand.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +4
2 code implementations • 20 Sep 2014 • Jack Kelly, Nipun Batra, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava
In this demonstration, we present an open source toolkit for evaluating non-intrusive load monitoring research; a field which aims to disaggregate a household's total electricity consumption into individual appliances.
Other Computer Science
2 code implementations • 15 Apr 2014 • Nipun Batra, Jack Kelly, Oliver Parson, Haimonti Dutta, William Knottenbelt, Alex Rogers, Amarjeet Singh, Mani Srivastava
We demonstrate the range of reproducible analyses which are made possible by our toolkit, including the analysis of six publicly available data sets and the evaluation of both benchmark disaggregation algorithms across such data sets.
Applications
2 code implementations • 24 Mar 2014 • Jack Kelly, William Knottenbelt
Energy disaggregation is the process of estimating the energy consumed by individual electrical appliances given only a time series of the whole-home power demand.
Databases H.3