Search Results for author: William Knottenbelt

Found 13 papers, 5 papers with code

CoxKAN: Kolmogorov-Arnold Networks for Interpretable, High-Performance Survival Analysis

1 code implementation6 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.

feature selection Kolmogorov-Arnold Networks +1

Decoding SEC Actions: Enforcement Trends through Analyzing Blockchain litigation using LLM-based Thematic Factor Mapping

no code implementations21 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.

Decision Making Navigate

Offline Model-Based Reinforcement Learning with Anti-Exploration

no code implementations20 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.

D4RL Model-based Reinforcement Learning +4

Offline Reinforcement Learning with Behavioral Supervisor Tuning

no code implementations25 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.

Offline RL reinforcement-learning +2

Market Misconduct in Decentralized Finance (DeFi): Analysis, Regulatory Challenges and Policy Implications

no code implementations29 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.

zkFL: Zero-Knowledge Proof-based Gradient Aggregation for Federated Learning

no code implementations4 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.

Federated Learning

Defending Against Poisoning Attacks in Federated Learning with Blockchain

no code implementations2 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.

Federated Learning

Neural NILM: Deep Neural Networks Applied to Energy Disaggregation

4 code implementations23 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

Demo Abstract: NILMTK v0.2: A Non-intrusive Load Monitoring Toolkit for Large Scale Data Sets

2 code implementations20 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

NILMTK: An Open Source Toolkit for Non-intrusive Load Monitoring

2 code implementations15 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

Metadata for Energy Disaggregation

2 code implementations24 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

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