145 citations · 433 across the 14 of their papers we have counts for
15 papers
MentalChat16K: A Benchmark Dataset for Conversational Mental Health Assistance
Jia Xu, Tianyi Wei, Bojian Hou +7
We introduce MentalChat16K, an English benchmark dataset combining a synthetic mental health counseling dataset and a dataset of anonymized transcripts from interventions between B…
Clustering Alzheimer's Disease Subtypes via Similarity Learning and Graph Diffusion
Tianyi Wei, Shu Yang, Davoud Ataee Tarzanagh +6
Alzheimer's disease (AD) is a complex neurodegenerative disorder that affects millions of people worldwide. Due to the heterogeneous nature of AD, its diagnosis and treatment pose…
Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond
Zack Goldblum, Zhongchuan Xu, Haoer Shi +6
The exponential growth of neuroscientific data necessitates platforms that facilitate data management and multidisciplinary collaboration. In this paper, we introduce Pennsieve - a…
Benchmarking AutoML algorithms on a collection of synthetic classification problems
Pedro Henrique Ribeiro, Patryk Orzechowski, Joost Wagenaar +1
Automated machine learning (AutoML) algorithms have grown in popularity due to their high performance and flexibility to adapt to different problems and data sets. With the increas…
Applying Autonomous Hybrid Agent-based Computing to Difficult Optimization Problems
Mateusz Godzik, Jacek Dajda, Marek Kisiel-Dorohinicki +5
Evolutionary multi-agent systems (EMASs) are very good at dealing with difficult, multi-dimensional problems, their efficacy was proven theoretically based on analysis of the relev…
Contemporary Symbolic Regression Methods and their Relative Performance
William La Cava, Patryk Orzechowski, Bogdan Burlacu +5
Many promising approaches to symbolic regression have been presented in recent years, yet progress in the field continues to suffer from a lack of uniform, robust, and transparent…
Generative and reproducible benchmarks for comprehensive evaluation of machine learning classifiers
Patryk Orzechowski, Jason H. Moore
Understanding the strengths and weaknesses of machine learning (ML) algorithms is crucial for determine their scope of application. Here, we introduce the DIverse and GENerative ML…
EBIC.JL -- an Efficient Implementation of Evolutionary Biclustering Algorithm in Julia
Paweł Renc, Patryk Orzechowski, Aleksander Byrski +2
Biclustering is a data mining technique which searches for local patterns in numeric tabular data with main application in bioinformatics. This technique has shown promise in multi…
Benchmarking in Optimization: Best Practice and Open Issues
Thomas Bartz-Beielstein, Carola Doerr, Daan van den Berg +14
This survey compiles ideas and recommendations from more than a dozen researchers with different backgrounds and from different institutes around the world. Promoting best practice…
EBIC: an open source software for high-dimensional and big data biclustering analyses
Patryk Orzechowski, Jason H. Moore
Motivation: In this paper we present the latest release of EBIC, a next-generation biclustering algorithm for mining genetic data. The major contribution of this paper is adding su…
Where are we now? A large benchmark study of recent symbolic regression methods
Patryk Orzechowski, William La Cava, Jason H. Moore
In this paper we provide a broad benchmarking of recent genetic programming approaches to symbolic regression in the context of state of the art machine learning approaches. We use…
EBIC: an evolutionary-based parallel biclustering algorithm for pattern discover
Patryk Orzechowski, Moshe Sipper, Xiuzhen Huang +1
In this paper a novel biclustering algorithm based on artificial intelligence (AI) is introduced. The method called EBIC aims to detect biologically meaningful, order-preserving pa…
Considerations of automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure
Alena Orlenko, Jason H. Moore, Patryk Orzechowski +6
With the maturation of metabolomics science and proliferation of biobanks, clinical metabolic profiling is an increasingly opportunistic frontier for advancing translational clinic…
A System for Accessible Artificial Intelligence
Randal S. Olson, Moshe Sipper, William La Cava +7
While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowled…
PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison
Randal S. Olson, William La Cava, Patryk Orzechowski +2
The selection, development, or comparison of machine learning methods in data mining can be a difficult task based on the target problem and goals of a particular study. Numerous p…