Lesia Semenova

According to our database, Lesia Semenova authored at least 9 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Amazing Things Come From Having Many Good Models.

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

CoRR, 2024

Position: Amazing Things Come From Having Many Good Models.

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges.

Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, M. Brandon Westover

CoRR, 2023

Fast and Interpretable Mortality Risk Scores for Critical Care Patients.

Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin

CoRR, 2023

A Path to Simpler Models Starts With Noise.

Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

On the Existence of Simpler Machine Learning Models.

Lesia Semenova, Cynthia Rudin, Ronald Parr

Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

2021

Multitask Learning for Citation Purpose Classification.

Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin

CoRR, 2021

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges.

Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong

CoRR, 2021

2019

A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning.

Lesia Semenova, Cynthia Rudin

CoRR, 2019