Lesia Semenova
Lesia Semenova
Ph.D. Candidate in Computer Science, Duke University
Verifierad e-postadress på cs.duke.edu - Startsida
Citeras av
Citeras av
Interpretable machine learning: Fundamental principles and 10 grand challenges
C Rudin, C Chen, Z Chen, H Huang, L Semenova, C Zhong
Statistic Surveys 16, 1-85, 2022
On the existence of simpler machine learning models
L Semenova, C Rudin, R Parr
Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022
A Path to Simpler Models Starts With Noise
L Semenova, H Chen, R Parr, C Rudin
Advances in Neural Information Processing Systems 36, 2024
Multitask learning for citation purpose classification
A Oesterling, A Ghosal, H Yu, R Xin, Y Baig, L Semenova, C Rudin
Proceedings of the Second Workshop on Scholarly Document Processing, 134–139, 2021
Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People With HIV on Suppressive Antiretroviral Therapy
SD Falcinelli, A Volkheimer, L Semenova, E Wu, A Richardson, ...
The Journal of Infectious Disease, 2023
Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms
ST Goh, L Semenova, C Rudin
INFORMS Journal on Data Science, 2024
Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.
L Semenova, Y Wang, SD Falcinelli, N Archin, AD Cooper-Volkheimer, ...
bioRxiv, 2023.11. 16.567386, 2023
In Pursuit of Simplicity: The Role of the Rashomon Effect for Informed Decision Making
L Semenova
Duke University, 2024
ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges
D Tang, F Willard, R Tegerdine, L Triplett, J Donnelly, L Moffett, ...
arXiv preprint arXiv:2312.10056, 2023
Fast and Interpretable Mortality Risk Scores for Critical Care Patients
CQ Zhu, M Tian, L Semenova, J Liu, J Xu, J Scarpa, C Rudin
arXiv preprint arXiv:2311.13015, 2023
Moving towards a more equal world, one ride at a time: Studying Public Transportation Initiatives using interpretable causal inference
GR Parikh, A Sun, J Huang, L Semenova, C Rudin
NeurIPS 2022 Workshop on Causality for Real-world Impact, 2022
Amazing Things Come From Having Many Good Models
C Rudin, C Zhong, L Semenova, M Seltzer, R Parr, J Liu, S Katta, ...
Transition Noise Facilitates Interpretability
R Parr, C Rudin, H Chen, Z Boner, M Moshkovitz, L Semenova
Workshop on Interpretable Policies in Reinforcement Learning@ RLC-2024, 0
Systemet kan inte utföra åtgärden just nu. Försök igen senare.
Artiklar 1–13