Jiachang Liu

Orcid: 0000-0002-8786-4885

Affiliations:
  • Duke University, Department of Electrical and Computer Engineering, Durham, NC, USA


According to our database1, Jiachang Liu authored at least 12 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2024
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models.
CoRR, 2024

Amazing Things Come From Having Many Good Models.
CoRR, 2024

Position: Amazing Things Come From Having Many Good Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023
Representing Graphs via Gromov-Wasserstein Factorization.
IEEE Trans. Pattern Anal. Mach. Intell., 2023

Fast and Interpretable Mortality Risk Scores for Critical Care Patients.
CoRR, 2023

OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems.
CoRR, 2023

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

OKRidge: Scalable Optimal k-Sparse Ridge Regression.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
FasterRisk: Fast and Accurate Interpretable Risk Scores.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Fast Sparse Classification for Generalized Linear and Additive Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

What Makes Good In-Context Examples for GPT-3?
Proceedings of Deep Learning Inside Out: The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, 2022

2020
CLUB: A Contrastive Log-ratio Upper Bound of Mutual Information.
Proceedings of the 37th International Conference on Machine Learning, 2020


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