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ORCIDLucile Ter-Minassian
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2020 – today
- 2024
[i6]Lucile Ter-Minassian, Liran Szlak, Ehud Karavani, Chris C. Holmes, Yishai Shimoni:
Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation. CoRR abs/2401.17737 (2024)- [i5]Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz, Chris C. Holmes:
Explainable AI for survival analysis: a median-SHAP approach. CoRR abs/2402.00072 (2024) - [i4]Nicholas Pochinkov, Angelo Benoit, Lovkush Agarwal, Zainab Ali Majid, Lucile Ter-Minassian:
Extracting Paragraphs from LLM Token Activations. CoRR abs/2409.06328 (2024) - [i3]Jake Fawkes, Lucile Ter-Minassian, Desi Ivanova, Uri Shalit, Christopher C. Holmes:
Is merging worth it? Securely evaluating the information gain for causal dataset acquisition. CoRR abs/2409.07215 (2024) - 2023
- [c2]Lucile Ter-Minassian, Oscar Clivio, Karla DiazOrdaz, Robin J. Evans, Christopher C. Holmes:
PWSHAP: A Path-Wise Explanation Model for Targeted Variables. ICML 2023: 34054-34089 - [i2]Lucile Ter-Minassian, Oscar Clivio, Karla Diaz-Ordaz, Robin J. Evans, Chris C. Holmes:
PWSHAP: A Path-Wise Explanation Model for Targeted Variables. CoRR abs/2306.14672 (2023) - 2021
- [c1]Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla DiazOrdaz, Chris C. Holmes:
On Locality of Local Explanation Models. NeurIPS 2021: 18395-18407 - [i1]Sahra Ghalebikesabi, Lucile Ter-Minassian, Karla Diaz-Ordaz, Chris C. Holmes:
On Locality of Local Explanation Models. CoRR abs/2106.14648 (2021)
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