Bio
Limor started her doctoral studies at The Alan Turing Institute in October 2019. She is registered at the Computer Science Department at the University of Oxford. Limor completed her undergraduate degree in Computer Science at Harvard University, and an MSc in Social Data Science at the Oxford Internet Institute.
Research interests
Limor's doctoral research focused on various novel causally-inspired ML methods, most of which resulted in publications in top venues in the field.
Selected publications and papers
Papers/conferences:
- AISTATS 2020:
Gultchin, L., Kusner, M., Kanade, V. & Silva, R.. (2020). Differentiable Causal Backdoor Discovery. <i>Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics</i>, in <i>Proceedings of Machine Learning Research</i> 108:3970-3979 Available from https://proceedings.mlr.press/v108/gultchin20a.html
- ICML 2021:
Gultchin, L., Watson, D., Kusner, M. & Silva, R.. (2021). Operationalizing Complex Causes: A Pragmatic View of Mediation. <i>Proceedings of the 38th International Conference on Machine Learning</i>, in <i>Proceedings of Machine Learning Research</i> 139:3875-3885 Available from https://proceedings.mlr.press/v139/gultchin21a.html
Mastouri, A., Zhu, Y., Gultchin, L., Korba, A., Silva, R., Kusner, M., Gretton, A. & Muandet, K.. (2021). Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction. <i>Proceedings of the 38th International Conference on Machine Learning</i>, in <i>Proceedings of Machine Learning Research</i> 139:7512-7523 Available from https://proceedings.mlr.press/v139/mastouri21a.html
- UAI 2021:
Watson, D.S., Gultchin, L., Taly, A. & Floridi, L.. (2021). Local explanations via necessity and sufficiency: unifying theory and practice. <i>Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence</i>, in <i>Proceedings of Machine Learning Research</i> 161:1382-1392 Available from https://proceedings.mlr.press/v161/watson21a.html
- UAI 2022:
Zhu, Y., Gultchin, L., Gretton, A., Kusner, M.J. & Silva, R.. (2022). Causal inference with treatment measurement error: a nonparametric instrumental variable approach. <i>Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence</i>, in <i>Proceedings of Machine Learning Research</i> 180:2414-2424 Available from https://proceedings.mlr.press/v180/zhu22a.html
- UAI 2023:
Gultchin, L., Aglietti, V., Bellot, A. & Chiappa, S.. (2023). Functional Causal Bayesian Optimization. <i>Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence</i>, in <i>Proceedings of Machine Learning Research</i> (camera ready version to follow).
Workshops:
- Machine Learning Meets Econometrics (ICML 2021)
- Spurious Correlations, Invariance and Stability (ICML 2022)
- Spurious Correlations, Invariance and Stability (ICML 2023)