In much of our work, we incorporate causal thinking into high-dimensional, messy machine learning problem formulations, with the goal of developing practical machine learning algorithms that can guide decisions and handling distribution shift.
Shantanu Gupta,
David Childers,
Zachary Lipton
Causal Learning and Reasoning (CLeaR)
Shantanu Gupta,
Zachary Lipton,
David Childers
Advances in Neural Information Processing Systems (NeurIPS)
Audrey Huang,
Liu Leqi,
Zachary Lipton,
Kamyar Azizzadenesheli
Advances in Neural Information Processing Systems (NeurIPS)
Cheng Cheng*,
Helen Zhou*,
Jeremy Weiss,
Zachary Lipton
American Medical Informatics Association (AMIA) Annual Symposium
Shantanu Gupta,
Zachary Lipton,
David Childers
Uncertainty in Artificial Intelligence (UAI)
Saurabh Garg,
Yifan Wu,
Sivaraman Balakrishnan,
Zachary Lipton
Advances in Neural Information Processing Systems (NeurIPS)
Sina Fazelpour,
Zachary Lipton
AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES)
Divyansh Kaushik,
Zachary Lipton
International Conference on Learning Representations (ICLR)
Kyra Gan,
Andrew Li,
Zachary Lipton,
Sridhar Tayur
NeurIPS Causality Workshop
Zachary Lipton*,
Yu-Xiang Wang*,
Alex Smola
International Conference on Machine Learning (ICML)