1 code implementation • NeurIPS 2019 • Daniel Kumor, Bryant Chen, Elias Bareinboim
Building on the literature of instrumental variables (IVs), a plethora of methods has been developed to identify causal effects in linear systems.
no code implementations • 27 May 2019 • Adarsh Subbaswamy, Bryant Chen, Suchi Saria
Recent interest in the external validity of prediction models (i. e., the problem of different train and test distributions, known as dataset shift) has produced many methods for finding predictive distributions that are invariant to dataset shifts and can be used for prediction in new, unseen environments.
1 code implementation • 9 Nov 2018 • Bryant Chen, Wilka Carvalho, Nathalie Baracaldo, Heiko Ludwig, Benjamin Edwards, Taesung Lee, Ian Molloy, Biplav Srivastava
While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing concern.
5 code implementations • 3 Jul 2018 • Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, Ben Edwards
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
no code implementations • ICML 2017 • Bryant Chen, Daniel Kumor, Elias Bareinboim
In this paper, we provide an algorithm for the identification of causal parameters in linear structural models that subsumes previous state-of-the-art methods.
no code implementations • NeurIPS 2016 • Bryant Chen
In this paper, we address the problems of identifying linear structural equation models and discovering the constraints they imply.
no code implementations • 10 Nov 2015 • Bryant Chen, Judea Pearl, Elias Bareinboim
This cancellation allows the auxiliary variables to help conventional methods of identification (e. g., single-door criterion, instrumental variables, half-trek criterion), as well as model testing (e. g., d-separation, over-identification).
no code implementations • 7 Aug 2015 • Bryant Chen
In this paper, we address the problem of identifying linear structural equation models.