6 papers
Learning Optimal and Sample-Efficient Decision Policies with Guarantees
Daqian Shao
The paradigm of decision-making has been revolutionised by reinforcement learning and deep learning. Although this has led to significant progress in domains such as robotics, heal…
Double Machine Learning for Conditional Moment Restrictions: IV Regression, Proximal Causal Learning and Beyond
Daqian Shao, Ashkan Soleymani, Francesco Quinzan +1
Solving conditional moment restrictions (CMRs) is a key problem considered in statistics, causal inference, and econometrics, where the aim is to solve for a function of interest t…
Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
Daqian Shao, Thomas Kleine Buening, Marta Kwiatkowska
We propose a general framework for causal Imitation Learning (IL) with hidden confounders, which subsumes several existing settings. Our framework accounts for two types of hidden…
Learning Decision Policies with Instrumental Variables through Double Machine Learning
Daqian Shao, Ashkan Soleymani, Francesco Quinzan +1
A common issue in learning decision-making policies in data-rich settings is spurious correlations in the offline dataset, which can be caused by hidden confounders. Instrumental v…
STR-Cert: Robustness Certification for Deep Text Recognition on Deep Learning Pipelines and Vision Transformers
Daqian Shao, Lukas Fesser, Marta Kwiatkowska
Robustness certification, which aims to formally certify the predictions of neural networks against adversarial inputs, has become an integral part of important tool for safety-cri…
Sample Efficient Model-free Reinforcement Learning from LTL Specifications with Optimality Guarantees
Daqian Shao, Marta Kwiatkowska
Linear Temporal Logic (LTL) is widely used to specify high-level objectives for system policies, and it is highly desirable for autonomous systems to learn the optimal policy with…