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Photo of Ron Meir
Learning causal estimators from unlabeled data

Models of real-world phenomena, e.g., human physiology, offer significant utility in health and disease.
However, they often suffer from misspecification. To understand the implications of such misspecification, we develop some basic theory for the simple setting of linear models, aiming to understand the benefit of the ubiquitously available unlabelled offline data in enhancing misspecified causal models. We implement these ideas on non-linear models, focusing on the cardiovascular system, where an abundance of unlabelled data and (partial) physiological models are available.

Curriculum learning agents

The effectiveness of learning systems depends on both the attributes of the learner and the teacher. Indeed, an optimal setup for learning is when the student and teacher/environment operate collaboratively to enhance learning, where the teacher’s task is to develop an appropriate learning curriculum that facilitates learning by the student. We develop approaches to enhance agents’ learning within a curriculum setting, focusing on the model-based Reinforcement Learning agents and continuous control settings.

Lifelong learning agents

Effective learning from data requires prior assumptions, referred to as inductive bias. A fundamental question pertains to the source of a ‘good’ inductive bias. One natural way to form such a bias is through lifelong learning, where an agent continually interacts with the world through a sequence of tasks, aiming to improve its performance on future tasks based on the tasks it has encountered so far. We develop a theoretical framework for incremental inductive bias formation, and demonstrate its effectiveness in problems of sequential learning and decision making.