Project description
Learning a new way to develop statistical learning systems
Statistical learning algorithms, natural language processing and conversational AI are all possible today thanks to advances in the field of machine learning. However, the question of generalisation remains one of the greatest unsolved mysteries of modern computer science: why do these immensely complex prediction rules successfully apply to future unseen instances? To answer this question, the ERC-funded OPTGEN project will focus on statistical and reinforcement learning, and the de facto contemporary standard optimisation algorithms for training learning models. The OPTGEN methodology points to inherent shortcomings of widely accepted viewpoints with regard to the generalisation theory of optimisation-based learning algorithms. The project is expected to transform how we view and develop practical, efficient and reliable learning systems.
Objective
Recent advances in the field of machine learning (ML) are revolutionizing an ever-growing variety of domains, ranging from statistical learning algorithms in computer vision and natural language processing all the way to reinforcement learning algorithms in autonomous driving and conversational AI. However, many of these breakthroughs demonstrate phenomena that lack explanations, and sometimes even contradict conventional wisdom. Perhaps the greatest mystery of modern ML---and arguably, one of the greatest mysteries of all of modern computer science---is the question of generalization: why do these immensely complex prediction rules successfully apply to future unseen instances? Apart from the pure scientific curiosity it stimulates, I believe that this lack of understanding poses a significant obstacle to widening the applicability of ML to critical applications, like in healthcare or autonomous driving, where the cost of error is disastrous. The broad goal of this project is to tackle the generalization mystery in the context of both statistical learning and reinforcement learning, focusing on optimization algorithms being the de facto contemporary standard in training learning models. Our methodology points out to inherent shortcomings of widely accepted viewpoints with regard to generalization of optimization-based learning algorithms, and takes a crucially different approach that targets the optimization algorithm itself; building bottom-up from fundamental and tractable optimization models, we identify intrinsic properties and develop algorithmic methodologies that enable optimization to effectively generalize in modern statistical- and reinforcement-learning scenarios. A successful outcome would not only lead to a timely and crucial shift in the way the research community approaches generalization of contemporary optimization-based ML, but it may also significantly transform the way we develop practical, efficient and reliable learning systems.
Keywords
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Topic(s)
Funding Scheme
HORIZON-ERC - HORIZON ERC GrantsHost institution
69978 Tel Aviv
Israel