Grants and Contributions:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2022-2023)

Machine learning is widely used in science and technology with deployed tools like automatic spam classification for emails or face detectors in digital cameras. Yet today’s partial solutions to the fundamental problem of structured prediction , that is, learning to make multiple interrelated predictions (e.g. predicting the sequence of translated words for an input sentence in machine translation), lag far behind those available for binary classification in term of accuracy and scalability. Progress has been fragmented, and the theory is almost nonexistent, preventing the widespread adoption of the technology to new areas. The key challenge in structured prediction is the combinatorial explosion of choices for the output, requiring a radical new marriage of computation and statistics .

The objective of this research program is to elaborate a general theoretical and algorithmic framework for robust and efficient structured prediction . The goal is to bring structured prediction to a level of maturity and usability similar to that of binary classification in modern machine learning. I plan to attack this problem through the tools of statistical consistency of surrogate losses. Our theoretical work will provide the groundwork to build new robust structured prediction models that can handle weak supervision. Radically more efficient structured prediction machines will be obtained through our algorithmic work on advanced convex and combinatorial optimization. The applicability of the framework will be demonstrated through applications in computer vision, natural language processing and computational biology.

More specifically, my research program will address the following four scientific challenges:
1) Provide a unified theoretical analysis of structured prediction models.
2) Propose novel structured prediction models that enjoy good theoretical properties, are tractable, and address the particularities of the field such as weak supervision .
3) Design efficient algorithms that solve the underlying large-scale convex or non-convex optimization problems.
4) Demonstrate the applicability of the framework in several application areas.

Breakthrough progress on structured prediction will have high impact on statistical machine learning research, notably by providing a new solution to the open problem of making robust interrelated predictions. Moreover, the developed methodology will directly impact numerous application areas in science and technology by enabling the widespread adoption of advanced structured prediction.