报告人:Angelica Aviles-Rivero
报告地点:腾讯会议ID: 860611501
报告时间:2025年07月11日星期五13:30-14:30
邀请人:刘俊
报告摘要:
Solving inverse problems remains a fundamental challenge in computational imaging, often requiring large datasets, carefully tuned regularisation, or extensive supervision. Yet in many real-world scenarios, such resources are unavailable — we may only have a single noisy observation and no access to similar examples. In this talk, we will discuss how we can still meaningfully approach inverse problems under such constraints, by leveraging single-instance priors — structural biases learned from the data point itself.
We will explore the limitations of conventional deep learning pipelines, including their dependence on large-scale training and vulnerability to overfitting in low-data regimes. Then, we will introduce a line of recent work showing that, with the right optimisation and structural strategies, one can build single-instance priors — enabling stable and effective reconstructions even in severely underdetermined settings.
This talk will walk through our journey in rethinking priors: moving from generic plug-and-play formulations to formulations that exploit both spatial and frequency structures in data. The results offer not only practical solutions for data-scarce settings, but also new theoretical insights into how learning and regularisation can be reframed when we have almost no data to learn from.

主讲人简介:
Angelica Aviles-Rivero is an Assistant Professor at the Yau Mathematical Sciences Center, Tsinghua University. Previously, she was a Senior Research Associate at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge. She is a member of ELLIS. Her research lies at the intersection of applied mathematics and machine learning, focusing on developing data-driven algorithmic techniques that enable computers to extract high-level understanding from vast datasets. Her research has been highlighted, including receiving an Outstanding Paper Award at ICML 2020. She was elected as an officer for the SIAM SIAG/IS secretary position for the term 2023. For more information visit: https://angelicaiaviles.wordpress.com/