"Recent Advances in Strongly Polynomial Algorithms for Linear Programming"

Abstract

Humans can effortlessly construct rich mental representations of the 3D world from sparse input, such as a single image. This is a core aspect of intelligence that helps us understand and interact with our surroundings and with each other. My research aims to build similar computational models–artificial intelligence methods that can perceive properties of the 3D structured world from images and videos. Despite remarkable progress in 2D computer vision, 3D perception remains an open problem due to some unique challenges, such as limited 3D training data and uncertainties in reconstruction. 

In this talk, I will discuss these challenges and explain how my research addresses them by posing vision as an inverse problem, and by designing machine learning models with physics-inspired inductive biases. I will demonstrate techniques for reconstructing 3D faces and objects, and for reasoning about uncertainties in scene reconstruction using generative models. I will then discuss how these efforts advance us toward scalable and generalizable visual perception and how they advance application domains such as robotics and computer graphics. 

Biography

Ayush Tewari is a postdoctoral researcher at MIT CSAIL with William Freeman, Vincent Sitzmann, and Joshua Tenenbaum. He previously completed his Ph.D. at the Max Planck Institute for Informatics, advised by Christian Theobalt. His research interests lie at the intersection of computer vision, computer graphics, and machine learning, focusing on 3D perception and its applications. Ayush was awarded the Otto Hahn medal from the Max Planck Society for his scientific contributions as a Ph.D. student.

Research Interests/Area

Computer vision, 3D perception, generative models, neural rendering

"Understanding Deep Learning Through Phenomena Discovery and Explanation"

Abstract

Deep learning has achieved great success in many applications. However, the success of deep learning has not been well understood in theory. In this talk, I will discuss some recent efforts to bridge the gap between theory and practice through phenomenon discovery and explanation. In the first part of this talk, I will discuss the phenomenon of “benign overfitting” in deep learning, and present our recent results characterizing benign and harmful overfitting in training convolutional neural networks (CNNs). In the second part of the talk, I will discuss the recently discovered phenomenon on the generalization gap between Adam and stochastic gradient descent in image classification tasks. I will present an intuitive explanation for this generalization gap and provide a rigorous theoretical guarantee to support the explanation. Overall, this talk will provide insights into the “feature learning” procedure of neural networks, and how it is related to various interesting phenomena in deep learning.

 
 Biography

Yuan Cao is an assistant professor in the Department of Statistics and Actuarial Science and Department of Mathematics at the University of Hong Kong. Before joining HKU, he was a postdoctoral scholar at UCLA. He received his B.S. from Fudan University and Ph.D. from Princeton University. Yuan’s research interests include deep learning theory, non-convex optimization, and high-dimensional statistics. He has published research papers in top machine learning journals (JMLR, ML, TMLR) and conferences (NeurIPS, ICML, ICLR, COLT, AAAI, IJCAI, etc), including a spotlight presentation in NeurIPS 2019, a long talk in ICML 2021, and an oral presentation in NeurIPS 2022. 

Research Interests/Area

deep learning theory, non-convex optimization, high-dimensional statistics

"Paths to AI Accountability"

Abstract

We have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in the age of AI. In this talk, I will discuss the two main components of AI accountability, then illustrate them through a case study on social media. Within the context of social media, I will focus on how social media platforms filter (or curate) the content that users see. I will review several methods for auditing social media, drawing from concepts and tools in hypothesis testing, causal inference, and LLMs.

 
 Biography

Sarah is a final-year PhD student at MIT in the Electrical Engineering and Computer Science Department advised by Professor Aleksander Mądry and Professor Devavrat Shah. Sarah utilizes methods from machine learning, statistical inference, causal inference, and game theory to study responsible computing and AI policy. Previously, she has written about social media, trustworthy algorithms, algorithmic fairness, and more. She is currently interested in AI auditing, AI supply chains, and IP Law x Gen AI.

Research Interests/Area

Responsible AI, ethics in AI, AI policy + statistical inference, causal inference

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