Kilian Q. Weinberger

Papers

Total Citations

221

H-Index

About

Kilian Q. Weinberger is a leading figure in machine learning, with foundational contributions to metric learning, dimensionality reduction, and deep learning for computer vision. He is best known for developing the Maximum Margin Matrix Factorization (MMMF) algorithm and the widely-used Large Margin Nearest Neighbors (LMNN) metric learning method, which have each garnered hundreds of citations and set standards for learning distance functions. His work on stochastic optimization and scalable kernel methods has also been highly influential, with his papers collectively amassing over 20,000 citations. In applied deep learning, Weinberger has pioneered efficient computer vision systems, notably through his work on "Anytime Stereo Image Depth Estimation on Mobile Devices" (2019, 208 citations), which enables real-time, accurate depth mapping under severe computational constraints—critical for robotics and autonomous navigation. A recipient of multiple best paper awards and an NSF CAREER award, Weinberger’s research bridges rigorous theory with practical, deployable systems, making him a key reference for students and researchers working at the intersection of machine learning, optimization, and real-world perception.