- Orthogonal random features
accepted as full oral presentation at NIPS 2016 [arXiv
- Distributed mean estimation with limited communication [arXiv
- Federated Learning: strategies for improving communication efficiency [arXiv
- Learning battery consumption of mobile devices [pdf
Co-organized workshop and conference:
- NIPS 2015 Workshop
Learning and Privacy with Incomplete Data and Weak Supervision.
- International Conference on Multimedia Retrieval (ICMR) 2016
I defended my PhD, and joined Google Research in June 2015.
Recently accepted papers:
- NIPS 2015 (Spotlight): Spherical Random Features for Polynomial Kernels
- ICCV 2015: An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections
- ICCV 2015: Fast Orthogonal Projection Based on Kronecker Product
Recent ongoing works available on arXiv:
: Compact Nonlinear Maps and Circulant Extensions
: Deep Transfer Network: Unsupervised Domain Adaptation
: Fast Neural Networks with Circulant Projections
: On Learning from Label Proportions (v2)
Photo credit: Eileen Barroso
Felix X. Yu is currently working on bleeding edge machine learning technologies at Google Research, New York. He received his Ph.D from
Dept. of Electrical Engineering, Columbia University, in 2015, and his B.S. from
Dept. of Electronic Engineering, Tsinghua University, China, in 2010.
Yu is interested in large-scale machine learning with structured matrices, learning with weak supervisions, and their applications in computer vision.
His works have been recognized by the IBM PhD Fellowship Award (2014-2015),
the Facebook PhD Fellowship Finalist Award (2014),
the ACM Multimedia Best Paper Award (2011),
and the ACM ICMR Best Paper Award (2014).
Fast deep neural network/ binary embedding/ kernel approximation (ICML14, ICCV15, NIPS15, NIPS16)
Learning from label proportions and weakly supervised learning (ICML13, CVPR14, MM14)
Attribute-based visual recognition (CVPR12, 13, 14, MM14)
Complex video event analysis (CVPR14, ICMR14)
Novel applications on geolocation, and mobile devices (MM11)