Balaji Lakshminarayanan
I'm a research scientist in Google Brain. My recent research is focused on probabilistic deep learning, specifically, uncertainty estimation, out-of-distribution robustness and applications. Before joining Google Brain, I was a research scientist at DeepMind. I received my PhD from the Gatsby Unit, University College London where I worked with Yee Whye Teh. Please see my webpage for more info: http://www.gatsby.ucl.ac.uk/~balaji/
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Morse Neural Networks for Uncertainty Quantification
Clara Huiyi Hu
ICML 2023 Workshop on Structured Probabilistic Inference & Generative Modeling (2023)
Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models
Yunhao Ge
Jiaping Zhao
Laurent Itti
Knowledge and Logical Reasoning workshop @ ICML (2023)
Pushing the Accuracy-Group Robustness Tradeoff Frontier with Introspective Self-play
Dj Dvijotham
Jihyeon Lee
Martin Strobel
Quan Yuan
ICLR'23 (2023) (to appear)
Plex: Towards Reliability using Pretrained Large Model Extensions
Du Phan
Mark Patrick Collier
Zi Wang
Zelda Mariet
Clara Huiyi Hu
Neil Band
Tim G. J. Rudner
Karan Singhal
Joost van Amersfoort
Andreas Christian Kirsch
Rodolphe Jenatton
Honglin Yuan
Kelly Buchanan
D. Sculley
Yarin Gal
ICML 2022 Pre-training Workshop (2022)
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Shreyas Padhy
Zi Lin
Yeming Wen
Ghassen Jerfel
Journal of Machine Learning Research (2022)
Density of States Estimation for Out of Distribution Detection
Alex Alemi
Cusuh Suh Ham
Josh Dillon
Warren Morningstar
AISTATS (2021)
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions
Aaron Loh
Basil Mustafa
Nick Pawlowski
Jan Freyberg
Zach William Beaver
Nam Vo
Peggy Bui
Samantha Winter
Patricia MacWilliams
Umesh Telang
Taylan Cemgil
Jim Winkens
Medical Imaging Analysis (2021)
Training independent subnetworks for robust prediction
Marton Havasi
Rodolphe Jenatton
Stanislav Fort
International Conference on Learning Representations (2021)
Combining Ensembles and Data Augmentation Can Harm Your Calibration
Yeming Wen
Ghassen Jerfel
Rafael Rios Müller
International Conference on Learning Representations (2021)
Soft Calibration Objectives for Neural Networks
Archit Karandikar
Nick Cain
Jon Shlens
Michael C. Mozer
Becca Roelofs
Advances in Neural Information Processing Systems (NeurIPS) (2021)
