Balaji Lakshminarayanan
Balaji Lakshminarayanan
Staff Research Scientist at Google Brain
Verified email at - Homepage
Cited by
Cited by
Simple and scalable predictive uncertainty estimation using deep ensembles
B Lakshminarayanan, A Pritzel, C Blundell
Advances in Neural Information Processing Systems, 6393-6395, 2017
Clinically applicable deep learning for diagnosis and referral in retinal disease
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
Nature medicine 24 (9), 1342-1350, 2018
Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
F Briggs, B Lakshminarayanan, L Neal, XZ Fern, R Raich, SJK Hadley, ...
The Journal of the Acoustical Society of America 131 (6), 4640-4650, 2012
Learning in Implicit Generative Models
S Mohamed, B Lakshminarayanan
arXiv preprint arXiv:1610.03483, 2016
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Y Ovadia, E Fertig, J Ren, Z Nado, D Sculley, S Nowozin, JV Dillon, ...
arXiv preprint arXiv:1906.02530, 2019
Variational Approaches for Auto-Encoding Generative Adversarial Networks
M Rosca, B Lakshminarayanan, D Warde-Farley, S Mohamed
arXiv preprint arXiv:1706.04987, 2017
Do Deep Generative Models Know What They Don't Know?
E Nalisnick, A Matsukawa, YW Teh, D Gorur, B Lakshminarayanan
arXiv preprint arXiv:1810.09136, 2018
Mondrian forests: Efficient online random forests
B Lakshminarayanan, DM Roy, YW Teh
Advances in Neural Information Processing Systems, 3140-3148, 2014
The Cramer Distance as a Solution to Biased Wasserstein Gradients
MG Bellemare, I Danihelka, W Dabney, S Mohamed, ...
arXiv preprint arXiv:1705.10743, 2017
Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step
W Fedus, M Rosca, B Lakshminarayanan, AM Dai, S Mohamed, ...
ICLR 2018, 0
Normalizing Flows for Probabilistic Modeling and Inference
G Papamakarios, E Nalisnick, DJ Rezende, S Mohamed, ...
arXiv preprint arXiv:1912.02762, 2019
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
D Hendrycks, N Mu, ED Cubuk, B Zoph, J Gilmer, B Lakshminarayanan
arXiv preprint arXiv:1912.02781, 2019
Likelihood ratios for out-of-distribution detection
J Ren, PJ Liu, E Fertig, J Snoek, R Poplin, M Depristo, J Dillon, ...
Advances in Neural Information Processing Systems, 14707-14718, 2019
Robust Bayesian matrix factorisation
B Lakshminarayanan, G Bouchard, C Archambeau
Proc. Intl. Conf. on Artificial Intelligence and Statistics (AISTATS), 2011
Distributed Bayesian Posterior Sampling via Moment Sharing
M Xu, B Lakshminarayanan, YW Teh, J Zhu, B Zhang
NIPS, 2014
Comparison of Maximum Likelihood and GAN-based training of Real NVPs
I Danihelka, B Lakshminarayanan, B Uria, D Wierstra, P Dayan
arXiv preprint arXiv:1705.05263, 2017
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server
L Hasenclever, S Webb, T Lienart, S Vollmer, B Lakshminarayanan, ...
Journal of Machine Learning Research 18 (106), 1-37, 2017
Distribution Matching in Variational Inference
M Rosca, B Lakshminarayanan, S Mohamed
arXiv preprint arXiv:1802.06847, 2018
Robust Bayesian matrix factorization and recommender systems using same
C Archambeau, G Bouchard, B Lakshminarayanan
US Patent 8,880,439, 2014
Mondrian forests for large-scale regression when uncertainty matters
B Lakshminarayanan, DM Roy, YW Teh
Int. Conf. Artificial Intelligence Stat.(AISTATS), 2016
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