Balaji Lakshminarayanan

According to our database1, Balaji Lakshminarayanan authored at least 39 papers between 2009 and 2020.

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Bibliography

2020
Bayesian Deep Ensembles via the Neural Tangent Kernel.
CoRR, 2020

Revisiting One-vs-All Classifiers for Predictive Uncertainty and Out-of-Distribution Detection in Neural Networks.
CoRR, 2020

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift.
CoRR, 2020

Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness.
CoRR, 2020

Density of States Estimation for Out-of-Distribution Detection.
CoRR, 2020

Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors.
CoRR, 2020

AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Normalizing Flows for Probabilistic Modeling and Inference.
CoRR, 2019

Deep Ensembles: A Loss Landscape Perspective.
CoRR, 2019

Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality.
CoRR, 2019

Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Likelihood Ratios for Out-of-Distribution Detection.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Hybrid Models with Deep and Invertible Features.
Proceedings of the 36th International Conference on Machine Learning, 2019

Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems.
Proceedings of the 36th International Conference on Machine Learning, 2019

Do Deep Generative Models Know What They Don't Know?
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Adapting Auxiliary Losses Using Gradient Similarity.
CoRR, 2018

Learning from Delayed Outcomes with Intermediate Observations.
CoRR, 2018

Distribution Matching in Variational Inference.
CoRR, 2018

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step.
Proceedings of the 6th International Conference on Learning Representations, 2018

2017
Distributed Bayesian Learning with Stochastic Natural Gradient Expectation Propagation and the Posterior Server.
J. Mach. Learn. Res., 2017

Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees.
CoRR, 2017

Variational Approaches for Auto-Encoding Generative Adversarial Networks.
CoRR, 2017

Comparison of Maximum Likelihood and GAN-based training of Real NVPs.
CoRR, 2017

The Cramer Distance as a Solution to Biased Wasserstein Gradients.
CoRR, 2017

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
Learning in Implicit Generative Models.
CoRR, 2016

The Mondrian Kernel.
Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Mondrian Forests for Large-Scale Regression when Uncertainty Matters.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Latent IBP Compound Dirichlet Allocation.
IEEE Trans. Pattern Anal. Mach. Intell., 2015

Approximate Inference with the Variational Holder Bound.
CoRR, 2015

Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages.
Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

Particle Gibbs for Bayesian Additive Regression Trees.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Distributed Bayesian Posterior Sampling via Moment Sharing.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Mondrian Forests: Efficient Online Random Forests.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2013
Inferring ground truth from multi-annotator ordinal data: a probabilistic approach
CoRR, 2013

Top-down particle filtering for Bayesian decision trees.
Proceedings of the 30th International Conference on Machine Learning, 2013

2011
Robust Bayesian Matrix Factorisation.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Inference in Supervised latent Dirichlet allocation.
Proceedings of the 2011 IEEE International Workshop on Machine Learning for Signal Processing, 2011

2009
A Syllable-Level Probabilistic Framework for Bird Species Identification.
Proceedings of the International Conference on Machine Learning and Applications, 2009


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