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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

• Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
• NIPS
• 2017

Deep neural networks (NNs) are powerful black box predictors that have recently achieved impressive performance on a wide spectrum of tasks. Quantifying predictive uncertainty in NNs is a challenging… (More)

The Cramer Distance as a Solution to Biased Wasserstein Gradients

• Marc G. Bellemare, Ivo Danihelka, +4 authors Rémi Munos
• ArXiv
• 2017

The Wasserstein probability metric has received much attention from the machine learning community. Unlike the Kullback-Leibler divergence, which strictly measures change in probability, the… (More)

Acoustic classification of multiple simultaneous bird species: a multi-instance multi-label approach.

• Forrest Briggs, Balaji Lakshminarayanan, +5 authors Matthew Betts
• The Journal of the Acoustical Society of America
• 2012

Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far.… (More)

Variational Approaches for Auto-Encoding Generative Adversarial Networks

• Mihaela Rosca, Balaji Lakshminarayanan, David Warde-Farley, Shakir Mohamed
• ArXiv
• 2017

Auto-encoding generative adversarial networks (GANs) combine the standard GAN algorithm, which discriminates between real and model-generated data, with a reconstruction loss given by an… (More)

Mondrian Forests: Efficient Online Random Forests

• Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
• NIPS
• 2014

Ensembles of randomized decision trees, usually referred to as random forests, are widely used for classification and regression tasks in machine learning and statistics. Random forests achieve… (More)

Robust Bayesian Matrix Factorisation

• Balaji Lakshminarayanan, Guillaume Bouchard, Cédric Archambeau
• AISTATS
• 2011

We analyse the noise arising in collaborative filtering when formalised as a probabilistic matrix factorisation problem. We show empirically that modelling rowand column-specific variances is… (More)

Learning in Implicit Generative Models

• Shakir Mohamed, Balaji Lakshminarayanan
• ArXiv
• 2016

Generative adversarial networks (GANs) provide an algorit hm c framework for constructing generative models with several appealing pro perties: they do not require a likelihood function to be… (More)

Many Paths to Equilibrium: GANs Do Not Need to Decrease a Divergence At Every Step

• William Fedus, Mihaela Rosca, Balaji Lakshminarayanan, Andrew M. Dai, Shakir Mohamed, Ian J. Goodfellow
• ArXiv
• 2017

Generative adversarial networks (GANs) are a family of generative models that do not minimize a single training criterion. Unlike other generative models, the data distribution is learned via a game… (More)

Non-negative matrix factorization for parameter estimation in hidden Markov models

• Balaji Lakshminarayanan, Raviv Raich
• 2010 IEEE International Workshop on Machine…
• 2010

Hidden Markov models are well-known in analysis of random processes, which exhibit temporal or spatial structure and have been successfully applied to a wide variety of applications such as but not… (More)

Latent IBP Compound Dirichlet Allocation

• Cédric Archambeau, Balaji Lakshminarayanan, Guillaume Bouchard
• IEEE Transactions on Pattern Analysis and Machine…
• 2015

We introduce the four-parameter IBP compound Dirichlet process (ICDP), a stochastic process that generates sparse non-negative vectors with potentially an unbounded number of entries. If we… (More)

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