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Practical Bayesian Optimization of Machine Learning Algorithms
This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms.
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
A large-scale benchmark of existing state-of-the-art methods on classification problems and the effect of dataset shift on accuracy and calibration is presented, finding that traditional post-hoc calibration does indeed fall short, as do several other previous methods.
Multi-Task Bayesian Optimization
This paper proposes an adaptation of a recently developed acquisition function, entropy search, to the cost-sensitive, multi-task setting and demonstrates the utility of this new acquisition function by leveraging a small dataset to explore hyper-parameter settings for a large dataset.
Scalable Bayesian Optimization Using Deep Neural Networks
This work shows that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically, which allows for a previously intractable degree of parallelism.
Likelihood Ratios for Out-of-Distribution Detection
This work investigates deep generative model based approaches for OOD detection and observes that the likelihood score is heavily affected by population level background statistics, and proposes a likelihood ratio method forDeep generative models which effectively corrects for these confounding background statistics.
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling
This work benchmarks well-established and recently developed methods for approximate posterior sampling combined with Thompson Sampling over a series of contextual bandit problems and finds that many approaches that have been successful in the supervised learning setting underperformed in the sequential decision-making scenario.
Learning Latent Permutations with Gumbel-Sinkhorn Networks
- Gonzalo E. Mena, David Belanger, Scott W. Linderman, Jasper Snoek
- Computer ScienceICLR
- 15 February 2018
A collection of new methods for end-to-end learning in such models that approximate discrete maximum-weight matching using the continuous Sinkhorn operator are introduced.
Spectral Representations for Convolutional Neural Networks
This work proposes spectral pooling, which performs dimensionality reduction by truncating the representation in the frequency domain, and demonstrates the effectiveness of complex-coefficient spectral parameterization of convolutional filters.
Bayesian Optimization with Unknown Constraints
This paper studies Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently.
How Good is the Bayes Posterior in Deep Neural Networks Really?
This work demonstrates through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD and argues that it is timely to focus on understanding the origin of the improved performance of cold posteriors.