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06/27/2023
Exploiting Inferential Structure in Neural Processes
Neural Processes (NPs) are appealing due to their ability to perform fas...
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06/05/2023
Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity
Modern predictive models are often deployed to environments in which com...
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11/11/2022
Do Bayesian Neural Networks Need To Be Fully Stochastic?
We investigate the efficacy of treating all the parameters in a Bayesian...
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10/30/2022
Learning to Defer to Multiple Experts: Consistent Surrogate Losses, Confidence Calibration, and Conformal Ensembles
We study the statistical properties of learning to defer (L2D) to multip...
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10/10/2022
Sampling-based inference for large linear models, with application to linearised Laplace
Large-scale linear models are ubiquitous throughout machine learning, wi...
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06/30/2022
Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions
Offensive Content Warning: This paper contains offensive language only f...
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06/17/2022
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has recei...
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03/19/2022
Adversarial Defense via Image Denoising with Chaotic Encryption
In the literature on adversarial examples, white box and black box attac...
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02/08/2022
Calibrated Learning to Defer with One-vs-All Classifiers
The learning to defer (L2D) framework has the potential to make AI syste...
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11/18/2021
How Emotionally Stable is ALBERT? Testing Robustness with Stochastic Weight Averaging on a Sentiment Analysis Task
Despite their success, modern language models are fragile. Even small ch...
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10/28/2020
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference
The Bayesian paradigm has the potential to solve some of the core issues...
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06/18/2020
Predictive Complexity Priors
Specifying a Bayesian prior is notoriously difficult for complex models ...
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12/05/2019
Normalizing Flows for Probabilistic Modeling and Inference
Normalizing flows provide a general mechanism for defining expressive pr...
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08/06/2019
Bayesian Batch Active Learning as Sparse Subset Approximation
Leveraging the wealth of unlabeled data produced in recent years provide...
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06/07/2019
Detecting Out-of-Distribution Inputs to Deep Generative Models Using a Test for Typicality
Recent work has shown that deep generative models can assign higher like...
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02/07/2019
Hybrid Models with Deep and Invertible Features
We propose a neural hybrid model consisting of a linear model defined on...
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10/22/2018
Do Deep Generative Models Know What They Don't Know?
A neural network deployed in the wild may be asked to make predictions f...
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10/09/2018
Unifying the Dropout Family Through Structured Shrinkage Priors
Dropout regularization of deep neural networks has been a mysterious yet...
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11/21/2017
Mondrian Processes for Flow Cytometry Analysis
Analysis of flow cytometry data is an essential tool for clinical diagno...
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04/04/2017
Learning Approximately Objective Priors
Informative Bayesian priors are often difficult to elicit, and when this...
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05/20/2016
Stick-Breaking Variational Autoencoders
We extend Stochastic Gradient Variational Bayes to perform posterior inf...
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11/17/2015
Learning the Dimensionality of Word Embeddings
We describe a method for learning word embeddings with data-dependent di...
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06/10/2015
