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08/01/2023
SelfCheck: Using LLMs to Zero-Shot Check Their Own Step-by-Step Reasoning
The recent progress in large language models (LLMs), especially the inve...
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07/23/2023
In-Context Learning in Large Language Models Learns Label Relationships but Is Not Conventional Learning
The performance of Large Language Models (LLMs) on downstream tasks ofte...
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06/12/2023
On the Expected Size of Conformal Prediction Sets
While conformal predictors reap the benefits of rigorous statistical gua...
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05/25/2023
Trans-Dimensional Generative Modeling via Jump Diffusion Models
We propose a new class of generative models that naturally handle data o...
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05/24/2023
Deep Stochastic Processes via Functional Markov Transition Operators
We introduce Markov Neural Processes (MNPs), a new class of Stochastic P...
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04/17/2023
Prediction-Oriented Bayesian Active Learning
Information-theoretic approaches to active learning have traditionally f...
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04/04/2023
Incorporating Unlabelled Data into Bayesian Neural Networks
We develop a contrastive framework for learning better prior distributio...
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02/28/2023
Modern Bayesian Experimental Design
Bayesian experimental design (BED) provides a powerful and general frame...
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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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05/30/2022
A Continuous Time Framework for Discrete Denoising Models
We provide the first complete continuous time framework for denoising di...
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02/14/2022
Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
We propose Active Surrogate Estimators (ASEs), a new method for label-ef...
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11/03/2021
Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
We introduce implicit Deep Adaptive Design (iDAD), a new method for perf...
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10/26/2021
Online Variational Filtering and Parameter Learning
We present a variational method for online state estimation and paramete...
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06/25/2021
InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents
We introduce a simple and effective method for learning VAEs with contro...
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06/23/2021
Learning Multimodal VAEs through Mutual Supervision
Multimodal VAEs seek to model the joint distribution over heterogeneous ...
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06/22/2021
Active Learning under Pool Set Distribution Shift and Noisy Data
Active Learning is essential for more label-efficient deep learning. Bay...
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06/10/2021
Group Equivariant Subsampling
Subsampling is used in convolutional neural networks (CNNs) in the form ...
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06/09/2021
Expectation Programming
Building on ideas from probabilistic programming, we introduce the conce...
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06/04/2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
We challenge a common assumption underlying most supervised deep learnin...
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03/09/2021
Active Testing: Sample-Efficient Model Evaluation
We introduce active testing: a new framework for sample-efficient model ...
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03/03/2021
Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design
We introduce Deep Adaptive Design (DAD), a method for amortizing the cos...
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02/15/2021
Certifiably Robust Variational Autoencoders
We introduce an approach for training Variational Autoencoders (VAEs) th...
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01/27/2021
On Statistical Bias In Active Learning: How and When To Fix It
Active learning is a powerful tool when labelling data is expensive, but...
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11/01/2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
We show that the gradient estimates used in training Deep Gaussian Proce...
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10/19/2020
Improving Transformation Invariance in Contrastive Representation Learning
We propose methods to strengthen the invariance properties of representa...
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07/14/2020
Towards a Theoretical Understanding of the Robustness of Variational Autoencoders
We make inroads into understanding the robustness of Variational Autoenc...
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06/17/2020
Rethinking Semi-Supervised Learning in VAEs
We present an alternative approach to semi-supervision in variational au...
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04/10/2020
A note on blind contact tracing at scale with applications to the COVID-19 pandemic
The current COVID-19 pandemic highlights the utility of contact tracing,...
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12/10/2019
Statistically Robust Neural Network Classification
Recently there has been much interest in quantifying the robustness of n...
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11/01/2019
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian opti...
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10/29/2019
Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
Universal probabilistic programming systems (PPSs) provide a powerful an...
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10/20/2019
Amortized Rejection Sampling in Universal Probabilistic Programming
Existing approaches to amortized inference in probabilistic programs wit...
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07/18/2019
Amortized Monte Carlo Integration
Current approaches to amortizing Bayesian inference focus solely on appr...
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05/31/2019
On the Fairness of Disentangled Representations
Recently there has been a significant interest in learning disentangled ...
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05/29/2019
Hijacking Malaria Simulators with Probabilistic Programming
Epidemiology simulations have become a fundamental tool in the fight aga...
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03/13/2019
Variational Estimators for Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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03/13/2019
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework fo...
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03/06/2019
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
We develop a new Low-level, First-order Probabilistic Programming Langua...
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12/06/2018
Disentangling Disentanglement
We develop a generalised notion of disentanglement in Variational Auto-E...
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11/17/2018
Statistical Verification of Neural Networks
We present a new approach to neural network verification based on estima...
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10/31/2018
On Exploration, Exploitation and Learning in Adaptive Importance Sampling
We study adaptive importance sampling (AIS) as an online learning proble...
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06/25/2018
Inference Trees: Adaptive Inference with Exploration
We introduce inference trees (ITs), a new class of inference methods tha...
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03/16/2018
Nesting Probabilistic Programs
We formalize the notion of nesting probabilistic programming queries and...
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02/13/2018
Tighter Variational Bounds are Not Necessarily Better
We provide theoretical and empirical evidence that using tighter evidenc...
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09/18/2017
On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators
We present a formalization of nested Monte Carlo (NMC) estimation, where...
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07/13/2017
Bayesian Optimization for Probabilistic Programs
We present the first general purpose framework for marginal maximum a po...
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05/29/2017
Auto-Encoding Sequential Monte Carlo
We introduce AESMC: a method for using deep neural networks for simultan...
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11/21/2016
Probabilistic structure discovery in time series data
Existing methods for structure discovery in time series data construct i...
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02/16/2016
