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05/04/2023
String Diagrams with Factorized Densities
A growing body of research on probabilistic programs and causal models h...
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02/23/2023
CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
Large Language Models (LLMs) have yielded fast and dramatic progress in ...
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11/02/2022
Verified Reversible Programming for Verified Lossless Compression
Lossless compression implementations typically contain two programs, an ...
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10/14/2022
A Variational Perspective on Generative Flow Networks
Generative flow networks (GFNs) are a class of models for sequential sam...
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10/12/2022
That's the Wrong Lung! Evaluating and Improving the Interpretability of Unsupervised Multimodal Encoders for Medical Data
Pretraining multimodal models on Electronic Health Records (EHRs) provid...
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08/22/2022
Deriving time-averaged active inference from control principles
Active inference offers a principled account of behavior as minimizing a...
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04/24/2022
Learning Symmetric Embeddings for Equivariant World Models
Incorporating symmetries can lead to highly data-efficient and generaliz...
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01/13/2022
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning
Modern deep learning requires large-scale extensively labelled datasets ...
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06/25/2021
Conjugate Energy-Based Models
In this paper, we propose conjugate energy-based models (CEBMs), a new c...
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06/21/2021
Nested Variational Inference
We develop nested variational inference (NVI), a family of methods that ...
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04/14/2021
Disentangling Representations of Text by Masking Transformers
Representations from large pretrained models such as BERT encode a range...
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04/13/2021
On the Impact of Random Seeds on the Fairness of Clinical Classifiers
Recent work has shown that fine-tuning large networks is surprisingly se...
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03/01/2021
Learning Proposals for Probabilistic Programs with Inference Combinators
We develop operators for construction of proposals in probabilistic prog...
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02/22/2021
Generator Surgery for Compressed Sensing
Image recovery from compressive measurements requires a signal prior for...
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01/11/2021
Action Priors for Large Action Spaces in Robotics
In robotics, it is often not possible to learn useful policies using pur...
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06/17/2020
Improving Few-Shot Visual Classification with Unlabelled Examples
We propose a transductive meta-learning method that uses unlabelled inst...
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04/09/2020
Query-Focused EHR Summarization to Aid Imaging Diagnosis
Electronic Health Records (EHRs) provide vital contextual information to...
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03/22/2020
Deep Markov Spatio-Temporal Factorization
We introduce deep Markov spatio-temporal factorization (DMSTF), a deep g...
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03/09/2020
Learning discrete state abstractions with deep variational inference
Abstraction is crucial for effective sequential decision making in domai...
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11/11/2019
Evaluating Combinatorial Generalization in Variational Autoencoders
We evaluate the ability of variational autoencoders to generalize to uns...
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11/04/2019
Amortized Population Gibbs Samplers with Neural Sufficient Statistics
We develop amortized population Gibbs (APG) samplers, a new class of aut...
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06/21/2019
Neural Topographic Factor Analysis for fMRI Data
Neuroimaging experiments produce a large volume (gigabytes) of high-dime...
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12/22/2018
Can VAEs Generate Novel Examples?
An implicit goal in works on deep generative models is that such models ...
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12/12/2018
Structured Neural Topic Models for Reviews
We present Variational Aspect-based Latent Topic Allocation (VALTA), a f...
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12/12/2018
Structured Representations for Reviews:Aspect-Based Variational Hidden Factor Models
We present Variational Aspect-Based Latent Dirichlet Allocation (VALDA),...
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12/04/2018
Modeling Theory of Mind for Autonomous Agents with Probabilistic Programs
As autonomous agents become more ubiquitous, they will eventually have t...
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11/14/2018
Composing Modeling and Inference Operations with Probabilistic Program Combinators
Probabilistic programs with dynamic computation graphs can define measur...
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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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09/27/2018
An Introduction to Probabilistic Programming
This document is designed to be a first-year graduate-level introduction...
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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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04/19/2018
Learning Disentangled Representations of Texts with Application to Biomedical Abstracts
We propose a method for learning disentangled sets of vector representat...
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04/06/2018
Hierarchical Disentangled Representations
Deep latent-variable models learn representations of high-dimensional da...
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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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06/01/2017
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Variational autoencoders (VAEs) learn representations of data by jointly...
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11/22/2016
Inducing Interpretable Representations with Variational Autoencoders
We develop a framework for incorporating structured graphical models in ...
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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
Interacting Particle Markov Chain Monte Carlo
We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a P...
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07/03/2015
A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive p...
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02/25/2015
Path Finding under Uncertainty through Probabilistic Inference
We introduce a new approach to solving path-finding problems under uncer...
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01/27/2015
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current state-of...
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01/22/2015
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...
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01/28/2014
Tempering by Subsampling
In this paper we demonstrate that tempering Markov chain Monte Carlo sam...
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01/05/2014
Stylistic Clusters and the Syrian/South Syrian Tradition of First-Millennium BCE Levantine Ivory Carving: A Machine Learning Approach
Thousands of first-millennium BCE ivory carvings have been excavated fro...
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05/15/2013
