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05/19/2023
Moment Matching Denoising Gibbs Sampling
Energy-Based Models (EBMs) offer a versatile framework for modeling comp...
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09/15/2022
Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and st...
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05/28/2022
Improving VAE-based Representation Learning
Latent variable models like the Variational Auto-Encoder (VAE) are commo...
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12/06/2021
Simulation Intelligence: Towards a New Generation of Scientific Methods
The original "Seven Motifs" set forth a roadmap of essential methods for...
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11/08/2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes
Variable selection in Gaussian processes (GPs) is typically undertaken b...
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06/09/2021
I Don't Need 𝐮: Identifiable Non-Linear ICA Without Side Information
In this work we introduce a new approach for identifiable non-linear ICA...
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12/21/2020
Barking up the right tree: an approach to search over molecule synthesis DAGs
When designing new molecules with particular properties, it is not only ...
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11/24/2020
Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learning
Graph neural networks have become very popular for machine learning on m...
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10/05/2020
Goal-directed Generation of Discrete Structures with Conditional Generative Models
Despite recent advances, goal-directed generation of structured discrete...
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07/02/2020
Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Models
Multimodal learning for generative models often refers to the learning o...
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02/18/2020
Learning Bijective Feature Maps for Linear ICA
Separating high-dimensional data like images into independent latent fac...
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11/08/2019
Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models
Learning generative models that span multiple data modalities, such as v...
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11/06/2019
Data Generation for Neural Programming by Example
Programming by example is the problem of synthesizing a program from a s...
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06/12/2019
A Model to Search for Synthesizable Molecules
Deep generative models are able to suggest new organic molecules by gene...
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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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07/18/2018
Take a Look Around: Using Street View and Satellite Images to Estimate House Prices
When an individual purchases a home, they simultaneously purchase its st...
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05/23/2018
Predicting Electron Paths
Chemical reactions can be described as the stepwise redistribution of el...
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04/06/2018
Hierarchical Disentangled Representations
Deep latent-variable models learn representations of high-dimensional da...
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12/05/2017
Learning a Generative Model for Validity in Complex Discrete Structures
Deep generative models have been successfully used to learn representati...
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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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03/06/2017
Grammar Variational Autoencoder
Deep generative models have been wildly successful at learning coherent ...
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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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02/22/2016
Inference Networks for Sequential Monte Carlo in Graphical Models
We introduce a new approach for amortizing inference in directed graphic...
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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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10/11/2015
Kernel Sequential Monte Carlo
We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
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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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01/22/2015
Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs
We introduce an adaptive output-sensitive Metropolis-Hastings algorithm ...
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07/10/2014
Asynchronous Anytime Sequential Monte Carlo
We introduce a new sequential Monte Carlo algorithm we call the particle...
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03/03/2014
A Compilation Target for Probabilistic Programming Languages
Forward inference techniques such as sequential Monte Carlo and particle...
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01/28/2014
