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04/06/2023
Anomaly Detection via Gumbel Noise Score Matching
We propose Gumbel Noise Score Matching (GNSM), a novel unsupervised meth...
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02/27/2023
Acquisition Conditioned Oracle for Nongreedy Active Feature Acquisition
We develop novel methodology for active feature acquisition (AFA), the s...
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05/11/2022
Learning to Retrieve Videos by Asking Questions
The majority of traditional text-to-video retrieval systems operate in s...
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01/18/2022
Interpretable Single-Cell Set Classification with Kernel Mean Embeddings
Modern single-cell flow and mass cytometry technologies measure the expr...
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10/25/2020
Multiscale Score Matching for Out-of-Distribution Detection
We present a new methodology for detecting out-of-distribution (OOD) ima...
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09/21/2019
Deep Message Passing on Sets
Modern methods for learning over graph input data have shown the fruitfu...
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09/18/2019
Meta-Neighborhoods
Traditional methods for training neural networks use training data just ...
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05/31/2019
MolecularRNN: Generating realistic molecular graphs with optimized properties
Designing new molecules with a set of predefined properties is a core pr...
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02/05/2019
Permutation Invariant Likelihoods and Equivariant Transformations
In this work, we fill a substantial void in machine learning and statist...
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02/04/2019
A Forest from the Trees: Generation through Neighborhoods
In this work, we propose to learn a generative model using both learned ...
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11/06/2017
Estimating Cosmological Parameters from the Dark Matter Distribution
A grand challenge of the 21st century cosmology is to accurately estimat...
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06/29/2015
Bayesian Nonparametric Kernel-Learning
Kernel methods are ubiquitous tools in machine learning. They have prove...
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10/27/2014
