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09/20/2023
Signature Activation: A Sparse Signal View for Holistic Saliency
The adoption of machine learning in healthcare calls for model transpare...
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09/01/2023
Why do universal adversarial attacks work on large language models?: Geometry might be the answer
Transformer based large language models with emergent capabilities are b...
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07/28/2023
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text
A common way to explore text corpora is through low-dimensional projecti...
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06/20/2023
The Unintended Consequences of Discount Regularization: Improving Regularization in Certainty Equivalence Reinforcement Learning
Discount regularization, using a shorter planning horizon when calculati...
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02/15/2023
Multimodal N-of-1 trials: A Novel Personalized Healthcare Design
N-of-1 trials aim to estimate treatment effects on the individual level ...
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12/01/2022
Modeling Mobile Health Users as Reinforcement Learning Agents
Mobile health (mHealth) technologies empower patients to adopt/maintain ...
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11/16/2022
An Empirical Analysis of the Advantages of Finite- v.s. Infinite-Width Bayesian Neural Networks
Comparing Bayesian neural networks (BNNs) with different widths is chall...
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11/10/2022
Does the explanation satisfy your needs?: A unified view of properties of explanations
Interpretability provides a means for humans to verify aspects of machin...
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08/02/2022
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
For responsible decision making in safety-critical settings, machine lea...
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07/13/2022
Policy Optimization with Sparse Global Contrastive Explanations
We develop a Reinforcement Learning (RL) framework for improving an exis...
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02/23/2022
Wide Mean-Field Bayesian Neural Networks Ignore the Data
Bayesian neural networks (BNNs) combine the expressive power of deep lea...
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06/24/2021
Promises and Pitfalls of Black-Box Concept Learning Models
Machine learning models that incorporate concept learning as an intermed...
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06/13/2021
Wide Mean-Field Variational Bayesian Neural Networks Ignore the Data
Variational inference enables approximate posterior inference of the hig...
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07/14/2020
Failure Modes of Variational Autoencoders and Their Effects on Downstream Tasks
Variational Auto-encoders (VAEs) are deep generative latent variable mod...
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07/12/2020
BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
Traditional training of deep classifiers yields overconfident models tha...
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06/21/2020
Learned Uncertainty-Aware (LUNA) Bases for Bayesian Regression using Multi-Headed Auxiliary Networks
Neural Linear Models (NLM) are deep models that produce predictive uncer...
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04/13/2020
Power-Constrained Bandits
Contextual bandits often provide simple and effective personalization in...
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03/17/2020
Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders
Variational Auto-encoders (VAEs) are deep generative latent variable mod...
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11/04/2019
Ensembles of Locally Independent Prediction Models
Many ensemble methods encourage their constituent models to be diverse, ...
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11/01/2019
Learning Deep Bayesian Latent Variable Regression Models that Generalize: When Non-identifiability is a Problem
Bayesian Neural Networks with Latent Variables (BNN+LV's) provide uncert...
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06/24/2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference
Bayesian Neural Networks (BNNs) place priors over the parameters in a ne...
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05/24/2019
A general method for regularizing tensor decomposition methods via pseudo-data
Tensor decomposition methods allow us to learn the parameters of latent ...
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12/07/2018
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models
In real-world applications, it is often expensive and time-consuming to ...
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11/16/2018
Latent Projection BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
While modern neural networks are making remarkable gains in terms of pre...
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06/22/2018
Learning Qualitatively Diverse and Interpretable Rules for Classification
There has been growing interest in developing accurate models that can a...
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10/18/2017
Weighted Tensor Decomposition for Learning Latent Variables with Partial Data
Tensor decomposition methods are popular tools for learning latent varia...
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06/20/2016
