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06/05/2023
Coupled Variational Autoencoder
Variational auto-encoders are powerful probabilistic models in generativ...
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05/26/2022
Evolution of beliefs in social networks
Evolution of beliefs of a society are a product of interactions between ...
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05/17/2022
A psychological theory of explainability
The goal of explainable Artificial Intelligence (XAI) is to generate hum...
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03/14/2022
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel
Deep Gaussian Process as a Bayesian learning model is promising because ...
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12/17/2021
Probabilistic Inverse Optimal Transport
Optimal transport (OT) formalizes the problem of finding an optimal coup...
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10/01/2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
It is desirable to combine the expressive power of deep learning with Ga...
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06/16/2021
Explainable AI for Natural Adversarial Images
Adversarial images highlight how vulnerable modern image classifiers are...
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06/08/2021
Explainable AI for medical imaging: Explaining pneumothorax diagnoses with Bayesian Teaching
Limited expert time is a key bottleneck in medical imaging. Due to advan...
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05/16/2021
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence
Neural network architectures are achieving superhuman performance on an ...
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02/16/2021
Efficient Discretizations of Optimal Transport
Obtaining solutions to Optimal Transportation (OT) problems is typically...
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02/15/2021
Distributionally-Constrained Policy Optimization via Unbalanced Optimal Transport
We consider constrained policy optimization in Reinforcement Learning, w...
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02/13/2021
Interactive Learning from Activity Description
We present a novel interactive learning protocol that enables training r...
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02/07/2021
Mitigating belief projection in explainable artificial intelligence via Bayesian Teaching
State-of-the-art deep-learning systems use decision rules that are chall...
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02/13/2020
Sequential Cooperative Bayesian Inference
Cooperation is often implicitly assumed when learning from other agents....
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02/07/2020
Multi-source Deep Gaussian Process Kernel Learning
For many problems, relevant data are plentiful but explicit knowledge is...
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10/10/2019
Manifold learning from a teacher's demonstrations
We consider the problem of manifold learning. Extending existing approac...
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10/07/2019
A mathematical theory of cooperative communication
Cooperative communication plays a central role in theories of human cogn...
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05/27/2019
Interpretable deep Gaussian processes
We propose interpretable deep Gaussian Processes (GPs) that combine the ...
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10/04/2018
Generalizing the theory of cooperative inference
Cooperation information sharing is important to theories of human learni...
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03/09/2018
Standing Wave Decomposition Gaussian Process
We propose a Standing Wave Decomposition (SWD) approximation to Gaussian...
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05/25/2016
Toward a general, scaleable framework for Bayesian teaching with applications to topic models
Machines, not humans, are the world's dominant knowledge accumulators bu...
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12/03/2015