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09/15/2023
Estimation of Counterfactual Interventions under Uncertainties
Counterfactual analysis is intuitively performed by humans on a daily ba...
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09/15/2023
Sampling-Free Probabilistic Deep State-Space Models
Many real-world dynamical systems can be described as State-Space Models...
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07/07/2023
BOF-UCB: A Bayesian-Optimistic Frequentist Algorithm for Non-Stationary Contextual Bandits
We propose a novel Bayesian-Optimistic Frequentist Upper Confidence Boun...
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05/02/2023
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Graph neural networks are often used to model interacting dynamical syst...
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01/30/2023
PAC-Bayesian Soft Actor-Critic Learning
Actor-critic algorithms address the dual goals of reinforcement learning...
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11/29/2022
PAC-Bayes Bounds for Bandit Problems: A Survey and Experimental Comparison
PAC-Bayes has recently re-emerged as an effective theory with which one ...
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05/24/2022
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
We study for the first time uncertainty-aware modeling of continuous-tim...
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03/07/2022
PAC-Bayesian Lifelong Learning For Multi-Armed Bandits
We present a PAC-Bayesian analysis of lifelong learning. In the lifelong...
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03/02/2022
Continual Learning of Multi-modal Dynamics with External Memory
We study the problem of fitting a model to a dynamical environment when ...
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12/06/2021
Traversing Time with Multi-Resolution Gaussian Process State-Space Models
Gaussian Process state-space models capture complex temporal dependencie...
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06/02/2021
Evidential Turing Processes
A probabilistic classifier with reliable predictive uncertainties i) fit...
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10/14/2020
Differentiable Implicit Layers
In this paper, we introduce an efficient backpropagation scheme for non-...
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06/17/2020
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
We propose a novel scheme for fitting heavily parameterized non-linear s...
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06/16/2020
Deterministic Inference of Neural Stochastic Differential Equations
Model noise is known to have detrimental effects on neural networks, suc...
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12/02/2019
Differential Bayesian Neural Nets
Neural Ordinary Differential Equations (N-ODEs) are a powerful building ...
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06/27/2019
Deep Active Learning with Adaptive Acquisition
Model selection is treated as a standard performance boosting step in ma...
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06/03/2019
Bayesian Prior Networks with PAC Training
We propose to train Bayesian Neural Networks (BNNs) by empirical Bayes a...
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06/05/2018
Evidential Deep Learning to Quantify Classification Uncertainty
Deterministic neural nets have been shown to learn effective predictors ...
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05/19/2018
