Tim G. J. Rudner | DeepAI

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∙ 07/14/2023

Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions

Accelerating the discovery of novel and more effective therapeutics is a...

0 Leo Klarner, et al. ∙

∙ 05/31/2023

A Study of Bayesian Neural Network Surrogates for Bayesian Optimization

Bayesian optimization is a highly efficient approach to optimizing objec...

0 Yucen Lily Li, et al. ∙

∙ 05/31/2023

Protein Design with Guided Discrete Diffusion

A popular approach to protein design is to combine a generative model wi...

3 Nate Gruver, et al. ∙

∙ 03/01/2023

An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization

In this paper, we provide an information-theoretic perspective on Varian...

0 Ravid Shwartz-Ziv, et al. ∙

∙ 01/04/2023

On Sequential Bayesian Inference for Continual Learning

Sequential Bayesian inference can be used for continual learning to prev...

0 Samuel Kessler, et al. ∙

∙ 12/28/2022

On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations

KL-regularized reinforcement learning from expert demonstrations has pro...

0 Tim G. J. Rudner, et al. ∙

∙ 11/23/2022

Benchmarking Bayesian Deep Learning on Diabetic Retinopathy Detection Tasks

Bayesian deep learning seeks to equip deep neural networks with the abil...

0 Neil Band, et al. ∙

∙ 06/09/2022

Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations

Offline reinforcement learning has shown great promise in leveraging lar...

0 Cong Lu, et al. ∙

∙ 06/07/2021

Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for num...

0 Zachary Nado, et al. ∙

∙ 04/20/2021

Outcome-Driven Reinforcement Learning via Variational Inference

While reinforcement learning algorithms provide automated acquisition of...

0 Tim G. J. Rudner, et al. ∙

∙ 11/01/2020

On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes

We show that the gradient estimates used in training Deep Gaussian Proce...

0 Tim G. J. Rudner, et al. ∙

∙ 11/01/2020

Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and low...

0 Tim G. J. Rudner, et al. ∙

∙ 12/22/2019

A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks

Evaluation of Bayesian deep learning (BDL) methods is challenging. We of...

86 Angelos Filos, et al. ∙

∙ 02/11/2019

The StarCraft Multi-Agent Challenge

In the last few years, deep multi-agent reinforcement learning (RL) has ...

32 Mikayel Samvelyan, et al. ∙

∙ 12/05/2018

Multi^3Net: Segmenting Flooded Buildings via Fusion of Multiresolution, Multisensor, and Multitemporal Satellite Imagery

We propose a novel approach for rapid segmentation of flooded buildings ...

2 Tim G. J. Rudner, et al. ∙

∙ 11/03/2018

VIREL: A Variational Inference Framework for Reinforcement Learning

Applying probabilistic models to reinforcement learning (RL) has become ...

0 Matthew Fellows, et al. ∙

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