Warren R. Morningstar

According to our database, Warren R. Morningstar authored at least 15 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations.

Neha Mukund Kalibhat, Warren Richard Morningstar, Alex Bijamov, Luyang Liu, Karan Singhal, Philip Andrew Mansfield

Proceedings of UniReps: the Second Edition of the Workshop on Unifying Representations in Neural Models, 2025

Forte : Finding Outliers with Representation Typicality Estimation.

Debargha Ganguly, Warren Richard Morningstar, Andrew Seohwan Yu, Vipin Chaudhary

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Federated Variational Inference: Towards Improved Personalization and Generalization.

Elahe Vedadi, Joshua V. Dillon, Philip Andrew Mansfield, Karan Singhal, Arash Afkanpour, Warren Richard Morningstar

Trans. Mach. Learn. Res., 2024

SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer.

Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad, Alex Bijamov, Warren Richard Morningstar, Philip Andrew Mansfield

Trans. Mach. Learn. Res., 2024

Augmentations vs Algorithms: What Works in Self-Supervised Learning.

Warren R. Morningstar, Alex Bijamov, Chris Duvarney, Luke Friedman, Neha Mukund Kalibhat, Luyang Liu, Philip Andrew Mansfield, Renan A. Rojas-Gomez, Karan Singhal, Bradley Green, Sushant Prakash

CoRR, 2024

2023

Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout.

Pengfei Guo, Warren Richard Morningstar, Raviteja Vemulapalli, Karan Singhal, Vishal M. Patel, Philip Andrew Mansfield

CoRR, 2023

Random field augmentations for self-supervised representation learning.

Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar, Karan Singhal

Proceedings of the NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2023

Weighted Ensemble Self-Supervised Learning.

Yangjun Ruan, Saurabh Singh, Warren Richard Morningstar, Alexander A. Alemi, Sergey Ioffe, Ian Fischer, Joshua V. Dillon

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Federated Training of Dual Encoding Models on Small Non-IID Client Datasets.

Raviteja Vemulapalli, Warren Richard Morningstar, Philip Andrew Mansfield, Hubert Eichner, Karan Singhal, Arash Afkanpour, Bradley Green

CoRR, 2022

What Do We Mean by Generalization in Federated Learning?

Honglin Yuan, Warren Richard Morningstar, Lin Ning, Karan Singhal

Proceedings of the Tenth International Conference on Learning Representations, 2022

PACm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime.

Warren R. Morningstar, Alex Alemi, Joshua V. Dillon

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Automatic Differentiation Variational Inference with Mixtures.

Warren R. Morningstar, Sharad M. Vikram, Cusuh Ham, Andrew G. Gallagher, Joshua V. Dillon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Density of States Estimation for Out of Distribution Detection.

Warren R. Morningstar, Cusuh Ham, Andrew G. Gallagher, Balaji Lakshminarayanan, Alexander A. Alemi, Joshua V. Dillon

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

VIB is Half Bayes.

Alexander A. Alemi, Warren R. Morningstar, Ben Poole, Ian Fischer, Joshua V. Dillon

CoRR, 2020

PAC^m-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime.

Warren R. Morningstar, Alexander A. Alemi, Joshua V. Dillon

CoRR, 2020