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ORCIDWesley J. Maddox
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2020 – today
- 2023
[c13]Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson:
Bayesian Optimization with Conformal Prediction Sets. AISTATS 2023: 959-986- [i18]Yanjun Liu, Milena Jovanovic, Krishnanand Mallayya, Wesley J. Maddox, Andrew Gordon Wilson, Sebastian Klemenz, Leslie M. Schoop, Eun-Ah Kim
:
Materials Expert-Artificial Intelligence for Materials Discovery. CoRR abs/2312.02796 (2023) - 2022
- [c12]Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson:
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. ICML 2022: 1798-1816 - [c11]Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson:
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. ICML 2022: 20459-20478 - [c10]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. NeurIPS 2022 - [c9]Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson:
Low-precision arithmetic for fast Gaussian processes. UAI 2022: 1306-1316 - [i17]Samuel Stanton, Wesley J. Maddox, Nate Gruver, Phillip M. Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson:
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders. CoRR abs/2203.12742 (2022) - [i16]Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson:
On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification. CoRR abs/2203.16481 (2022) - [i15]Gregory W. Benton, Wesley J. Maddox, Andrew Gordon Wilson:
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes. CoRR abs/2207.06544 (2022) - [i14]Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson:
Low-Precision Arithmetic for Fast Gaussian Processes. CoRR abs/2207.06856 (2022) - [i13]Samuel Stanton, Wesley J. Maddox, Andrew Gordon Wilson:
Bayesian Optimization with Conformal Coverage Guarantees. CoRR abs/2210.12496 (2022) - 2021
- [c8]Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. AISTATS 2021: 2737-2745 - [c7]Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson:
Kernel Interpolation for Scalable Online Gaussian Processes. AISTATS 2021: 3133-3141 - [c6]Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson:
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. ICML 2021: 769-779 - [c5]Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson:
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. NeurIPS 2021: 6365-6379 - [c4]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. NeurIPS 2021: 19274-19287 - [i12]Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson:
Loss Surface Simplexes for Mode Connecting Volumes and Fast Ensembling. CoRR abs/2102.13042 (2021) - [i11]Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas C. Damianou:
Fast Adaptation with Linearized Neural Networks. CoRR abs/2103.01439 (2021) - [i10]Samuel Stanton, Wesley J. Maddox, Ian A. Delbridge, Andrew Gordon Wilson:
Kernel Interpolation for Scalable Online Gaussian Processes. CoRR abs/2103.01454 (2021) - [i9]Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy:
Bayesian Optimization with High-Dimensional Outputs. CoRR abs/2106.12997 (2021) - [i8]Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson:
Conditioning Sparse Variational Gaussian Processes for Online Decision-making. CoRR abs/2110.15172 (2021) - [i7]Wesley J. Maddox, Qing Feng, Maximilian Balandat:
Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization. CoRR abs/2111.14911 (2021) - [i6]Wesley J. Maddox, Sanyam Kapoor, Andrew Gordon Wilson:
When are Iterative Gaussian Processes Reliably Accurate? CoRR abs/2112.15246 (2021) - 2020
- [i5]Wesley J. Maddox, Gregory W. Benton, Andrew Gordon Wilson:
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited. CoRR abs/2003.02139 (2020) - [i4]Shuai Tang, Wesley J. Maddox, Charlie Dickens, Tom Diethe, Andreas C. Damianou:
Similarity of Neural Networks with Gradients. CoRR abs/2003.11498 (2020)
2010 – 2019
- 2019
- [c3]Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. NeurIPS 2019: 13132-13143 - [c2]Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson:
Function-Space Distributions over Kernels. NeurIPS 2019: 14939-14950 - [c1]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. UAI 2019: 1169-1179 - [i3]Wesley J. Maddox, Timur Garipov, Pavel Izmailov, Dmitry P. Vetrov, Andrew Gordon Wilson:
A Simple Baseline for Bayesian Uncertainty in Deep Learning. CoRR abs/1902.02476 (2019) - [i2]Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson:
Subspace Inference for Bayesian Deep Learning. CoRR abs/1907.07504 (2019) - [i1]Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson:
Function-Space Distributions over Kernels. CoRR abs/1910.13565 (2019)
Coauthor Index
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