You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 6 Feb 2024 • Nate Gruver, Anuroop Sriram, Andrea Madotto, Andrew Gordon Wilson, C. Lawrence Zitnick, Zachary Ulissi

We propose fine-tuning large language models for generation of stable materials.

no code implementations • 1 Feb 2024 • Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, Jose Miguel Hernandez Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.

1 code implementation • 28 Dec 2023 • Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson

In this work, we approach regularization in neural networks from a probabilistic perspective and show that by viewing parameter-space regularization as specifying an empirical prior distribution over the model parameters, we can derive a probabilistically well-motivated regularization technique that allows explicitly encoding information about desired predictive functions into neural network training.

1 code implementation • NeurIPS 2023 • Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson

Vision-language pretrained models have seen remarkable success, but their application to safety-critical settings is limited by their lack of interpretability.

no code implementations • 28 Dec 2023 • Sanae Lotfi, Marc Finzi, Yilun Kuang, Tim G. J. Rudner, Micah Goldblum, Andrew Gordon Wilson

Modern language models can contain billions of parameters, raising the question of whether they can generalize beyond the training data or simply regurgitate their training corpora.

no code implementations • 7 Dec 2023 • Micah Goldblum, Anima Anandkumar, Richard Baraniuk, Tom Goldstein, Kyunghyun Cho, Zachary C Lipton, Melanie Mitchell, Preetum Nakkiran, Max Welling, Andrew Gordon Wilson

The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time.

no code implementations • 5 Dec 2023 • Yanjun Liu, Milena Jovanovic, Krishnanand Mallayya, Wesley J. Maddox, Andrew Gordon Wilson, Sebastian Klemenz, Leslie M. Schoop, Eun-Ah Kim

The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space.

1 code implementation • NeurIPS 2023 • Ravid Shwartz-Ziv, Micah Goldblum, Yucen Lily Li, C. Bayan Bruss, Andrew Gordon Wilson

Real-world datasets are often highly class-imbalanced, which can adversely impact the performance of deep learning models.

1 code implementation • NeurIPS 2023 • Shikai Qiu, Tim G. J. Rudner, Sanyam Kapoor, Andrew Gordon Wilson

Moreover, the most likely parameters under the parameter posterior do not generally correspond to the most likely function induced by the parameter posterior.

2 code implementations • NeurIPS 2023 • Micah Goldblum, Hossein Souri, Renkun Ni, Manli Shu, Viraj Prabhu, Gowthami Somepalli, Prithvijit Chattopadhyay, Mark Ibrahim, Adrien Bardes, Judy Hoffman, Rama Chellappa, Andrew Gordon Wilson, Tom Goldstein

Battle of the Backbones (BoB) makes this choice easier by benchmarking a diverse suite of pretrained models, including vision-language models, those trained via self-supervised learning, and the Stable Diffusion backbone, across a diverse set of computer vision tasks ranging from classification to object detection to OOD generalization and more.

1 code implementation • NeurIPS 2023 • Nate Gruver, Marc Finzi, Shikai Qiu, Andrew Gordon Wilson

By encoding time series as a string of numerical digits, we can frame time series forecasting as next-token prediction in text.

1 code implementation • NeurIPS 2023 • Andres Potapczynski, Marc Finzi, Geoff Pleiss, Andrew Gordon Wilson

In this paper, we propose a simple but general framework for large-scale linear algebra problems in machine learning, named CoLA (Compositional Linear Algebra).

1 code implementation • 19 Jun 2023 • Shikai Qiu, Andres Potapczynski, Pavel Izmailov, Andrew Gordon Wilson

A major challenge to out-of-distribution generalization is reliance on spurious features -- patterns that are predictive of the class label in the training data distribution, but not causally related to the target.

no code implementations • 13 Jun 2023 • Marc Finzi, Anudhyan Boral, Andrew Gordon Wilson, Fei Sha, Leonardo Zepeda-Núñez

In this work, we develop a probabilistic approximation scheme for the conditional score function which provably converges to the true distribution as the noise level decreases.

1 code implementation • NeurIPS 2023 • Nate Gruver, Samuel Stanton, Nathan C. Frey, Tim G. J. Rudner, Isidro Hotzel, Julien Lafrance-Vanasse, Arvind Rajpal, Kyunghyun Cho, Andrew Gordon Wilson

A popular approach to protein design is to combine a generative model with a discriminative model for conditional sampling.

2 code implementations • 31 May 2023 • Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson

Bayesian optimization is a highly efficient approach to optimizing objective functions which are expensive to query.

1 code implementation • 21 May 2023 • Rami Aly, Xingjian Shi, Kaixiang Lin, Aston Zhang, Andrew Gordon Wilson

We observe, in the context of classification tasks, that instruction finetuned language models exhibit remarkable prompt robustness, and we subsequently propose a simple method to eliminate the need for handcrafted prompts, named AuT-Few.

1 code implementation • 28 Apr 2023 • Marc Finzi, Andres Potapczynski, Matthew Choptuik, Andrew Gordon Wilson

Unlike conventional grid and mesh based methods for solving partial differential equations (PDEs), neural networks have the potential to break the curse of dimensionality, providing approximate solutions to problems where using classical solvers is difficult or impossible.

no code implementations • 24 Apr 2023 • Randall Balestriero, Mark Ibrahim, Vlad Sobal, Ari Morcos, Shashank Shekhar, Tom Goldstein, Florian Bordes, Adrien Bardes, Gregoire Mialon, Yuandong Tian, Avi Schwarzschild, Andrew Gordon Wilson, Jonas Geiping, Quentin Garrido, Pierre Fernandez, Amir Bar, Hamed Pirsiavash, Yann Lecun, Micah Goldblum

Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning.

1 code implementation • 11 Apr 2023 • Micah Goldblum, Marc Finzi, Keefer Rowan, Andrew Gordon Wilson

No free lunch theorems for supervised learning state that no learner can solve all problems or that all learners achieve exactly the same accuracy on average over a uniform distribution on learning problems.

1 code implementation • 8 Feb 2023 • Gianluca Detommaso, Alberto Gasparin, Michele Donini, Matthias Seeger, Andrew Gordon Wilson, Cedric Archambeau

We present Fortuna, an open-source library for uncertainty quantification in deep learning.

1 code implementation • 29 Dec 2022 • Zichang Liu, Zhiqiang Tang, Xingjian Shi, Aston Zhang, Mu Li, Anshumali Shrivastava, Andrew Gordon Wilson

The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems.

1 code implementation • 13 Dec 2022 • Amin Ghiasi, Hamid Kazemi, Eitan Borgnia, Steven Reich, Manli Shu, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein

In addition, we show that ViTs maintain spatial information in all layers except the final layer.

no code implementations • 28 Nov 2022 • Wanqian Yang, Polina Kirichenko, Micah Goldblum, Andrew Gordon Wilson

Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure.

1 code implementation • 24 Nov 2022 • Sanae Lotfi, Marc Finzi, Sanyam Kapoor, Andres Potapczynski, Micah Goldblum, Andrew Gordon Wilson

While there has been progress in developing non-vacuous generalization bounds for deep neural networks, these bounds tend to be uninformative about why deep learning works.

no code implementations • 23 Oct 2022 • Renkun Ni, Ping-Yeh Chiang, Jonas Geiping, Micah Goldblum, Andrew Gordon Wilson, Tom Goldstein

Sharpness-Aware Minimization (SAM) has recently emerged as a robust technique for improving the accuracy of deep neural networks.

1 code implementation • 22 Oct 2022 • Samuel Stanton, Wesley Maddox, Andrew Gordon Wilson

Bayesian optimization is a coherent, ubiquitous approach to decision-making under uncertainty, with applications including multi-arm bandits, active learning, and black-box optimization.

1 code implementation • 20 Oct 2022 • Pavel Izmailov, Polina Kirichenko, Nate Gruver, Andrew Gordon Wilson

Deep classifiers are known to rely on spurious features $\unicode{x2013}$ patterns which are correlated with the target on the training data but not inherently relevant to the learning problem, such as the image backgrounds when classifying the foregrounds.

1 code implementation • 12 Oct 2022 • Jonas Geiping, Micah Goldblum, Gowthami Somepalli, Ravid Shwartz-Ziv, Tom Goldstein, Andrew Gordon Wilson

Despite the clear performance benefits of data augmentations, little is known about why they are so effective.

1 code implementation • 6 Oct 2022 • Nate Gruver, Marc Finzi, Micah Goldblum, Andrew Gordon Wilson

In order to better understand the role of equivariance in recent vision models, we introduce the Lie derivative, a method for measuring equivariance with strong mathematical foundations and minimal hyperparameters.

1 code implementation • 14 Jul 2022 • Wesley J. Maddox, Andres Potapczynski, Andrew Gordon Wilson

Low-precision arithmetic has had a transformative effect on the training of neural networks, reducing computation, memory and energy requirements.

1 code implementation • 13 Jul 2022 • Gregory Benton, Wesley J. Maddox, Andrew Gordon Wilson

A broad class of stochastic volatility models are defined by systems of stochastic differential equations.

1 code implementation • 30 Jun 2022 • Roman Levin, Valeriia Cherepanova, Avi Schwarzschild, Arpit Bansal, C. Bayan Bruss, Tom Goldstein, Andrew Gordon Wilson, Micah Goldblum

In this work, we demonstrate that upstream data gives tabular neural networks a decisive advantage over widely used GBDT models.

1 code implementation • 20 Jun 2022 • Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa

While low-precision optimization has been widely used to accelerate deep learning, low-precision sampling remains largely unexplored.

1 code implementation • 20 May 2022 • Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor, Chen Zhu, Yann Lecun, Andrew Gordon Wilson

Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task.

4 code implementations • 6 Apr 2022 • Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson

Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions.

Ranked #1 on Out-of-Distribution Generalization on UrbanCars

1 code implementation • 30 Mar 2022 • Sanyam Kapoor, Wesley J. Maddox, Pavel Izmailov, Andrew Gordon Wilson

In Bayesian regression, we often use a Gaussian observation model, where we control the level of aleatoric uncertainty with a noise variance parameter.

1 code implementation • 23 Mar 2022 • Samuel Stanton, Wesley Maddox, Nate Gruver, Phillip Maffettone, Emily Delaney, Peyton Greenside, Andrew Gordon Wilson

Bayesian optimization (BayesOpt) is a gold standard for query-efficient continuous optimization.

1 code implementation • 23 Feb 2022 • Sanae Lotfi, Pavel Izmailov, Gregory Benton, Micah Goldblum, Andrew Gordon Wilson

We provide a partial remedy through a conditional marginal likelihood, which we show is more aligned with generalization, and practically valuable for large-scale hyperparameter learning, such as in deep kernel learning.

1 code implementation • ICLR 2022 • Nate Gruver, Marc Finzi, Samuel Stanton, Andrew Gordon Wilson

Physics-inspired neural networks (NNs), such as Hamiltonian or Lagrangian NNs, dramatically outperform other learned dynamics models by leveraging strong inductive biases.

1 code implementation • 31 Dec 2021 • Wesley J. Maddox, Sanyam Kapoor, Andrew Gordon Wilson

While recent work on conjugate gradient methods and Lanczos decompositions have achieved scalable Gaussian process inference with highly accurate point predictions, in several implementations these iterative methods appear to struggle with numerical instabilities in learning kernel hyperparameters, and poor test likelihoods.

1 code implementation • NeurIPS 2021 • Marc Finzi, Gregory Benton, Andrew Gordon Wilson

There is often a trade-off between building deep learning systems that are expressive enough to capture the nuances of the reality, and having the right inductive biases for efficient learning.

1 code implementation • NeurIPS 2021 • Wesley J. Maddox, Samuel Stanton, Andrew Gordon Wilson

With a principled representation of uncertainty and closed form posterior updates, Gaussian processes (GPs) are a natural choice for online decision making.

no code implementations • ICML Workshop INNF 2021 • Polina Kirichenko, Mehrdad Farajtabar, Dushyant Rao, Balaji Lakshminarayanan, Nir Levine, Ang Li, Huiyi Hu, Andrew Gordon Wilson, Razvan Pascanu

Learning new tasks continuously without forgetting on a constantly changing data distribution is essential for real-world problems but extremely challenging for modern deep learning.

2 code implementations • NeurIPS 2021 • Wesley J. Maddox, Maximilian Balandat, Andrew Gordon Wilson, Eytan Bakshy

However, the Gaussian Process (GP) models typically used as probabilistic surrogates for multi-task Bayesian Optimization scale poorly with the number of outcomes, greatly limiting applicability.

1 code implementation • NeurIPS 2021 • Pavel Izmailov, Patrick Nicholson, Sanae Lotfi, Andrew Gordon Wilson

Approximate Bayesian inference for neural networks is considered a robust alternative to standard training, often providing good performance on out-of-distribution data.

1 code implementation • 12 Jun 2021 • Sanyam Kapoor, Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

State-of-the-art methods for scalable Gaussian processes use iterative algorithms, requiring fast matrix vector multiplies (MVMs) with the covariance kernel.

1 code implementation • NeurIPS 2021 • Samuel Stanton, Pavel Izmailov, Polina Kirichenko, Alexander A. Alemi, Andrew Gordon Wilson

Knowledge distillation is a popular technique for training a small student network to emulate a larger teacher model, such as an ensemble of networks.

2 code implementations • 10 Jun 2021 • Shengyang Sun, Jiaxin Shi, Andrew Gordon Wilson, Roger Grosse

We introduce a new scalable variational Gaussian process approximation which provides a high fidelity approximation while retaining general applicability.

3 code implementations • 29 Apr 2021 • Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, Andrew Gordon Wilson

The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex.

4 code implementations • 19 Apr 2021 • Marc Finzi, Max Welling, Andrew Gordon Wilson

Symmetries and equivariance are fundamental to the generalization of neural networks on domains such as images, graphs, and point clouds.

2 code implementations • 2 Mar 2021 • Samuel Stanton, Wesley J. Maddox, Ian Delbridge, Andrew Gordon Wilson

Gaussian processes (GPs) provide a gold standard for performance in online settings, such as sample-efficient control and black box optimization, where we need to update a posterior distribution as we acquire data in a sequential fashion.

1 code implementation • 2 Mar 2021 • Wesley J. Maddox, Shuai Tang, Pablo Garcia Moreno, Andrew Gordon Wilson, Andreas Damianou

The inductive biases of trained neural networks are difficult to understand and, consequently, to adapt to new settings.

1 code implementation • 25 Feb 2021 • Gregory W. Benton, Wesley J. Maddox, Sanae Lotfi, Andrew Gordon Wilson

In this paper, we show that there are mode-connecting simplicial complexes that form multi-dimensional manifolds of low loss, connecting many independently trained models.

no code implementations • 1 Jan 2021 • Gregory Benton, Wesley Maddox, Andrew Gordon Wilson

Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity.

no code implementations • NeurIPS 2020 • Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

Invariances to translations have imbued convolutional neural networks with powerful generalization properties.

1 code implementation • NeurIPS 2020 • Marc Finzi, Ke Alexander Wang, Andrew Gordon Wilson

Reasoning about the physical world requires models that are endowed with the right inductive biases to learn the underlying dynamics.

1 code implementation • 22 Oct 2020 • Gregory Benton, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

Invariances to translations have imbued convolutional neural networks with powerful generalization properties.

1 code implementation • 28 Aug 2020 • Brandon Amos, Samuel Stanton, Denis Yarats, Andrew Gordon Wilson

For over a decade, model-based reinforcement learning has been seen as a way to leverage control-based domain knowledge to improve the sample-efficiency of reinforcement learning agents.

1 code implementation • NeurIPS 2020 • Polina Kirichenko, Pavel Izmailov, Andrew Gordon Wilson

Detecting out-of-distribution (OOD) data is crucial for robust machine learning systems.

1 code implementation • NeurIPS 2020 • Yue Wu, Pan Zhou, Andrew Gordon Wilson, Eric P. Xing, Zhiting Hu

Despite success on a wide range of problems related to vision, generative adversarial networks (GANs) often suffer from inferior performance due to unstable training, especially for text generation.

Ranked #2 on Text Generation on EMNLP2017 WMT

1 code implementation • 4 Mar 2020 • Wesley J. Maddox, Gregory Benton, Andrew Gordon Wilson

Neural networks appear to have mysterious generalization properties when using parameter counting as a proxy for complexity.

2 code implementations • ICML 2020 • Marc Finzi, Samuel Stanton, Pavel Izmailov, Andrew Gordon Wilson

The translation equivariance of convolutional layers enables convolutional neural networks to generalize well on image problems.

1 code implementation • NeurIPS 2020 • Andrew Gordon Wilson, Pavel Izmailov

The key distinguishing property of a Bayesian approach is marginalization, rather than using a single setting of weights.

no code implementations • 29 Jan 2020 • Andrew Gordon Wilson

(3) The structure of neural networks gives rise to a structured prior in function space, which reflects the inductive biases of neural networks that help them generalize.

no code implementations • ICLR 2020 • Diego Granziol, Timur Garipov, Dmitry Vetrov, Stefan Zohren, Stephen Roberts, Andrew Gordon Wilson

This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.

2 code implementations • ICML 2020 • Pavel Izmailov, Polina Kirichenko, Marc Finzi, Andrew Gordon Wilson

Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood.

Semi-Supervised Image Classification Semi-Supervised Text Classification

1 code implementation • ICML 2020 • Ian A. Delbridge, David S. Bindel, Andrew Gordon Wilson

Surprisingly, we find that as the number of random projections increases, the predictive performance of this approach quickly converges to the performance of a kernel operating on the original full dimensional inputs, over a wide range of data sets, even if we are projecting into a single dimension.

1 code implementation • NeurIPS 2019 • Gregory W. Benton, Wesley J. Maddox, Jayson P. Salkey, Julio Albinati, Andrew Gordon Wilson

The resulting approach enables learning of rich representations, with support for any stationary kernel, uncertainty over the values of the kernel, and an interpretable specification of a prior directly over kernels, without requiring sophisticated initialization or manual intervention.

2 code implementations • NeurIPS 2020 • Maximilian Balandat, Brian Karrer, Daniel R. Jiang, Samuel Daulton, Benjamin Letham, Andrew Gordon Wilson, Eytan Bakshy

Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.

1 code implementation • 17 Jul 2019 • Pavel Izmailov, Wesley J. Maddox, Polina Kirichenko, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

Bayesian inference was once a gold standard for learning with neural networks, providing accurate full predictive distributions and well calibrated uncertainty.

4 code implementations • ICLR 2019 • Chuan Guo, Jacob R. Gardner, Yurong You, Andrew Gordon Wilson, Kilian Q. Weinberger

We propose an intriguingly simple method for the construction of adversarial images in the black-box setting.

2 code implementations • 26 Apr 2019 • Guandao Yang, Tianyi Zhang, Polina Kirichenko, Junwen Bai, Andrew Gordon Wilson, Christopher De Sa

Low precision operations can provide scalability, memory savings, portability, and energy efficiency.

no code implementations • 29 Mar 2019 • Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Jennifer Chayes, Eric Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim Hazelwood, Furong Huang, Martin Jaggi, Kevin Jamieson, Michael. I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konečný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Aparna Lakshmiratan, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Murray, Kunle Olukotun, Dimitris Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar

Machine learning (ML) techniques are enjoying rapidly increasing adoption.

3 code implementations • NeurIPS 2019 • Ke Alexander Wang, Geoff Pleiss, Jacob R. Gardner, Stephen Tyree, Kilian Q. Weinberger, Andrew Gordon Wilson

Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows with the available data.

no code implementations • 12 Mar 2019 • Jian Wu, Saul Toscano-Palmerin, Peter I. Frazier, Andrew Gordon Wilson

Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck.

4 code implementations • ICLR 2020 • Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson

The posteriors over neural network weights are high dimensional and multimodal.

8 code implementations • NeurIPS 2019 • Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson

We propose SWA-Gaussian (SWAG), a simple, scalable, and general purpose approach for uncertainty representation and calibration in deep learning.

1 code implementation • NeurIPS 2018 • David Eriksson, Kun Dong, Eric Hans Lee, David Bindel, Andrew Gordon Wilson

Gaussian processes (GPs) with derivatives are useful in many applications, including Bayesian optimization, implicit surface reconstruction, and terrain reconstruction.

no code implementations • 28 Oct 2018 • William Herlands, Daniel B. Neill, Hannes Nickisch, Andrew Gordon Wilson

We provide a model-agnostic formalization of change surfaces, illustrating how they can provide variable, heterogeneous, and non-monotonic rates of change across multiple dimensions.

4 code implementations • NeurIPS 2018 • Jacob R. Gardner, Geoff Pleiss, David Bindel, Kilian Q. Weinberger, Andrew Gordon Wilson

Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware.

2 code implementations • ICLR 2019 • Ben Athiwaratkun, Marc Finzi, Pavel Izmailov, Andrew Gordon Wilson

Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters.

1 code implementation • ACL 2018 • Ben Athiwaratkun, Andrew Gordon Wilson, Anima Anandkumar

We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information.

2 code implementations • ICLR 2018 • Ben Athiwaratkun, Andrew Gordon Wilson

By representing words with probability densities rather than point vectors, probabilistic word embeddings can capture rich and interpretable semantic information and uncertainty.

Ranked #2 on Lexical Entailment on HyperLex

no code implementations • 4 Apr 2018 • William Herlands, Edward McFowland III, Andrew Gordon Wilson, Daniel B. Neill

We introduce methods for identifying anomalous patterns in non-iid data by combining Gaussian processes with novel log-likelihood ratio statistic and subset scanning techniques.

1 code implementation • ICML 2018 • Geoff Pleiss, Jacob R. Gardner, Kilian Q. Weinberger, Andrew Gordon Wilson

One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions.

15 code implementations • 14 Mar 2018 • Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, Andrew Gordon Wilson

Deep neural networks are typically trained by optimizing a loss function with an SGD variant, in conjunction with a decaying learning rate, until convergence.

Ranked #78 on Image Classification on CIFAR-100 (using extra training data)

10 code implementations • NeurIPS 2018 • Timur Garipov, Pavel Izmailov, Dmitrii Podoprikhin, Dmitry Vetrov, Andrew Gordon Wilson

The loss functions of deep neural networks are complex and their geometric properties are not well understood.

1 code implementation • 24 Feb 2018 • Jacob R. Gardner, Geoff Pleiss, Ruihan Wu, Kilian Q. Weinberger, Andrew Gordon Wilson

Recent work shows that inference for Gaussian processes can be performed efficiently using iterative methods that rely only on matrix-vector multiplications (MVMs).

1 code implementation • 2 Feb 2018 • Phillip A. Jang, Andrew E. Loeb, Matthew B. Davidow, Andrew Gordon Wilson

We propose a distribution over kernels formed by modelling a spectral mixture density with a L\'evy process.

no code implementations • 27 Nov 2017 • Andrew Gordon Wilson, Jason Yosinski, Patrice Simard, Rich Caruana, William Herlands

This is the Proceedings of NIPS 2017 Symposium on Interpretable Machine Learning, held in Long Beach, California, USA on December 7, 2017

3 code implementations • NeurIPS 2017 • Kun Dong, David Eriksson, Hannes Nickisch, David Bindel, Andrew Gordon Wilson

For applications as varied as Bayesian neural networks, determinantal point processes, elliptical graphical models, and kernel learning for Gaussian processes (GPs), one must compute a log determinant of an $n \times n$ positive definite matrix, and its derivatives - leading to prohibitive $\mathcal{O}(n^3)$ computations.

4 code implementations • NeurIPS 2017 • Yunus Saatchi, Andrew Gordon Wilson

Generative adversarial networks (GANs) can implicitly learn rich distributions over images, audio, and data which are hard to model with an explicit likelihood.

2 code implementations • ACL 2017 • Ben Athiwaratkun, Andrew Gordon Wilson

Word embeddings provide point representations of words containing useful semantic information.

1 code implementation • NeurIPS 2017 • Jian Wu, Matthias Poloczek, Andrew Gordon Wilson, Peter I. Frazier

Bayesian optimization has been successful at global optimization of expensive-to-evaluate multimodal objective functions.

no code implementations • 28 Nov 2016 • Andrew Gordon Wilson, Been Kim, William Herlands

This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016

no code implementations • NeurIPS 2016 • Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

We propose a novel deep kernel learning model and stochastic variational inference procedure which generalizes deep kernel learning approaches to enable classification, multi-task learning, additive covariance structures, and stochastic gradient training.

2 code implementations • 27 Oct 2016 • Maruan Al-Shedivat, Andrew Gordon Wilson, Yunus Saatchi, Zhiting Hu, Eric P. Xing

To model such structure, we propose expressive closed-form kernel functions for Gaussian processes.

5 code implementations • 6 Nov 2015 • Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric P. Xing

We introduce scalable deep kernels, which combine the structural properties of deep learning architectures with the non-parametric flexibility of kernel methods.

3 code implementations • 5 Nov 2015 • Andrew Gordon Wilson, Christoph Dann, Hannes Nickisch

This multi-level circulant approximation allows one to unify the orthogonal computational benefits of fast Kronecker and Toeplitz approaches, and is significantly faster than either approach in isolation; 2) local kernel interpolation and inducing points to allow for arbitrarily located data inputs, and $O(1)$ test time predictions; 3) exploiting block-Toeplitz Toeplitz-block structure (BTTB), which enables fast inference and learning when multidimensional Kronecker structure is not present; and 4) projections of the input space to flexibly model correlated inputs and high dimensional data.

no code implementations • NeurIPS 2015 • Andrew Gordon Wilson, Christoph Dann, Christopher G. Lucas, Eric P. Xing

Bayesian nonparametric models, such as Gaussian processes, provide a compelling framework for automatic statistical modelling: these models have a high degree of flexibility, and automatically calibrated complexity.

3 code implementations • 3 Mar 2015 • Andrew Gordon Wilson, Hannes Nickisch

We introduce a new structured kernel interpolation (SKI) framework, which generalises and unifies inducing point methods for scalable Gaussian processes (GPs).

no code implementations • 19 Dec 2014 • Zichao Yang, Alexander J. Smola, Le Song, Andrew Gordon Wilson

Kernel methods have great promise for learning rich statistical representations of large modern datasets.

no code implementations • 18 Feb 2014 • Amar Shah, Andrew Gordon Wilson, Zoubin Ghahramani

We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions.

no code implementations • 14 Feb 2014 • Andrew Gordon Wilson, Yuting Wu, Daniel J. Holland, Sebastian Nowozin, Mick D. Mantle, Lynn F. Gladden, Andrew Blake

Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules.

no code implementations • 20 Oct 2013 • Andrew Gordon Wilson, Elad Gilboa, Arye Nehorai, John P. Cunningham

We introduce a new Bayesian nonparametric framework -- GPatt -- enabling automatic pattern extrapolation with Gaussian processes on large multidimensional datasets.

2 code implementations • 18 Feb 2013 • Andrew Gordon Wilson, Ryan Prescott Adams

Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation.

1 code implementation • 19 Oct 2011 • Andrew Gordon Wilson, David A. Knowles, Zoubin Ghahramani

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes.

no code implementations • 31 Dec 2010 • Andrew Gordon Wilson, Zoubin Ghahramani

We introduce a stochastic process with Wishart marginals: the generalised Wishart process (GWP).

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.