Timezone: »
We present a new method for evaluating and training unnormalized density models. Our approach only requires access to the gradient of the unnormalized model’s log-density. We estimate the Stein discrepancy between the data density p(x) and the model density q(x) based on a vector function of the data. We parameterize this function with a neural network and fit its parameters to maximize this discrepancy. This yields a novel goodness-of-fit test which outperforms existing methods on high dimensional data. Furthermore, optimizing q(x) to minimize this discrepancy produces a novel method for training unnormalized models. This training method can fit large unnormalized models faster than existing approaches. The ability to both learn and compare models is a unique feature of the proposed method.
Author Information
Will Grathwohl (University of Toronto)
Kuan-Chieh Wang (University of Toronto)
Joern-Henrik Jacobsen (Apple Inc.)
David Duvenaud (University of Toronto)
Richard Zemel (Vector Institute)
More from the Same Authors
-
2021 : Online Algorithmic Recourse by Collective Action »
Elliot Creager · Richard Zemel -
2022 : Towards Environment-Invariant Representation Learning for Robust Task Transfer »
Benjamin Eyre · Richard Zemel · Elliot Creager -
2023 : Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift »
Benjamin Eyre · Elliot Creager · David Madras · Vardan Papyan · Richard Zemel -
2023 Test Of Time: Learning Fair Representations »
Richard Zemel · Yu Wu · Kevin Swersky · Toniann Pitassi · Cynthia Dwork -
2022 : Invited talks 3, Q/A, Amy, Rich and Liting »
Liting Sun · Amy Zhang · Richard Zemel -
2022 : Invited talks 3, Amy Zhang, Rich Zemel and Liting Sun »
Amy Zhang · Richard Zemel · Liting Sun -
2022 Poster: On Implicit Bias in Overparameterized Bilevel Optimization »
Paul Vicol · Jonathan Lorraine · Fabian Pedregosa · David Duvenaud · Roger Grosse -
2022 Spotlight: On Implicit Bias in Overparameterized Bilevel Optimization »
Paul Vicol · Jonathan Lorraine · Fabian Pedregosa · David Duvenaud · Roger Grosse -
2021 : David Duvenaud »
David Duvenaud -
2021 Poster: SketchEmbedNet: Learning Novel Concepts by Imitating Drawings »
Alexander Wang · Mengye Ren · Richard Zemel -
2021 Poster: Learning a Universal Template for Few-shot Dataset Generalization »
Eleni Triantafillou · Hugo Larochelle · Richard Zemel · Vincent Dumoulin -
2021 Poster: Environment Inference for Invariant Learning »
Elliot Creager · Joern-Henrik Jacobsen · Richard Zemel -
2021 Spotlight: Learning a Universal Template for Few-shot Dataset Generalization »
Eleni Triantafillou · Hugo Larochelle · Richard Zemel · Vincent Dumoulin -
2021 Spotlight: Environment Inference for Invariant Learning »
Elliot Creager · Joern-Henrik Jacobsen · Richard Zemel -
2021 Spotlight: SketchEmbedNet: Learning Novel Concepts by Imitating Drawings »
Alexander Wang · Mengye Ren · Richard Zemel -
2021 Poster: Out-of-Distribution Generalization via Risk Extrapolation (REx) »
David Krueger · Ethan Caballero · Joern-Henrik Jacobsen · Amy Zhang · Jonathan Binas · Dinghuai Zhang · Remi Le Priol · Aaron Courville -
2021 Oral: Out-of-Distribution Generalization via Risk Extrapolation (REx) »
David Krueger · Ethan Caballero · Joern-Henrik Jacobsen · Amy Zhang · Jonathan Binas · Dinghuai Zhang · Remi Le Priol · Aaron Courville -
2021 Poster: Oops I Took A Gradient: Scalable Sampling for Discrete Distributions »
Will Grathwohl · Kevin Swersky · Milad Hashemi · David Duvenaud · Chris Maddison -
2021 Poster: On Monotonic Linear Interpolation of Neural Network Parameters »
James Lucas · Juhan Bae · Michael Zhang · Stanislav Fort · Richard Zemel · Roger Grosse -
2021 Spotlight: On Monotonic Linear Interpolation of Neural Network Parameters »
James Lucas · Juhan Bae · Michael Zhang · Stanislav Fort · Richard Zemel · Roger Grosse -
2021 Oral: Oops I Took A Gradient: Scalable Sampling for Discrete Distributions »
Will Grathwohl · Kevin Swersky · Milad Hashemi · David Duvenaud · Chris Maddison -
2020 : Invited Talk 4: Prof. Richard Zemel from University of Toronto »
Richard Zemel -
2020 Workshop: Participatory Approaches to Machine Learning »
Angela Zhou · David Madras · Deborah Raji · Smitha Milli · Bogdan Kulynych · Richard Zemel -
2020 Poster: How to Train Your Neural ODE: the World of Jacobian and Kinetic Regularization »
Chris Finlay · Joern-Henrik Jacobsen · Levon Nurbekyan · Adam Oberman -
2020 Poster: Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations »
Florian Tramer · Jens Behrmann · Nicholas Carlini · Nicolas Papernot · Joern-Henrik Jacobsen -
2020 Poster: Causal Modeling for Fairness In Dynamical Systems »
Elliot Creager · David Madras · Toniann Pitassi · Richard Zemel -
2020 Poster: Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach »
Martin Mladenov · Elliot Creager · Omer Ben-Porat · Kevin Swersky · Richard Zemel · Craig Boutilier -
2019 Workshop: Learning and Reasoning with Graph-Structured Representations »
Ethan Fetaya · Zhiting Hu · Thomas Kipf · Yujia Li · Xiaodan Liang · Renjie Liao · Raquel Urtasun · Hao Wang · Max Welling · Eric Xing · Richard Zemel -
2019 : Invertible Residual Networks and a Novel Perspective on Adversarial Examples »
Joern-Henrik Jacobsen -
2019 Poster: Lorentzian Distance Learning for Hyperbolic Representations »
Marc Law · Renjie Liao · Jake Snell · Richard Zemel -
2019 Poster: Flexibly Fair Representation Learning by Disentanglement »
Elliot Creager · David Madras · Joern-Henrik Jacobsen · Marissa Weis · Kevin Swersky · Toniann Pitassi · Richard Zemel -
2019 Oral: Lorentzian Distance Learning for Hyperbolic Representations »
Marc Law · Renjie Liao · Jake Snell · Richard Zemel -
2019 Oral: Flexibly Fair Representation Learning by Disentanglement »
Elliot Creager · David Madras · Joern-Henrik Jacobsen · Marissa Weis · Kevin Swersky · Toniann Pitassi · Richard Zemel -
2019 Poster: Understanding the Origins of Bias in Word Embeddings »
Marc-Etienne Brunet · Colleen Alkalay-Houlihan · Ashton Anderson · Richard Zemel -
2019 Poster: Invertible Residual Networks »
Jens Behrmann · Will Grathwohl · Ricky T. Q. Chen · David Duvenaud · Joern-Henrik Jacobsen -
2019 Oral: Understanding the Origins of Bias in Word Embeddings »
Marc-Etienne Brunet · Colleen Alkalay-Houlihan · Ashton Anderson · Richard Zemel -
2019 Oral: Invertible Residual Networks »
Jens Behrmann · Will Grathwohl · Ricky T. Q. Chen · David Duvenaud · Joern-Henrik Jacobsen -
2018 Poster: Learning Adversarially Fair and Transferable Representations »
David Madras · Elliot Creager · Toniann Pitassi · Richard Zemel -
2018 Oral: Learning Adversarially Fair and Transferable Representations »
David Madras · Elliot Creager · Toniann Pitassi · Richard Zemel -
2018 Poster: Reviving and Improving Recurrent Back-Propagation »
Renjie Liao · Yuwen Xiong · Ethan Fetaya · Lisa Zhang · Kijung Yoon · Zachary S Pitkow · Raquel Urtasun · Richard Zemel -
2018 Poster: Noisy Natural Gradient as Variational Inference »
Guodong Zhang · Shengyang Sun · David Duvenaud · Roger Grosse -
2018 Poster: Distilling the Posterior in Bayesian Neural Networks »
Kuan-Chieh Wang · Paul Vicol · James Lucas · Li Gu · Roger Grosse · Richard Zemel -
2018 Oral: Noisy Natural Gradient as Variational Inference »
Guodong Zhang · Shengyang Sun · David Duvenaud · Roger Grosse -
2018 Oral: Distilling the Posterior in Bayesian Neural Networks »
Kuan-Chieh Wang · Paul Vicol · James Lucas · Li Gu · Roger Grosse · Richard Zemel -
2018 Oral: Reviving and Improving Recurrent Back-Propagation »
Renjie Liao · Yuwen Xiong · Ethan Fetaya · Lisa Zhang · Kijung Yoon · Zachary S Pitkow · Raquel Urtasun · Richard Zemel -
2018 Poster: Neural Relational Inference for Interacting Systems »
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel -
2018 Poster: Inference Suboptimality in Variational Autoencoders »
Chris Cremer · Xuechen Li · David Duvenaud -
2018 Oral: Inference Suboptimality in Variational Autoencoders »
Chris Cremer · Xuechen Li · David Duvenaud -
2018 Oral: Neural Relational Inference for Interacting Systems »
Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel -
2017 Poster: Deep Spectral Clustering Learning »
Marc Law · Raquel Urtasun · Richard Zemel -
2017 Talk: Deep Spectral Clustering Learning »
Marc Law · Raquel Urtasun · Richard Zemel