`

Timezone: »

 
Poster
Learning to Pivot with Adversarial Networks
Gilles Louppe · Michael Kagan · Kyle Cranmer

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #105 #None

Several techniques for domain adaptation have been proposed to account for differences in the distribution of the data used for training and testing. The majority of this work focuses on a binary domain label. Similar problems occur in a scientific context where there may be a continuous family of plausible data generation processes associated to the presence of systematic uncertainties. Robust inference is possible if it is based on a pivot -- a quantity whose distribution does not depend on the unknown values of the nuisance parameters that parametrize this family of data generation processes. In this work, we introduce and derive theoretical results for a training procedure based on adversarial networks for enforcing the pivotal property (or, equivalently, fairness with respect to continuous attributes) on a predictive model. The method includes a hyperparameter to control the trade-off between accuracy and robustness. We demonstrate the effectiveness of this approach with a toy example and examples from particle physics.

Author Information

Gilles Louppe (New York University)
Michael Kagan (SLAC / Stanford)
Kyle Cranmer (New York University)

Kyle Cranmer is an Associate Professor of Physics at New York University and affiliated with NYU's Center for Data Science. He is an experimental particle physicists working, primarily, on the Large Hadron Collider, based in Geneva, Switzerland. He was awarded the Presidential Early Career Award for Science and Engineering in 2007 and the National Science Foundation's Career Award in 2009. Professor Cranmer developed a framework that enables collaborative statistical modeling, which was used extensively for the discovery of the Higgs boson in July, 2012. His current interests are at the intersection of physics and machine learning and include inference in the context of intractable likelihoods, development of machine learning models imbued with physics knowledge, adversarial training for robustness to systematic uncertainty, the use of generative models in the physical sciences, and integration of reproducible workflows in the inference pipeline.

More from the Same Authors

  • 2021 : Characterizing γ-ray maps of the Galactic Center with neural density estimation »
    Siddharth Mishra-Sharma · Kyle Cranmer
  • 2021 : The Quantum Trellis: A classical algorithm for sampling the parton shower with interference effects »
    Sebastian Macaluso · Kyle Cranmer
  • 2021 : Kyle Cranmer »
    Kyle Cranmer
  • 2021 Workshop: Machine Learning and the Physical Sciences »
    Anima Anandkumar · Kyle Cranmer · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Emine Kucukbenli · Gilles Louppe · Benjamin Nachman · Brian Nord · Savannah Thais
  • 2020 Workshop: Machine Learning and the Physical Sciences »
    Anima Anandkumar · Kyle Cranmer · Shirley Ho · Mr. Prabhat · Lenka Zdeborová · Atilim Gunes Baydin · Juan Carrasquilla · Adji Dieng · Karthik Kashinath · Gilles Louppe · Brian Nord · Michela Paganini · Savannah Thais
  • 2020 Poster: Flows for simultaneous manifold learning and density estimation »
    Johann Brehmer · Kyle Cranmer
  • 2020 Poster: Discovering Symbolic Models from Deep Learning with Inductive Biases »
    Miles Cranmer · Alvaro Sanchez Gonzalez · Peter Battaglia · Rui Xu · Kyle Cranmer · David Spergel · Shirley Ho
  • 2020 Poster: Set2Graph: Learning Graphs From Sets »
    Hadar Serviansky · Nimrod Segol · Jonathan Shlomi · Kyle Cranmer · Eilam Gross · Haggai Maron · Yaron Lipman
  • 2020 Poster: Black-Box Optimization with Local Generative Surrogates »
    Sergey Shirobokov · Vladislav Belavin · Michael Kagan · Andrei Ustyuzhanin · Atilim Gunes Baydin
  • 2019 : Poster Session »
    Pravish Sainath · Mohamed Akrout · Charles Delahunt · Nathan Kutz · Guangyu Robert Yang · Joe Marino · L F Abbott · Nicolas Vecoven · Damien Ernst · andrew warrington · Michael Kagan · Kyunghyun Cho · Kameron Harris · Leopold Grinberg · John J. Hopfield · Dmitry Krotov · Taliah Muhammad · Erick Cobos · Edgar Walker · Jacob Reimer · Andreas Tolias · Alexander Ecker · Janaki Sheth · Yu Zhang · Maciej Wołczyk · Jacek Tabor · Szymon Maszke · Roman Pogodin · Dane Corneil · Wulfram Gerstner · Baihan Lin · Guillermo Cecchi · Jenna M Reinen · Irina Rish · Guillaume Bellec · Darjan Salaj · Anand Subramoney · Wolfgang Maass · Yueqi Wang · Ari Pakman · Jin Hyung Lee · Liam Paninski · Bryan Tripp · Colin Graber · Alex Schwing · Luke Prince · Gabriel Ocker · Michael Buice · Ben Lansdell · Konrad Kording · Jack Lindsey · Terrence Sejnowski · Matthew Farrell · Eric Shea-Brown · Nicolas Farrugia · Victor Nepveu · Daniel Im · Kristin Branson · Brian Hu · Ram Iyer · Stefan Mihalas · Sneha Aenugu · Hananel Hazan · Sophie Dai · Tan Nguyen · Ying Tsao · Richard Baraniuk · Anima Anandkumar · Hidenori Tanaka · Aran Nayebi · Stephen Baccus · Surya Ganguli · Dean Pospisil · Eilif Muller · Jeffrey S Cheng · Gaël Varoquaux · Kamalaker Dadi · Dimitrios C Gklezakos · Rajesh PN Rao · Anand Louis · Christos Papadimitriou · Santosh Vempala · Naganand Yadati · Daniel Zdeblick · Daniela M Witten · Nick Roberts · Vinay Prabhu · Pierre Bellec · Poornima Ramesh · Jakob H Macke · Santiago Cadena · Guillaume Bellec · Franz Scherr · Owen Marschall · Robert Kim · Hannes Rapp · Marcio Fonseca · Oliver Armitage · Jiwoong Im · Thomas Hardcastle · Abhishek Sharma · Wyeth Bair · Adrian Valente · Shane Shang · Merav Stern · Rutuja Patil · Peter Wang · Sruthi Gorantla · Peter Stratton · Tristan Edwards · Jialin Lu · Martin Ester · Yurii Vlasov · Siavash Golkar
  • 2019 : Opening Remarks »
    Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood
  • 2019 Workshop: Machine Learning and the Physical Sciences »
    Atilim Gunes Baydin · Juan Carrasquilla · Shirley Ho · Karthik Kashinath · Michela Paganini · Savannah Thais · Anima Anandkumar · Kyle Cranmer · Roger Melko · Mr. Prabhat · Frank Wood
  • 2019 Poster: Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model »
    Atilim Gunes Baydin · Lei Shao · Wahid Bhimji · Lukas Heinrich · Saeid Naderiparizi · Andreas Munk · Jialin Liu · Bradley Gram-Hansen · Gilles Louppe · Lawrence Meadows · Philip Torr · Victor Lee · Kyle Cranmer · Mr. Prabhat · Frank Wood
  • 2017 : Panel session »
    Iain Murray · Max Welling · Juan Carrasquilla · Anatole von Lilienfeld · Gilles Louppe · Kyle Cranmer
  • 2017 Workshop: Deep Learning for Physical Sciences »
    Atilim Gunes Baydin · Mr. Prabhat · Kyle Cranmer · Frank Wood
  • 2016 Invited Talk: Machine Learning and Likelihood-Free Inference in Particle Physics »
    Kyle Cranmer
  • 2015 : An alternative to ABC for likelihood-free inference »
    Kyle Cranmer