`

Timezone: »

 
Poster
Reducing Reparameterization Gradient Variance
Andrew Miller · Nick Foti · Alexander D'Amour · Ryan Adams

Tue Dec 05 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #181 #None
Optimization with noisy gradients has become ubiquitous in statistics and machine learning. Reparameterization gradients, or gradient estimates computed via the ``reparameterization trick,'' represent a class of noisy gradients often used in Monte Carlo variational inference (MCVI). However, when these gradient estimators are too noisy, the optimization procedure can be slow or fail to converge. One way to reduce noise is to generate more samples for the gradient estimate, but this can be computationally expensive. Instead, we view the noisy gradient as a random variable, and form an inexpensive approximation of the generating procedure for the gradient sample. This approximation has high correlation with the noisy gradient by construction, making it a useful control variate for variance reduction. We demonstrate our approach on a non-conjugate hierarchical model and a Bayesian neural net where our method attained orders of magnitude (20-2{,}000$\times$) reduction in gradient variance resulting in faster and more stable optimization.

Author Information

Andrew Miller (Columbia)
Nick Foti (Apple & University of Washington)
Alexander D'Amour (UC Berkeley)
Ryan Adams

More from the Same Authors

  • 2021 Spotlight: SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression »
    Steve Yadlowsky · Taedong Yun · Cory Y McLean · Alexander D'Amour
  • 2021 Spotlight: Counterfactual Invariance to Spurious Correlations in Text Classification »
    Victor Veitch · Alexander D'Amour · Steve Yadlowsky · Jacob Eisenstein
  • 2021 : Learning Invariant Representations with Missing Data »
    Mark Goldstein · Adriel Saporta · Aahlad Puli · Rajesh Ranganath · Andrew Miller
  • 2021 Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling »
    Diana Cai · Sameer Deshpande · Mike Hughes · Tamara Broderick · Trevor Campbell · Nick Foti · Barbara Engelhardt · Sinead Williamson
  • 2021 Poster: SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression »
    Steve Yadlowsky · Taedong Yun · Cory Y McLean · Alexander D'Amour
  • 2021 Poster: Counterfactual Invariance to Spurious Correlations in Text Classification »
    Victor Veitch · Alexander D'Amour · Steve Yadlowsky · Jacob Eisenstein
  • 2018 : Poster Session I »
    Aniruddh Raghu · Dan Jarrett · Katie Lewis · Elias Chaibub Neto · Nick Mastronarde · Shazia Akbar · Chun-Hung Chao · Henry Zhu · Seth Stafford · Luna Zhang · Jen-Tang Lu · Changhee Lee · Adit Radhakrishnan · Fabian Falck · Liyue Shen · Daniel Neil · Yusuf Roohani · Aparna Balagopalan · Brett Marinelli · Hagai Rossman · Sven Giesselbach · Jose Javier Gonzalez Ortiz · Edward De Brouwer · Byung-Hoon Kim · Rafid Mahmood · Harry Hsu · Antonio Ribeiro · Rumi Chunara · Agni Orfanoudaki · Kristen Severson · Mingjie Mai · Sonali Parbhoo · Albert Haque · Viraj Prabhu · Di Jin · Alena Harley · Geoffroy Dubourg-Felonneau · Xiaodan Hu · Maithra Raghu · Jonathan Warrell · Nelson Johansen · Wenyuan Li · Marko Järvenpää · Satya Narayan Shukla · Sarah Tan · Vincent Fortuin · Beau Norgeot · Yi-Te Hsu · Joel H Saltz · Veronica Tozzo · Andrew Miller · Guillaume Ausset · Azin Asgarian · Francesco Paolo Casale · Antoine Neuraz · Bhanu Pratap Singh Rawat · Turgay Ayer · Xinyu Li · Mehul Motani · Nathaniel Braman · Laetitia M Shao · Adrian Dalca · Hyunkwang Lee · Emma Pierson · Sandesh Ghimire · Yuji Kawai · Owen Lahav · Anna Goldenberg · Denny Wu · Pavitra Krishnaswamy · Colin Pawlowski · Arijit Ukil · Yuhui Zhang
  • 2018 Workshop: All of Bayesian Nonparametrics (Especially the Useful Bits) »
    Diana Cai · Trevor Campbell · Mike Hughes · Tamara Broderick · Nick Foti · Sinead Williamson
  • 2017 : Contributed talk: Taylor Residual Estimators via Automatic Differentiation »
    Andrew Miller
  • 2017 : Poster Session »
    Shunsuke Horii · Heejin Jeong · Tobias Schwedes · Qing He · Ben Calderhead · Ertunc Erdil · Jaan Altosaar · Patrick Muchmore · Rajiv Khanna · Ian Gemp · Pengfei Zhang · Yuan Zhou · Chris Cremer · Maria DeYoreo · Alexander Terenin · Brendan McVeigh · Rachit Singh · Yaodong Yang · Erik Bodin · Trefor Evans · Henry Chai · Shandian Zhe · Jeffrey Ling · Vincent ADAM · Lars Maaløe · Andrew Miller · Ari Pakman · Josip Djolonga · Hong Ge
  • 2017 : Coffee break and Poster Session I »
    Nishith Khandwala · Steve Gallant · Greg Way · Aniruddh Raghu · Li Shen · Aydan Gasimova · Alican Bozkurt · Willie Boag · Daniel Lopez-Martinez · Ulrich Bodenhofer · Samaneh Nasiri GhoshehBolagh · Michelle Guo · Christoph Kurz · Kirubin Pillay · Kimis Perros · George H Chen · Alexandre Yahi · Madhumita Sushil · Sanjay Purushotham · Elena Tutubalina · Tejpal Virdi · Marc-Andre Schulz · Samuel Weisenthal · Bharat Srikishan · Petar Veličković · Kartik Ahuja · Andrew Miller · Erin Craig · Disi Ji · Filip Dabek · Chloé Pou-Prom · Hejia Zhang · Janani Kalyanam · Wei-Hung Weng · Harish Bhat · Hugh Chen · Simon Kohl · Mingwu Gao · Tingting Zhu · Ming-Zher Poh · Iñigo Urteaga · Antoine Honoré · Alessandro De Palma · Maruan Al-Shedivat · Pranav Rajpurkar · Matthew McDermott · Vincent Chen · Yanan Sui · Yun-Geun Lee · Li-Fang Cheng · David Fang · Sibt Hussain · Cesare Furlanello · Zeev Waks · Hiba Chougrad · Hedvig Kjellstrom · Finale Doshi-Velez · Wolfgang Fruehwirt · Yanqing Zhang · Lily Hu · Junfang Chen · Sunho Park · Gatis Mikelsons · Jumana Dakka · Stephanie Hyland · yann chevaleyre · Hyunwoo Lee · Xavi Giro-i-Nieto · David Kale · Mike Hughes · Gabriel Erion · Rishab Mehra · William Zame · Stojan Trajanovski · Prithwish Chakraborty · Kelly Peterson · Muktabh Srivastava · Amy Jin · Helio Tejeda Lemus · Priyadip Ray · Tamas Madl · Joe Futoma · Enhao Gong · Syed Rameel Ahmad · Eric Lei · Ferdinand Legros
  • 2017 Poster: PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
    Jonathan Huggins · Ryan Adams · Tamara Broderick
  • 2017 Spotlight: PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
    Jonathan Huggins · Ryan Adams · Tamara Broderick
  • 2016 Workshop: Practical Bayesian Nonparametrics »
    Nick Foti · Tamara Broderick · Trevor Campbell · Mike Hughes · Jeff Miller · Aaron Schein · Sinead Williamson · Yanxun Xu
  • 2015 Workshop: Bayesian Nonparametrics: The Next Generation »
    Tamara Broderick · Nick Foti · Aaron Schein · Alex Tank · Hanna Wallach · Sinead Williamson
  • 2015 Poster: A Gaussian Process Model of Quasar Spectral Energy Distributions »
    Andrew Miller · Albert Wu · Jeff Regier · Jon McAuliffe · Dustin Lang · Mr. Prabhat · David Schlegel · Ryan Adams
  • 2014 Poster: Stochastic variational inference for hidden Markov models »
    Nick Foti · Jason Xu · Dillon Laird · Emily Fox
  • 2012 Poster: Slice sampling normalized kernel-weighted completely random measure mixture models »
    Nick Foti · Sinead Williamson