More from the Same Authors

• 2021 : Measuring the sensitivity of Gaussian processes to kernel choice »
  Will Stephenson · Soumya Ghosh · Tin Nguyen · Mikhail Yurochkin · Sameer Deshpande · Tamara Broderick
• 2022 : Ice Core Dating using Probabilistic Programming »
  Aditya Ravuri · Tom Andersson · Ieva Kazlauskaite · William Tebbutt · Richard Turner · Scott Hosking · Neil Lawrence · Markus Kaiser
• 2022 : Sequential Gaussian Processes for Online Learning of Nonstationary Functions »
  Michael Minyi Zhang · Bianca Dumitrascu · Sinead Williamson · Barbara Engelhardt
• 2022 : Multi-fidelity experimental design for ice-sheet simulation »
  Pierre Thodoroff · Markus Kaiser · Rosie Williams · Robert Arthern · Scott Hosking · Neil Lawrence · Ieva Kazlauskaite
• 2022 : Detecting Synthetic Opioids with NQR Spectroscopy and Complex-Valued Signal Denoising »
  Amber Day · Natalie Klein · Michael Malone · Harris Mason · Sinead Williamson
• 2022 : Detecting State Changes in Dynamic Neuronal Networks »
  Yiwei Gong · Sinead Williamson
• 2022 : Spike-and-Slab Probabilistic Backpropagation: When Smarter Approximations Make No Difference »
  Evan Ott · Sinead Williamson
• 2022 : Panel on Privacy and Security in Machine Learning Systems »
  Graham Cormode · Borja Balle · Yu-Xiang Wang · Alejandro Saucedo · Neil Lawrence
• 2022 Poster: Modeling the Machine Learning Multiverse »
  Samuel J. Bell · Onno Kampman · Jesse Dodge · Neil Lawrence
• 2021 Workshop: Your Model is Wrong: Robustness and misspecification in probabilistic modeling »
  Diana Cai · Sameer Deshpande · Michael Hughes · Tamara Broderick · Trevor Campbell · Nick Foti · Barbara Engelhardt · Sinead Williamson
• 2021 Workshop: Learning Meaningful Representations of Life (LMRL) »
  Elizabeth Wood · Adji Bousso Dieng · Aleksandrina Goeva · Anshul Kundaje · Barbara Engelhardt · Chang Liu · David Van Valen · Debora Marks · Edward Boyden · Eli N Weinstein · Lorin Crawford · Mor Nitzan · Romain Lopez · Tamara Broderick · Ray Jones · Wouter Boomsma · Yixin Wang
• 2021 Panel: How Should a Machine Learning Researcher Think About AI Ethics? »
  Amanda Askell · Abeba Birhane · Jesse Dodge · Casey Fiesler · Pascale N Fung · Hanna Wallach
• 2021 Poster: Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression »
  Will Stephenson · Zachary Frangella · Madeleine Udell · Tamara Broderick
• 2021 Poster: For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets »
  Brian Trippe · Hilary Finucane · Tamara Broderick
• 2020 : Morning keynote »
  Hanna Wallach · Rosie Campbell
• 2020 Workshop: I Can’t Believe It’s Not Better! Bridging the gap between theory and empiricism in probabilistic machine learning »
  Jessica Forde · Francisco Ruiz · Melanie Fernandez Pradier · Aaron Schein · Finale Doshi-Velez · Isabel Valera · David Blei · Hanna Wallach
• 2020 : Tamara Broderick »
  Tamara Broderick
• 2020 Poster: Approximate Cross-Validation for Structured Models »
  Soumya Ghosh · Will Stephenson · Tin Nguyen · Sameer Deshpande · Tamara Broderick
• 2020 Poster: Approximate Cross-Validation with Low-Rank Data in High Dimensions »
  Will Stephenson · Madeleine Udell · Tamara Broderick
• 2019 Poster: Discrete Flows: Invertible Generative Models of Discrete Data »
  Dustin Tran · Keyon Vafa · Kumar Agrawal · Laurent Dinh · Ben Poole
• 2019 Poster: Poisson-Randomized Gamma Dynamical Systems »
  Aaron Schein · Scott Linderman · Mingyuan Zhou · David Blei · Hanna Wallach
• 2019 Poster: Invertible Convolutional Flow »
  Mahdi Karami · Dale Schuurmans · Jascha Sohl-Dickstein · Laurent Dinh · Daniel Duckworth
• 2019 Spotlight: Invertible Convolutional Flow »
  Mahdi Karami · Dale Schuurmans · Jascha Sohl-Dickstein · Laurent Dinh · Daniel Duckworth
• 2018 : Research Panel »
  Sinead Williamson · Barbara Engelhardt · Tom Griffiths · Neil Lawrence · Hanna Wallach
• 2018 : Panel on research process »
  Zachary Lipton · Charles Sutton · Finale Doshi-Velez · Hanna Wallach · Suchi Saria · Rich Caruana · Thomas Rainforth
• 2018 : Hanna Wallach - Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? »
  Hanna Wallach
• 2018 Workshop: All of Bayesian Nonparametrics (Especially the Useful Bits) »
  Diana Cai · Trevor Campbell · Michael Hughes · Tamara Broderick · Nick Foti · Sinead Williamson
• 2017 : Neil Lawrence, Francis Bach and François Laviolette »
  Neil Lawrence · Francis Bach · Francois Laviolette
• 2017 : Panel Session »
  Neil Lawrence · Finale Doshi-Velez · Zoubin Ghahramani · Yann LeCun · Max Welling · Yee Whye Teh · Ole Winther
• 2017 : Poster spotlights »
  Hiroshi Kuwajima · Masayuki Tanaka · Qingkai Liang · Matthieu Komorowski · Fanyu Que · Thalita F Drumond · Aniruddh Raghu · Leo Anthony Celi · Christina Göpfert · Andrew Ross · Sarah Tan · Rich Caruana · Yin Lou · Devinder Kumar · Graham Taylor · Forough Poursabzi-Sangdeh · Jennifer Wortman Vaughan · Hanna Wallach
• 2017 Workshop: Advances in Approximate Bayesian Inference »
  Francisco Ruiz · Stephan Mandt · Cheng Zhang · James McInerney · James McInerney · Dustin Tran · Dustin Tran · David Blei · Max Welling · Tamara Broderick · Michalis Titsias
• 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
• 2017 Tutorial: Deep Probabilistic Modelling with Gaussian Processes »
  Neil D Lawrence
• 2016 : Tamara Broderick: Foundations Talk »
  Tamara Broderick
• 2016 Workshop: Advances in Approximate Bayesian Inference »
  Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan
• 2016 Workshop: Practical Bayesian Nonparametrics »
  Nick Foti · Tamara Broderick · Trevor Campbell · Michael Hughes · Jeffrey Miller · Aaron Schein · Sinead Williamson · Yanxun Xu
• 2016 Poster: Variance Reduction in Stochastic Gradient Langevin Dynamics »
  Kumar Avinava Dubey · Sashank J. Reddi · Sinead Williamson · Barnabas Poczos · Alexander Smola · Eric Xing
• 2016 Poster: Poisson-Gamma dynamical systems »
  Aaron Schein · Hanna Wallach · Mingyuan Zhou
• 2016 Oral: Poisson-Gamma dynamical systems »
  Aaron Schein · Hanna Wallach · Mingyuan Zhou
• 2016 Poster: Coresets for Scalable Bayesian Logistic Regression »
  Jonathan Huggins · Trevor Campbell · Tamara Broderick
• 2016 Poster: Flexible Models for Microclustering with Application to Entity Resolution »
  Brenda Betancourt · Giacomo Zanella · Jeffrey Miller · Hanna Wallach · Abbas Zaidi · Beka Steorts
• 2016 Poster: Edge-exchangeable graphs and sparsity »
  Diana Cai · Trevor Campbell · Tamara Broderick
• 2015 Workshop: Bayesian Nonparametrics: The Next Generation »
  Tamara Broderick · Nick Foti · Aaron Schein · Alex Tank · Hanna Wallach · Sinead Williamson
• 2015 Workshop: Advances in Approximate Bayesian Inference »
  Dustin Tran · Tamara Broderick · Stephan Mandt · James McInerney · Shakir Mohamed · Alp Kucukelbir · Matthew D. Hoffman · Neil Lawrence · David Blei
• 2015 Poster: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
  Ryan Giordano · Tamara Broderick · Michael Jordan
• 2015 Spotlight: Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes »
  Ryan Giordano · Tamara Broderick · Michael Jordan
• 2015 Poster: A Recurrent Latent Variable Model for Sequential Data »
  Junyoung Chung · Kyle Kastner · Laurent Dinh · Kratarth Goel · Aaron Courville · Yoshua Bengio
• 2014 Workshop: Advances in Variational Inference »
  David Blei · Shakir Mohamed · Michael Jordan · Charles Blundell · Tamara Broderick · Matthew D. Hoffman
• 2014 Workshop: ABC in Montreal »
  Max Welling · Neil D Lawrence · Richard D Wilkinson · Ted Meeds · Christian X Robert
• 2014 Poster: Dependent nonparametric trees for dynamic hierarchical clustering »
  Kumar Avinava Dubey · Qirong Ho · Sinead Williamson · Eric Xing
• 2013 Workshop: Topic Models: Computation, Application, and Evaluation »
  David Mimno · Amr Ahmed · Jordan Boyd-Graber · Ankur Moitra · Hanna Wallach · Alexander Smola · David Blei · Anima Anandkumar
• 2013 Workshop: Probabilistic Models for Big Data »
  Neil D Lawrence · Joaquin Quiñonero-Candela · Tianshi Gao · James Hensman · Zoubin Ghahramani · Max Welling · David Blei · Ralf Herbrich
• 2013 Poster: Optimistic Concurrency Control for Distributed Unsupervised Learning »
  Xinghao Pan · Joseph Gonzalez · Stefanie Jegelka · Tamara Broderick · Michael Jordan
• 2013 Session: Oral Session 1 »
  Neil D Lawrence
• 2013 Poster: Streaming Variational Bayes »
  Tamara Broderick · Nicholas Boyd · Andre Wibisono · Ashia C Wilson · Michael Jordan
• 2012 Poster: Topic-Partitioned Multinetwork Embeddings »
  Peter Krafft · Juston S Moore · Hanna Wallach · Bruce Desmarais
• 2012 Poster: Fast Variational Inference in the Conjugate Exponential Family »
  James Hensman · Magnus Rattray · Neil D Lawrence
• 2011 Workshop: 2nd Workshop on Computational Social Science and the Wisdom of Crowds »
  Winter Mason · Jennifer Wortman Vaughan · Hanna Wallach
• 2011 Poster: Learning sparse inverse covariance matrices in the presence of confounders »
  Oliver Stegle · Christoph Lippert · Joris M Mooij · Neil D Lawrence · Karsten Borgwardt
• 2011 Poster: Variational Gaussian Process Dynamical Systems »
  Andreas Damianou · Michalis Titsias · Neil D Lawrence
• 2010 Workshop: Computational Social Science and the Wisdom of Crowds »
  Jennifer Wortman Vaughan · Hanna Wallach
• 2010 Placeholder: Opening Remarks »
  Terrence Sejnowski · Neil D Lawrence
• 2010 Spotlight: Switched Latent Force Models for Movement Segmentation »
  Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence
• 2010 Poster: Switched Latent Force Models for Movement Segmentation »
  Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence
• 2009 Workshop: Kernels for Multiple Outputs and Multi-task Learning: Frequentist and Bayesian Points of View »
  Mauricio A Alvarez · Lorenzo Rosasco · Neil D Lawrence
• 2009 Workshop: Applications for Topic Models: Text and Beyond »
  David Blei · Jordan Boyd-Graber · Jonathan Chang · Katherine Heller · Hanna Wallach
• 2009 Poster: Rethinking LDA: Why Priors Matter »
  Hanna Wallach · David Mimno · Andrew McCallum
• 2009 Spotlight: Rethinking LDA: Why Priors Matter »
  Hanna Wallach · David Mimno · Andrew McCallum
• 2008 Poster: Sparse Convolved Gaussian Processes for Multi-ouptut Regression »
  Mauricio A Alvarez · Neil D Lawrence
• 2008 Poster: Efficient Sampling for Gaussian Process Inference using Control Variables »
  Michalis Titsias · Neil D Lawrence · Magnus Rattray
• 2008 Spotlight: Efficient Sampling for Gaussian Process Inference using Control Variables »
  Michalis Titsias · Neil D Lawrence · Magnus Rattray
• 2008 Poster: Accelerating Bayesian Inference over Nonlinear Differential Equations with Gaussian Processes »
  Ben Calderhead · Mark A Girolami · Neil D Lawrence
• 2007 Workshop: Approximate Bayesian Inference in Continuous/Hybrid Models »
  Matthias Seeger · David Barber · Neil D Lawrence · Onno Zoeter
• 2006 Workshop: Learning when test and training inputs have different distributions »
  Joaquin Quiñonero-Candela · Masashi Sugiyama · Anton Schwaighofer · Neil D Lawrence
• 2006 Poster: Modelling transcriptional regulation using Gaussian Processes »
  Neil D Lawrence · Guido Sanguinetti · Magnus Rattray