% Encoding: UTF-8 @InProceedings{ahn2013adapregmcmc, Title = {Distributed and Adaptive Darting {M}onte {C}arlo through Regenerations}, Author = {Sungjin Ahn and Yutian Chen and Max Welling}, Booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, Year = {2013}, Editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, Pages = {108–116}, Volume = {31} } @InProceedings{bornn2013herdedgibbs, Title = {Herded {G}ibbs Sampling}, Author = {Bornn, Luke and Chen, Yutian and de Freitas, Nando and Eskelin, Mareija and Fang, Jing and Welling, Max}, Booktitle = {Proceedings of the International Conference on Learning Representations}, Year = {2013} } @InProceedings{chen10kernelherd, Title = {Super-Samples from Kernel Herding}, Author = {Yutian Chen and Alex Smola and Max Welling}, Booktitle = {Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)}, Year = {2010}, Address = {Corvallis, Oregon}, Pages = {109--116}, Publisher = {AUAI Press} } @InProceedings{chen10paramherd, Title = {Parametric Herding}, Author = {Yutian Chen and Max Welling}, Booktitle = {Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics}, Year = {2010}, Pages = {97-104}, Volume = {9} } @InProceedings{chen11herdcrfimgseg, author = {Yutian Chen and Andrew Gelfand and Charless C. Fowlkes and Max Welling}, title = {Integrating Local Classifiers through Nonlinear Dynamics on Label Graphs with an Application to Image Segmentation}, booktitle = {Proceedings of the 2011 International Conference on Computer Vision}, year = {2011}, series = {ICCV '11}, pages = {2635-2642}, isbn = {978-1-4577-1101-5}, numpages = {8} } @InProceedings{chen13bayespomrf, Title = {Evidence Estimation for {B}ayesian Partially Observed {MRF}s}, Author = {Yutian Chen and Max Welling}, Booktitle = {Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics}, Year = {2013}, Editor = {Carvalho, Carlos M. and Ravikumar, Pradeep}, Pages = {178–186}, Volume = {31} } @Article{chen16herdedgibbs, author = {Yutian Chen and Luke Bornn and Nando de Freitas and Mareija Eskelin and Jing Fang and Max Welling}, title = {Herded {G}ibbs Sampling}, journal = {Journal of Machine Learning Research}, year = {2016}, volume = {17}, number = {10}, pages = {1-29}, url = {http://jmlr.org/papers/v17/chen16a.html} } @InProceedings{chen2009bayesxca, Title = {{B}ayesian Extreme Components Analysis}, Author = {Chen, Yutian and Welling, Max}, Booktitle = {Proceedings of the 21\textsuperscript{st} international jont conference on Artifical intelligence}, Year = {2009}, Address = {San Francisco, CA, USA}, Pages = {1022--1027}, Publisher = {Morgan Kaufmann Publishers Inc.}, Series = {IJCAI'09}, Location = {Pasadena, California, USA}, Numpages = {6} } @InProceedings{chen2010dpot, Title = {Dynamical Products of Experts for Modeling Financial Time Series}, Author = {Yutian Chen and Max Welling}, Booktitle = {Proceedings of the 27th International Conference on Machine Learning (ICML-10)}, Year = {2010}, Address = {Haifa, Israel}, Editor = {Johannes F{\"u}rnkranz and Thorsten Joachims}, Month = {June}, Pages = {207--214}, Publisher = {Omnipress} } @InProceedings{chen2012spikeslabmrf, Title = {{B}ayesian Structure Learning for {M}arkov Random Fields with a Spike and Slab Prior}, Author = {Yutian Chen and Max Welling}, Booktitle = {Proceedings of the Twenty-Eighth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-12)}, Year = {2012}, Address = {Corvallis, Oregon}, Pages = {174--184}, Publisher = {AUAI Press} } @PhdThesis{chen2013herdingthesis, Title = {Herding: Driving Deterministic Dynamics to Learn and Sample Probabilistic Models DISSERTATION}, Author = {Chen, Yutian}, School = {UNIVERSITY OF CALIFORNIA, IRVINE}, Year = {2013} } @InBook{chen2014herdingbookchapter, Title = {Advanced Structured Prediction}, Author = {Chen, Yutian and Gelfand, Andrew E and Welling, Max}, Chapter = {Herding for Structured Prediction}, Editor = {Nowozin, Sebastian and Gehler, Peter V. and Jancsary, Jeremy and Lampert, Christoph H.}, Pages = {187}, Publisher = {The MIT Press}, Year = {2014} } @TechReport{chen2015austerityventure, author = {Yutian Chen and Vikash Mansinghka and Zoubin Ghahramani.}, title = {Sublinear approximate inference for probabilistic programs}, institution = {arXiv:1411.1690}, year = {2015} } @InProceedings{chen2016discsampling, author = {Yutian Chen and Zoubin Ghahramani}, title = {Scalable Discrete Sampling as a Multi-Armed Bandit Problem}, booktitle = {Proceedings of the 33rd International Conference on Machine Learning}, year = {2016}, pages = {2492–2501} } @InBook{chen2016herdingbookchapter, chapter = {Herding as a Learning System with Edge-of-Chaos Dynamics}, title = {Perturbations, Optimization, and Statistics}, publisher = {The MIT Press}, year = {2016}, author = {Chen, Yutian and Welling, Max}, editor = {Tamir Hazan and George Papandreou and Daniel Tarlow} } @InProceedings{chen2016l2lblackboxoptim, author = {Yutian Chen and Matthew W. Hoffman and Sergio Gomez Colmenarejo and Misha Denil and Timothy P. Lillicrap and Nando de Freitas}, title = {Learning to Learn for Global Optimization of Black Box Functions}, booktitle = {Deep Reinforcement Learning Workshop, NIPS}, year = {2016} } @InProceedings{chen2017l2l, author = {Yutian Chen and Matthew W. Hoffman and Sergio Gómez Colmenarejo and Misha Denil and Timothy P Lillicrap and Matt Botvinick and Nando de Freitas}, title = {Learning to learn without gradient descent by gradient descent}, booktitle = {Accepted by the 33rd International Conference on Machine Learning}, year = {2017} } @InProceedings{frigola2014variational, Title = {Variational Gaussian process state-space models}, Author = {Frigola, Roger and Chen, Yutian and Rasmussen, Carl}, Booktitle = {Advances in Neural Information Processing Systems}, Year = {2014}, Pages = {3680--3688} } @InProceedings{gal2015clgp, author = {Yarin Gal and Yutian Chen and Zoubin Ghahramani }, title = {Latent {G}aussian Processes for Distribution Estimation of Multivariate Categorical Data}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, year = {2015}, pages = {645--654}, url = {http://jmlr.org/proceedings/papers/v37/gala15.html} } @InProceedings{ge2015paralelldphdp, author = {Hong Ge and Yutian Chen and Moquan Wan and Zoubin Ghahramani}, title = {Distributed Inference for {D}irichlet Process Mixture Models}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, year = {2015}, pages = {2276--2284}, url = {http://jmlr.org/proceedings/papers/v37/gea15.html} } @InProceedings{gelfand10pct, Title = {On Herding and the Perceptron Cycling Theorem}, Author = {Andrew Gelfand and Yutian Chen and Laurens van der Maaten and Max Welling}, Booktitle = {Advances in Neural Information Processing Systems 23}, Year = {2010}, Editor = {J. Lafferty and C. K. I. Williams and J. Shawe-Taylor and R.S. Zemel and A. Culotta}, Pages = {694--702} } @InProceedings{korattikara2013austerity, author = {Anoop Korattikara and Yutian Chen and Max Welling}, title = {Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, year = {2014}, editor = {Eric P. Xing and Tony Jebara}, volume = {32}, number = {1}, series = {Proceedings of Machine Learning Research}, pages = {181--189}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, abstract = {Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypothesis test that allows us to accept or reject samples with high confidence using only a fraction of the data required for the exact MH rule. While this method introduces an asymptotic bias, we show that this bias can be controlled and is more than offset by a decrease in variance due to our ability to draw more samples per unit of time.}, file = {korattikara14.pdf:http\://proceedings.mlr.press/v32/korattikara14.pdf:PDF}, url = {http://proceedings.mlr.press/v32/korattikara14.html}, } @Article{korattikara2016seqtest, author = {Korattikara, Anoop and Chen, Yutian and Welling, Max}, title = {Sequential Tests for Large-Scale Learning}, journal = {Neural Computation}, year = {2016}, volume = {28}, number = {1}, pages = {45--70}, month = {1}, doi = {doi:10.1162/NECO\_a\_00796} } @InProceedings{reed2017parallelpixcnn, author = {Reed, Scott and van den Oord, A{\"a}ron and Kalchbrenner, Nal and G{\'o}mez Colmenarejo, Sergio and Wang, Ziyu and Chen, Yutian and Belov, Dan and de Freitas, Nando}, title = {Parallel multiscale autoregressive density estimation}, booktitle = {Accepted by the 33rd International Conference on Machine Learning}, year = {2017} } @InProceedings{welling10chaos, Title = {Statistical Inference Using Weak Chaos and Infinite Memory}, Author = {Max Welling and Yutian Chen}, Booktitle = {Proceedings of the Int'l Workshop on Statistical-Mechanical Informatics (IW-SMI 2010)}, Year = {2010}, Pages = {185-199} } @Article{zhu2010pinyin, Title = {Combined Recognition System for Handwritten Pinyin}, Author = {Zhu, Meng and Liu, Chang-song and Chen, Yutian and Zou, Yan-ming}, Journal = {Computer Engineering}, Year = {2010}, Pages = {170-172}, Volume = {36(7)}, ISSN = {1000-3428} } @Article{Silver2017, author = {Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and Chen, Yutian and Lillicrap, Timothy and Hui, Fan and Sifre, Laurent and van den Driessche, George and Graepel, Thore and Hassabis, Demis}, title = {Mastering the game of Go without human knowledge}, journal = {Nature}, year = {2017}, volume = {550}, number = {7676}, pages = {354--359}, month = oct, issn = {0028-0836}, __markedentry = {[yutianc:6]}, abstract = {A long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo’s own move selections and also the winner of AlphaGo’s games. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo.}, publisher = {Macmillan Publishers Limited, part of Springer Nature. All rights reserved.}, url = {http://dx.doi.org/10.1038/nature24270}, } @InProceedings{Mansinghka2018PPP, author = {Mansinghka, Vikash K. and Schaechtle, Ulrich and Handa, Shivam and Radul, Alexey and Chen, Yutian and Rinard, Martin}, title = {Probabilistic Programming with Programmable Inference}, booktitle = {Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation}, year = {2018}, series = {PLDI 2018}, pages = {603--616}, address = {New York, NY, USA}, publisher = {ACM}, acmid = {3192409}, doi = {10.1145/3192366.3192409}, isbn = {978-1-4503-5698-5}, keywords = {Inference, Probabilistic Programming, Semantics}, location = {Philadelphia, PA, USA}, numpages = {14}, url = {http://doi.acm.org/10.1145/3192366.3192409}, } @InProceedings{reed2018fewshot, author = {Scott Reed and Yutian Chen and Thomas Paine and Aäron van den Oord and S. M. Ali Eslami and Danilo Rezende and Oriol Vinyals and Nando de Freitas}, title = {Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions}, booktitle = {International Conference on Learning Representations}, year = {2018}, url = {https://openreview.net/forum?id=r1wEFyWCW}, } @Article{chen2018boalphago, author = {Yutian Chen and Aja Huang and Ziyu Wang and Ioannis Antonoglou and Julian Schrittwieser and David Silver and Nando de Freitas}, title = {{B}ayesian Optimization in {A}lpha{G}o}, journal = {arXiv:1812.06855}, year = {2018}, } @InProceedings{chen2019seatts, author = {Yutian Chen and Yannis M. Assael and Brendan Shillingford and David Budden and Scott E. Reed and Heiga Zen and Quan Wang and Luis C. Cobo and Andrew Trask and Ben Laurie and {\c{C}}aglar G{\"{u}}l{\c{c}}ehre and A{\"{a}}ron van den Oord and Oriol Vinyals and Nando de Freitas}, title = {Sample Efficient Adaptive Text-to-Speech}, booktitle = {Proceedings of the International Conference on Learning Representations}, year = {2019}, } @inproceedings{chen2020shrinkage, author = {Chen, Yutian and Friesen, Abram L and Behbahani, Feryal and Doucet, Arnaud and Budden, David and Hoffman, Matthew and de Freitas, Nando}, booktitle = {Advances in Neural Information Processing Systems}, editor = {H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin}, pages = {2858--2869}, publisher = {Curran Associates, Inc.}, title = {Modular Meta-Learning with Shrinkage}, url = {https://proceedings.neurips.cc/paper/2020/file/1e04b969bf040acd252e1faafb51f829-Paper.pdf}, volume = {33}, year = {2020} } @article{yang2020large, title={Large-scale multilingual audio visual dubbing}, author={Yang, Yi and Shillingford, Brendan and Assael, Yannis and Wang, Miaosen and Liu, Wendi and Chen, Yutian and Zhang, Yu and Sezener, Eren and Cobo, Luis C and Denil, Misha and others}, journal={arXiv preprint arXiv:2011.03530}, year={2020} } @inproceedings{huang2021synth2aug, title={Synth2Aug: Cross-domain speaker recognition with TTS synthesized speech}, author={Huang, Yiling and Chen, Yutian and Pelecanos, Jason and Wang, Quan}, booktitle={2021 IEEE Spoken Language Technology Workshop (SLT)}, pages={316--322}, year={2021}, organization={IEEE} } @inproceedings{ xu2021learning, title={Learning Deep Features in Instrumental Variable Regression}, author={Liyuan Xu and Yutian Chen and Siddarth Srinivasan and Nando de Freitas and Arnaud Doucet and Arthur Gretton}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=sy4Kg_ZQmS7} } @article{titsias2020sequential, title={Sequential Changepoint Detection in Neural Networks with Checkpoints}, author={Titsias, Michalis K and Sygnowski, Jakub and Chen, Yutian}, journal={arXiv preprint arXiv:2010.03053}, year={2020} } @inproceedings{ fu2021benchmarks, title={Benchmarks for Deep Off-Policy Evaluation}, author={Justin Fu and Mohammad Norouzi and Ofir Nachum and George Tucker and ziyu wang and Alexander Novikov and Mengjiao Yang and Michael R Zhang and Yutian Chen and Aviral Kumar and Cosmin Paduraru and Sergey Levine and Thomas Paine}, booktitle={International Conference on Learning Representations}, year={2021}, url={https://openreview.net/forum?id=kWSeGEeHvF8} } @article{gulcehre2021regularized, title={Regularized Behavior Value Estimation}, author={Gulcehre, Caglar and Colmenarejo, Sergio G{\'o}mez and Wang, Ziyu and Sygnowski, Jakub and Paine, Thomas and Zolna, Konrad and Chen, Yutian and Hoffman, Matthew and Pascanu, Razvan and de Freitas, Nando}, journal={arXiv preprint arXiv:2103.09575}, year={2021} } @article{chen2021instrumental, title={On Instrumental Variable Regression for Deep Offline Policy Evaluation}, author={Chen, Yutian and Xu, Liyuan and Gulcehre, Caglar and Paine, Tom Le and Gretton, Arthur and de Freitas, Nando and Doucet, Arnaud}, journal={arXiv preprint arXiv:2105.10148}, year={2021} } @article{kirsch2021vsmrl, title={Introducing Symmetries to Black Box Meta Reinforcement Learning}, author={Kirsch, Louis and Flennerhag, Sebastian and van Hasselt, Hado and Friesen, Abram and Oh, Junhyuk and Chen, Yutian}, journal={arXiv preprint arXiv:2109.10781}, year={2021} } @article{konyushkova2021active, title={Active Offline Policy Selection}, author={Konyushkova, Ksenia and Chen, Yutian and Paine, Thomas and Gulcehre, Caglar and Paduraru, Cosmin and Mankowitz, Daniel J and Denil, Misha and de Freitas, Nando}, journal={arXiv preprint arXiv:2106.10251}, year={2021} } @Comment{jabref-meta: databaseType:bibtex;}