Journals   Conferences    Thesis    Talks

Journals

2024

• F. J. R. Ruiz, T. Laakkonen, J. Bausch, M. Balog, M. Barekatain, F. J. H. Heras, A. Novikov, N. Fitzpatrick, B. Romera-Paredes, J. van de Wetering, A. Fawzi, K. Meichanetzidis, P. Kohli. Quantum Circuit Optimization with AlphaTensor. Preprint. 2024.
  [arxiv] [bibtex]

2023

• B. Romera-Paredes, M. Barekatain, A. Novikov, M. Balog, M. P. Kumar, E. Dupont, F. J. R. Ruiz, J. S. Ellenberg, P. Wang, O. Fawzi, P. Kohli, A. Fawzi. Mathematical discoveries from program search with large language models. Nature. 2023.
  [link] [pdf] [supplement] [blog] [bibtex]
• X. Han, X. Chen, F. J. R. Ruiz, and L. Liu. Fitting autoregressive graph generative models through maximum likelihood estimation. Journal of Machine Learning Research. 2023.
  [pdf] [bibtex] [code]

2022

• A. Fawzi, M. Balog, A. Huang, T. Hubert, B. Romera-Paredes, M. Barekatain, A. Novikov, F. J. R. Ruiz, J. Schrittwieser, G. Swirszcz, D. Silver, D. Hassabis, and P. Kohli. Discovering faster matrix multiplication algorithms with reinforcement learning. Nature. 2022.
  [link] [pdf] [supplement] [blog] [bibtex]

2021

• R. Donnelly, F. J. R. Ruiz, D. M. Blei, and S. Athey. Counterfactual inference for consumer choice across many product categories. Quantitative Marketing and Economics. 2021.
  [pdf] [arxiv] [bibtex]

2020

• F. J. R. Ruiz, S. Athey, and D. M. Blei. SHOPPER: A probabilistic model of consumer choice with substitutes and complements. Annals of Applied Statistics. 2020. "Best of AoAS session" at Joint Statistical Meetings of the Americal Statistics Association, 2020.
  [pdf] [arxiv] [bibtex] [code]
• A. B. Dieng, F. J. R. Ruiz, and D. M. Blei. Topic modeling in embedding spaces. Transactions of the Association for Computational Linguistics. 2020.
  [pdf] [arxiv] [code] [bibtex]

2019

• A. B. Dieng, F. J. R. Ruiz, D. M. Blei and M. K. Titsias. Prescribed generative adversarial networks. ArXiV preprint. 2019.
  [pdf] [arxiv] [bibtex] [code]
• H. M. Levitin, J. Yuan, Y. L. Cheng, F. J. R. Ruiz, E. C. Bush, J. N. Bruce, P. Canoll, A. Iavarone, A. Lasorella, D. M. Blei, and P. A. Sims. De novo gene signature identification from single-cell RNA-seq with hierarchical Poisson factorization. Molecular Systems Biology. 2019.
  [pdf] [biorxiv] [code] [bibtex]

2018

• S. Athey, D. M. Blei, R. Donnelly, F. J. R. Ruiz, and T. Schmidt. Estimating heterogeneous consumer preferences for restaurants and travel time using mobile location data. American Economics Association Papers and Proceedings. 2018.
  [pdf] [arxiv] [bibtex] [blog post]
• F. J. R. Ruiz, I. Valera, L. Svensson, and F. Perez-Cruz. Infinite factorial finite state machine for blind multiuser channel estimation. IEEE Transactions on Cognitive Communications and Networking. 2018.
  [link] [arxiv] [bibtex]
• A. P. Ruiz-Beltran, C. M. Appendini, and F. J. R. Ruiz. Impact and recovery assessment of the mangroves affected by Hurricane Patricia (2015). Submitted to Environmental Monitoring and Assessment. 2018.
  [pdf] [arxiv] [bibtex]

2017

• D. Tran, F. J. R. Ruiz, S. Athey, and D. M. Blei. Bayesian model criticism with potential outcomes. ArXiV preprint. 2017.
  [pdf] [arxiv] [bibtex]
• M. Fatemi, K. Granstrom, L. Svensson, F. J. R. Ruiz, and L. Hammarstrand. Poisson multi-Bernoulli radar mapping using Gibbs sampling. IEEE Transactions on Signal Processing. 2017.
  [link] [pdf] [bibtex]

2016

• M. F. Pradier, F. J. R. Ruiz, and F. Perez-Cruz. Prior design for dependent Dirichlet processes: An application to marathon modeling. PlosONE. 2016.
  [pdf] [bibtex]
• I. Valera, F. J. R. Ruiz, P. M. Olmos, C. Blanco, and F. Perez-Cruz. Infinite continuous feature model for psychiatric comorbidity analysis. Neural Computation. 2016.
  [pdf] [bibtex]

2015

• I. Valera, F. J. R. Ruiz, and F. Perez-Cruz. Infinite factorial unbounded-state hidden Markov model. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2015.
  [pdf] [supplement] [bibtex]
• F. J. R. Ruiz and F. Perez-Cruz. A generative model for predicting outcomes in college basketball. Journal of Quantitative Analysis in Sports (Special Issue: Prediction methodology for the NCAA men's basketball tournament). 2015.
  [link] [pdf] [bibtex]

2014

• F. J. R. Ruiz, I. Valera, C. Blanco, and F. Perez-Cruz. Bayesian nonparametric comorbidity analysis of psychiatric disorders. Journal of Machine Learning Research. 2014.
  [pdf] [bibtex]

Conferences

2024

• V. Aglietti, I. Ktena, J. Schrouff, E. Sgouritsa, F. J. R. Ruiz, A. Bellot, S. Chiappa. FunBO: Discovering acquisition functions for Bayesian optimization with FunSearch. arXiv Preprint. Online, June 2024.
  [pdf] [arxiv]

2021

• F. J. R. Ruiz, M. K. Titsias, T. Cemgil, and A. Doucet. Unbiased gradient estimation for variational auto-encoders using coupled Markov chains. Uncertainty in Artificial Intelligence. Online, July 2021. Runner-up for best paper award. Oral presentation.
  [pdf] [arxiv] [bibtex]
• M. K. Titsias, F. J. R. Ruiz, S. Nikoloutsopoulos, and A. Galashov. Information theoretic meta learning with Gaussian processes. Uncertainty in Artificial Intelligence. Online, July 2021. Oral presentation.
  [pdf] [arxiv] [bibtex]
• X. Chen, X. Han, J. Hu, F. J. R. Ruiz, and L. Liu. Order matters: Probabilistic modeling of node sequence for graph generation. International Conference on Machine Learning. Online, July 2021.
  [pdf] [arxiv] [bibtex]

2020

• L. Richter, A. Boustati, N. Nusken, F. J. R. Ruiz, and O. D. Akyildiz. VarGrad: A low-variance gradient estimator for variational inference. Neural Information Processing Systems. Online, December 2020.
  [pdf] [arxiv] [bibtex]
• A. B. Dieng, F. J. R. Ruiz, and D. M. Blei. Topic modeling in embedding spaces. Conference on Empirical Methods in Natural Language Processing. Online, November 2020.
  [pdf] [arxiv] [code] [bibtex]
• A. B. Dieng, F. J. R. Ruiz, D. M. Blei. The dynamic embedded topic model. ArXiV preprint. 2020.
  [pdf] [arxiv] [bibtex] [code]

2019

• F. J. R. Ruiz and M. K. Titsias. A contrastive divergence for combining variational inference and MCMC. International Conference on Machine Learning. Long Beach (CA, USA), June 2019.
  [pdf] [arxiv] [bibtex] [code]
• M. K. Titsias and F. J. R. Ruiz. Unbiased implicit variational inference. Artificial Intelligence and Statistics. Naha (Japan), April 2019.
  [pdf] [arxiv] [bibtex] [code]

2018

• F. J. R. Ruiz, M. K. Titsias, A. B. Dieng, and D. M. Blei. Augment and reduce: Stochastic inference for large categorical distributions. International Conference on Machine Learning. Stockholm (Sweden), July 2018.
  [pdf] [arxiv] [bibtex] [code]

2017

• M. Rudolph, F. J. R. Ruiz, S. Athey, and D. M. Blei. Structured embedding models for grouped data. Advances in Neural Information Processing Systems. Long Beach (CA, USA), December 2017.
  [pdf] [arxiv] [bibtex] [code]
• L. Liu, F. J. R. Ruiz, S. Athey, and D. M. Blei. Context selection for embedding models. Advances in Neural Information Processing Systems. Long Beach (CA, USA), December 2017.
  [pdf] [bibtex] [code]
• C. A. Naesseth, F. J. R. Ruiz, S. W. Linderman, and D. M. Blei. Reparameterization gradients through acceptance-rejection sampling algorithms. International Conference on Artificial Intelligence and Statistics (AISTATS). Fort Lauderdale, FL (USA), April 2017. Best paper award. Oral presentation.
  [pdf] [supplement] [arxiv] [code] [blog post] [bibtex]

2016

• F. J. R. Ruiz, M. K. Titsias, and D. M. Blei. The generalized reparameterization gradient. Advances in Neural Information Processing Systems. Barcelona (Spain), December 2016.
  [pdf] [arxiv] [bibtex]
• M. Rudolph, F. J. R. Ruiz, S. Mandt, and D. M. Blei. Exponential family embeddings. Advances in Neural Information Processing Systems. Barcelona (Spain), December 2016.
  [pdf] [arxiv] [bibtex] [spotlight video] [b-emb code] [p-emb code]
• F. J. R. Ruiz, M. K. Titsias, and D. M. Blei. Overdispersed black-box variational inference. Uncertainty in Artificial Intelligence. Jersey City, NJ (USA), June 2016. Plenary presentation.
  [pdf] [supplement] [bibtex]

2015

• I. Valera, F. J. R. Ruiz, L. Svensson, and F. Perez-Cruz. Infinite factorial dynamical model. Neural Information Processing Systems (NIPS). Montreal (Canada), December 2015.
  [pdf] [supplement] [code] [bibtex]
• I. Valera, F. J. R. Ruiz, L. Svensson, and F. Perez-Cruz. A Bayesian nonparametric approach for blind multiuser channel estimation. European Signal Processing Conference (EUSIPCO). Nice (France), August 2015. Invited contribution.
  [pdf] [bibtex]

2014

• I. Valera, F. J. R. Ruiz, and F. Perez-Cruz. Infinite factorial unbounded hidden Markov model for blind multiuser channel estimation. 4th International Workshop on Cognitive Information Processing (CIP). Copenhaguen (Denmark), May 2014.
  [pdf] [bibtex]
• P. Gopalan, F. J. R. Ruiz, R. Ranganath, and D. M. Blei. Bayesian nonparametric Poisson factorization for recommendation systems. International Conference on Artificial Intelligence and Statistics (AISTATS). Reykjavik (Iceland), April 2014.
  [pdf] [supplement] [bibtex]

2012

• F. J. R. Ruiz, I. Valera, C. Blanco, and F. Perez-Cruz. Bayesian nonparametric modeling of suicide attempts. Neural Information Processing Systems (NIPS), 25:1862-1870. Lake Tahoe (USA), December 2012. Spotlight session.
  [pdf] [spotlight] [bibtex]

2011

• F. J. R. Ruiz and F. Perez-Cruz. Zero-error codes for the noisy-typewriter channel. IEEE Information Theory Workshop (ITW). Paraty (Brazil), October 2011.
  [pdf] [bibtex]

2010

• L. Romero Cortes, F. J. Rodriguez Ruiz, S. Martin Lopez, M. Alcon Camas, M. Gonzalez Herraez, P. Corredera Guillen, and J. D. Ania Castanon. Sensado distribuido Brillouin en un laser ultralargo (Brillouin distributed sensing in an ultralong laser). XXV National Simposium of International Union of Radio Science (URSI), Bilbao (Spain), September 2010.
  [pdf] [bibtex]

Thesis

• Bayesian nonparametrics for time series modeling. June 2015.
  [pdf] [slides]

Talks

• F. J. R. Ruiz. Variational Inference with Implicit and Semi-Implicit Distributions. Nordic Probabilistic AI (ProbAI) Summer School (Trondheim, Norway), June 2021.
  [slides]
• F. J. R. Ruiz, M. K. Titsias, T. Cemgil, and A. Doucet Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. University Carlos III in Madrid (Madrid, Spain), December 2020.
  [slides]
• F. J. R. Ruiz, M. K. Titsias, T. Cemgil, and A. Doucet Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. Deep Learning Sydney meetup (Sydney, Australia), October 2020.
  [slides]
• F. J. R. Ruiz, S. Athey, and D. M. Blei. SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. Seminar at Imperial College (London, UK), April 2020.
  [slides]
• F. J. R. Ruiz. A Contrastive Divergence for Combining Variational Inference and MCMC. Max Planck Institute for Intelligent Systems (Tuebingen, Germany), October 2019.
  [slides]
• F. J. R. Ruiz. Variational Inference with Implicit and Semi-Implicit Distributions. Deep|Bayes Summer School (Moscow, Russia), August 2019.
  [slides]
• F. J. R. Ruiz and M. K. Titsias. A Contrastive Divergence for Combining Variational Inference and MCMC. EMS: Special Session on "Recent advances in simulation-based methods for numerical integration and inference" (Palermo, Italy), July 2019.
  [slides]
• F. J. R. Ruiz and M. K. Titsias. Beyond the Mean-Field Family: Variational Inference with Implicit Distributions. Linkoping University (Linkoping, Sweden), May 2019.
  [slides]
• M. K. Titsias and F. J. R. Ruiz. Unbiased Implicit Variational Inference. University Carlos III in Madrid (Madrid, Spain), December 2018.
  [slides]
• F. J. R. Ruiz, S. Athey, and D. M. Blei. SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. Amazon Research (Cambridge, UK), November 2018.
  
• F. J. R. Ruiz, S. Athey, and D. M. Blei. SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. Bayesian Econometric Methods and Applications Workshop at ISBA (Edinburgh, UK), June 2018.
  
• F. J. R. Ruiz, S. Athey, and D. M. Blei. SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. Barcelona GSE Summer Forum (Data Science for Economics Workshop, Barcelona, Spain), June 2018.
  [slides]
• F. J. R. Ruiz, S. Athey, and D. M. Blei. SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. Universitat de Barcelona (Barcelona, Spain), June 2018.
  [slides]
• F. J. R. Ruiz, M. K. Titsias, A. B. Dieng, and D. M. Blei. Augment and Reduce: Stochastic Inference for Large Categorical Distributions. Stony Brook University (NY, USA), April 2018.
  [slides]
• F. J. R. Ruiz, S. Athey, and D. M. Blei. Item Embeddings for Demand Estimation in Economics. Annual Machine Learning Symposium. New York Academy of Sciences (NY, USA), March 2017. Poster presentation. IBM best poster presenter award (5th place).
• M. Rudolph, F. J. R. Ruiz, S. Mandt, S. Athey, and D. M. Blei. Exponential Family Embeddings: Application to Economics. University Carlos III in Madrid (Spain), December 2016.
  [slides]
• C. Naesseth, F. J. R. Ruiz, S. Linderman, M. K. Titsias, and D. M. Blei. Reparameterizing Challenging Distributions. Machine Learning Group. University of Cambridge (UK), November 2016.
  [slides]
• I. Valera, F. J. R. Ruiz, L. Svensson, and F. Perez-Cruz. Infinite Factorial Dynamical Model. Annual Machine Learning Symposium. New York Academy of Sciences (NY, USA), March 2016.
  [slides]
• F. J. R. Ruiz, N. D. Lawrence, and J. Hensman. True Natural Gradient of Collapsed Variational Bayes. Group Talk. Signal Theory and Processing Group. University Carlos III in Madrid (Spain), November 2014.
  [slides]
• F. J. R. Ruiz, I. Valera, C. Blanco, and F. Perez-Cruz. Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders. Machine Learning Group Talk. Sheffield Institute for Translational Neuroscience. University of Sheffield (UK), July 2014.
  [slides]
• P. Gopalan, F. J. R. Ruiz, R. Ranganath, and D. M. Blei. Bayesian Nonparametric Poisson Factorization for Recommendation Systems. Group Talk. Signal Theory and Processing Group. University Carlos III in Madrid (Spain), February 2014.
  [slides]
• F. J. R. Ruiz, I. Valera, C. Blanco, and F. Perez-Cruz. Bayesian Nonparametric Comorbidity Analysis of Psychiatric Disorders. Machine Learning Group Talk. Computer Science Department. Princeton University (NJ, USA), October 2013.
  [slides]
• F. J. R. Ruiz, I. Valera, and F. Perez-Cruz. Infinite Factorial Unbounded-Memory Finite State Machines. Group Talk. Signal Theory and Processing Group. University Carlos III in Madrid (Spain), June 2013.
• I. Valera, F. J. R. Ruiz, and F. Perez-Cruz. Infinite Factorial Infinite Hidden Markov Model. Group Talk. Signal Theory and Processing Group. University Carlos III in Madrid (Spain), October 2012.
• F. J. R. Ruiz, I. Valera, C. Blanco, and F. Perez-Cruz. Bayesian Nonparametric Modeling of Suicide Attempts. Group Talk. Signal Theory and Processing Group. University Carlos III in Madrid (Spain), October 2012.
  [slides]
• F. J. R. Ruiz and F. Perez-Cruz. Zero-error codes for the noisy-typewriter channel. Summer Research Institute. Lausanne (Switzerland), June 2011.
• F. J. R. Ruiz and F. Perez-Cruz. Zero-error codes for the noisy-typewriter channel. Group Talk. Signal Theory and Processing Group. University Carlos III in Madrid (Spain), February 2011.