Publications

2024

  1. Mirramezani, M., Oktay, D., & Adams, R. P. (2024). A rapid and automated computational approach to the design of multistable soft actuators. Computer Physics Communicationsn. [PDF] bibtex/details
  2. Liu, S., Ramadge, P. J., & Adams, R. P. (2024). Generative Marginalization Models. Proceedings of the 41st International Conference on Machine Learning (ICML). [PDF] bibtex/details
  3. Novick, A., Cai, D., Nguyen, Q., Garnett, R., Adams, R. P., & Toberer, E. (2024). Probabilistic Prediction of Material Stability: Integrating Convex Hulls into Active Learning. ArXiv Preprint ArXiv:2402.15582. [PDF] bibtex/details
  4. Bordiga, G., Medina, E., Jafarzadeh, S., Boesch, C., Adams, R. P., Tournat, V., & Bertoldi, K. (2024). Automated discovery of reprogrammable nonlinear dynamic metamaterials. ArXiv Preprint ArXiv:2403.08078. [PDF] bibtex/details

2023

  1. Sun, X., Cai, C., Adams, R. P., & Rusinkiewicz, S. (2023). Gradient-Based Dovetail Joint Shape Optimization for Stiffness. Symposium on Computational Fabrication. [PDF] bibtex/details
  2. Li, M., Callaway, F., Thompson, W., Adams, R. P., & Griffiths, T. (2023). Learning to learn functions. Cognitive Science, 47(4). [PDF] bibtex/details
  3. Pastrana, R., Oktay, D., Adams, R. P., & Adriaenssens, S. (2023). JAX FDM: A differentiable solver for inverse form-finding. ArXiv Preprint ArXiv:2307.12407. [PDF] bibtex/details
  4. Adams, R. P., & Orbanz, P. (2023). Representing and Learning Functions Invariant Under Crystallographic Groups. ArXiv Preprint ArXiv:2306.05261. [PDF] bibtex/details
  5. Oktay, D., Mirramezani, M., Medina, E., & Adams, R. P. (2023). Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity. The Eleventh International Conference on Learning Representations. [PDF] bibtex/details
  6. Sun, X. (2023). Gradient-Based Shape Optimization for Engineering Using Machine Learning [PhD thesis]. Princeton University. [PDF] bibtex/details
  7. Cai, D. (2023). Probabilistic Inference When the Model Is Wrong [PhD thesis]. Princeton University. [PDF] bibtex/details
  8. Liu, S. (2023). Scalable and Interpretable Learning with Probabilistic Models for Knowledge Discovery [PhD thesis]. Princeton University. [PDF] bibtex/details
  9. Sun, X., Roeder, G., Xue, T., Adams, R. P., & Rusinkiewicz, S. (2023). More Stiffness with Less Fiber: End-to-End Fiber Path Optimization for 3D-Printed Composites. Symposium on Computational Fabrication. [PDF] bibtex/details

2022

  1. Seff, A., Zhou, W., Richardson, N., & Adams, R. P. (2022). Vitruvion: A Generative Model of Parametric CAD Sketches. International Conference on Learning Representations. [PDF] bibtex/details
  2. Torres, J. A. G., Lau, S. H., Anchuri, P., Stevens, J. M., Tabora, J. E., Li, J., Borovika, A., Adams, R. P., & Doyle, A. G. (2022). A multi-objective active learning platform and web app for reaction optimization. Journal of the American Chemical Society, 144(43), 19999–20007. [PDF] bibtex/details
  3. Cai, D., & Adams, R. P. (2022). Multi-fidelity Monte Carlo: a pseudo-marginal approach. Advances in Neural Information Processing Systems, 35, 21654–21667. [PDF] bibtex/details
  4. Qin, T., Beatson, A., Oktay, D., McGreivy, N., & Adams, R. P. (2022). Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh. ArXiv Preprint ArXiv:2211.01604. [PDF] bibtex/details
  5. Rahme, J. (2022). Learning Algorithms for Intelligent Agents and Mechanisms [PhD thesis]. Princeton University. [PDF] bibtex/details
  6. Xue, T. (2022). Computational modeling and design of mechanical metamaterials: A machine learning approach [PhD thesis]. Princeton University. [PDF] bibtex/details

2021

  1. Seff, A. (2021). Learning-Aided Design with Structured Generative Modeling [PhD thesis]. Princeton University. [PDF] bibtex/details
  2. Gundersen, G. W., Cai, D., Zhou, C., Engelhardt, B. E., & Adams, R. P. (2021). Active multi-fidelity Bayesian online changepoint detection. Uncertainty in Artificial Intelligence, 1916–1926. [PDF] bibtex/details
  3. Zoltowski, D., Cai, D., & Adams, R. P. (2021). Slice Sampling Reparameterization Gradients. Advances in Neural Information Processing Systems, 34, 23532–23544. [PDF] bibtex/details
  4. Sun, X., Xue, T., Rusinkiewicz, S., & Adams, R. P. (2021). Amortized synthesis of constrained configurations using a differentiable surrogate. Advances in Neural Information Processing Systems, 34, 18891–18906. [PDF] bibtex/details
  5. Ghosh, D., Rahme, J., Kumar, A., Zhang, A., Adams, R. P., & Levine, S. (2021). Why generalization in rl is difficult: Epistemic pomdps and implicit partial observability. Advances in Neural Information Processing Systems, 34, 25502–25515. [PDF] bibtex/details
  6. Kumar, A. R., Liu, S., Fisac, J. F., Adams, R. P., & Ramadge, P. J. (2021). ProBF: Learning Probabilistic Safety Certificates with Barrier Functions. ArXiv Preprint ArXiv:2112.12210. [PDF] bibtex/details
  7. Shields, B. J., Stevens, J., Li, J., Parasram, M., Damani, F., Alvarado, J. I. M., Janey, J. M., Adams, R. P., & Doyle, A. G. (2021). Bayesian reaction optimization as a tool for chemical synthesis. Nature, 590(7844), 89–96. [PDF] bibtex/details
  8. Beatson, A. (2021). Learned surrogates and stochastic gradients for accelerating numerical modeling, simulation, and design [PhD thesis]. Princeton University. [PDF] bibtex/details
  9. Oktay, D., McGreivy, N., Aduol, J., Beatson, A., & Adams, R. P. (2021). Randomized Automatic Differentiation. International Conference on Learning Representations. [PDF] bibtex/details

2020

  1. Ash, J. (2020). Towards Flexible Active And Online Learning With Neural Networks [PhD thesis]. Princeton University. [PDF] bibtex/details
  2. Ash, J., & Adams, R. P. (2020). On warm-starting neural network training. Advances in Neural Information Processing Systems, 33, 3884–3894. [PDF] bibtex/details
  3. Beatson, A., Ash, J., Roeder, G., Xue, T., & Adams, R. P. (2020). Learning composable energy surrogates for pde order reduction. Advances in Neural Information Processing Systems, 33, 338–348. [PDF] bibtex/details
  4. Liu, S., Sun, X., Ramadge, P. J., & Adams, R. P. (2020). Task-agnostic amortized inference of gaussian process hyperparameters. Advances in Neural Information Processing Systems, 33, 21440–21452. [PDF] bibtex/details
  5. Seff, A., Ovadia, Y., Zhou, W., & Adams, R. P. (2020). Sketchgraphs: A large-scale dataset for modeling relational geometry in computer-aided design. ArXiv Preprint ArXiv:2007.08506. [PDF] bibtex/details
  6. Luo, Y., Beatson, A., Norouzi, M., Zhu, J., Duvenaud, D., Adams, R. P., & Chen, R. T. Q. (2020). SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models. International Conference on Learning Representations. [PDF] bibtex/details
  7. Xue, T., Beatson, A., Chiaramonte, M., Roeder, G., Ash, J. T., Menguc, Y., Adriaenssens, S., Adams, R. P., & Mao, S. (2020). A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation. Soft Matter, 16(32), 7524–7534. [PDF] bibtex/details
  8. Xue, T., Beatson, A., Adriaenssens, S., & Adams, R. P. (2020). Amortized finite element analysis for fast pde-constrained optimization. International Conference on Machine Learning, 10638–10647. [PDF] bibtex/details

2019

  1. Wei, J. N., Belanger, D., Adams, R. P., & Sculley, D. (2019). Rapid prediction of electron–ionization mass spectrometry using neural networks. ACS Central Science, 5(4), 700–708. [PDF] bibtex/details
  2. Rahme, J., & Adams, R. P. (2019). A theoretical connection between statistical physics and reinforcement learning. ArXiv Preprint ArXiv:1906.10228. [PDF] bibtex/details
  3. Beatson, A., & Adams, R. P. (2019). Efficient optimization of loops and limits with randomized telescoping sums. International Conference on Machine Learning, 534–543. [PDF] bibtex/details
  4. Seff, A., Zhou, W., Damani, F., Doyle, A., & Adams, R. P. (2019). Discrete object generation with reversible inductive construction. Advances in Neural Information Processing Systems, 32. [PDF] bibtex/details
  5. Fedorov, I., Adams, R. P., Mattina, M., & Whatmough, P. (2019). Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers. Advances in Neural Information Processing Systems, 32. [PDF] bibtex/details
  6. Zhou, W., Veitch, V., Austern, M., Adams, R. P., & Orbanz, P. (2019). Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach. International Conference on Learning Representations. [PDF] bibtex/details
  7. Regier, J., Miller, A. C., Schlegel, D., Adams, R. P., & Mcauliffe, J. D. (2019). APPROXIMATE INFERENCE FOR CONSTRUCTING ASTRONOMICAL CATALOGS FROM IMAGES. The Annals of Applied Statistics, 13(3), 1884–1926. [PDF] bibtex/details

2018

  1. Saeedi, A. (2018). Latent variable models for understanding user behavior in software applicationsg [PhD thesis]. Massachusetts Institute of Technology. [PDF] bibtex/details
  2. Gómez-Bombarelli, R., Wei, J., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018). Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules. ACS Central Science, 4(2), 268–276. [PDF] bibtex/details
  3. Reshef, Y. A., Finucane, H. K., Kelley, D. R., Gusev, A., Kotliar, D., Ulirsch, J. C., Hormozdiari, F., Nasser, J., O’Connor, L., Van De Geijn, B., & others. (2018). Detecting genome-wide directional effects of transcription factor binding on polygenic disease risk. Nature Genetics, 50(10), 1483–1493. [PDF] bibtex/details
  4. Gilmer, J., Adams, R. P., Goodfellow, I., Andersen, D., & Dahl, G. E. (2018). Motivating the rules of the game for adversarial example research. ArXiv Preprint ArXiv:1807.06732. [PDF] bibtex/details
  5. Adams, R. P., Pennington, J., Johnson, M. J., Smith, J., Ovadia, Y., Patton, B., & Saunderson, J. (2018). Estimating the spectral density of large implicit matrices. ArXiv Preprint ArXiv:1802.03451. [PDF] bibtex/details
  6. Biswas, S., Kuznetsov, G., Ogden, P. J., Conway, N. J., Adams, R. P., & Church, G. M. (2018). Toward machine-guided design of proteins. BioRxiv, 337154. [PDF] bibtex/details
  7. Miller, A. (2018). Advances in Monte Carlo Variational Inference and Applied Probabilistic Modeling [PhD thesis]. Harvard University. [PDF] bibtex/details
  8. Saeedi, A., Hoffman, M. D., DiVerdi, S. J., Ghandeharioun, A., Johnson, M. J., & Adams, R. P. (2018). Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models. Proceedings of the 21st International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details

2017

  1. Linderman, S. W., Johnson, M. J., Miller, A. C., Adams, R. P., Blei, D. M., & Paninski, L. (2017). Recurrent Switching Linear Dynamical Systems. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details
  2. Miller, A. C., Foti, N. J., & Adams, R. P. (2017). Variational Boosting: Iteratively Refining Posterior Approximations. Proceedings of the 34th International Conference on Machine Learning (ICML). [PDF] bibtex/details
  3. Huggins, J., Adams, R. P., & Broderick, T. (2017). PASS-GLM: Polynomial Approximate Sufficient Statistics for Scalable Bayesian GLM Inference. Advances in Neural Information Processing Systems (NIPS) 30. [PDF] bibtex/details
  4. Miller, A. C., Foti, N. J., d’Amour, A., & Adams, R. P. (2017). Reducing Reparameterization Gradient Variance. Advances in Neural Information Processing Systems (NIPS) 30. [PDF] bibtex/details
  5. Chung, S. Y. (2017). Statistical Mechanics of Neural Processing of Object Manifolds [PhD thesis]. Harvard University. [PDF] bibtex/details

2016

  1. Rippel, O. (2016). Sculpting representations for deep learning [PhD thesis]. Massachusetts Institute of Technology. [PDF] bibtex/details
  2. Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016). Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE, 104(1), 148–175. [PDF] bibtex/details
  3. Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M. A., Chae, H. S., Einzinger, M., Ha, D.-G., Wu, T., Markopolous, G., Jeon, S., Kang, H., Miyazaki, H., Numata, M., Kim, S., Huang, W., Hong, S. I., Baldo, M., … Aspuru-Guzik, A. (2016). Design of Efficient Molecular Organic Light-Emitting Diodes by a High-Throughput Virtual Screening and Experimental Approach. Nature Materials, 15(10), 1120–1127. [PDF] bibtex/details
  4. Doshi-Velez, F., Wallace, B., & Adams, R. P. (2016). Graph-Sparse LDA: A Topic Model with Structured Sparsity. Proceedings of the 29th AAAI Conference on Artificial Intelligence. [PDF] bibtex/details
  5. Hennek, J. W., Kumar, A. A., Wiltschko, A. B., Patton, M., Lee, S. Y. R., Brugnara, C., Adams, R. P., & Whitesides, G. M. (2016). Diagnosis of Iron Deficiency Anemia Using Density-based Fractionation of Red Blood Cells. Lab on a Chip, 16, 3929–3939. [PDF] bibtex/details
  6. Angelino, E., Johnson, M. J., & Adams, R. P. (2016). Patterns of Scalable Bayesian Inference. Foundations and Trends in Machine Learning, 9(2-3), 119–247. [PDF] bibtex/details
  7. Rao, V., Adams, R. P., & Dunson, D. B. (2016). Bayesian Inference for Matérn Repulsive Processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(3), 877–897. [PDF] bibtex/details
  8. Tarlow, D., Gaunt, A., Adams, R. P., & Zemel, R. S. (2016). Factorizing Shortest Paths with Randomized Optimum Models. In Perturbations, Optimization, and Statistics. MIT Press. [PDF] bibtex/details
  9. Duvenaud, D., Maclaurin, D., & Adams, R. P. (2016). Early Stopping is Nonparametric Variational Inference. Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details
  10. Wan, Q., Adams, R. P., & Howe, R. D. (2016). Variability and Predictability in Tactile Sensing During Grasping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). [PDF] bibtex/details
  11. Saeedi, A., Hoffman, M. D., Johnson, M. J., & Adams, R. P. (2016). The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM. Proceedings of the 33rd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  12. Hernández-Lobato, D., Hernández-Lobato, J. M., Shah, A., & Adams, R. P. (2016). Predictive Entropy Search for Multi-Objective Bayesian Optimization. Proceedings of the 33rd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  13. Johnson, M. J., Duvenaud, D., Wiltschko, A. B., Datta, S. R., & Adams, R. P. (2016). Composing Graphical Models with Neural Networks for Structured Representations and Fast Inference. Advances in Neural Information Processing Systems (NIPS) 29. [PDF] bibtex/details
  14. Linderman, S. W., Adams, R. P., & Pillow, J. W. (2016). Bayesian Latent Structure Discovery from Multi-neuron Recordings. Advances in Neural Information Processing Systems (NIPS) 29. [PDF] bibtex/details
  15. Linderman, S. (2016). Bayesian Methods for Discovering Structure in Neural Spike Trains [PhD thesis]. Harvard University. [PDF] bibtex/details
  16. Maclaurin, D. (2016). Modeling, Inference and Optimization with Composable Differentiable Procedures [PhD thesis]. Harvard University. [PDF] bibtex/details
  17. Grosse, R. B., Ghahramani, Z., & Adams, R. P. (2016). Sandwiching the Marginal Likelihood Using Bidirectional Monte Carlo. [PDF] bibtex/details

2015

  1. Gelbart, M. A. (2015). Constrained Bayesian Optimization and Applications [PhD thesis]. Harvard University. [PDF] bibtex/details
  2. Nemati, S., & Adams, R. P. (2015). Identifying Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series. In Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press. [PDF] bibtex/details
  3. Lehman, L.-W., Johnson, M. J., Nemati, S., Adams, R. P., & Mark, R. (2015). Bayesian Nonparametric Learning of Switching Dynamics in Cohort Physiological Time Series: Application in Critical Care Patient Monitoring. In Advanced State Space Methods for Neural and Clinical Data. Cambridge University Press. bibtex/details
  4. Maclaurin, D., Duvenaud, D., & Adams, R. P. (2015). Gradient-based Hyperparameter Optimization through Reversible Learning. Proceedings of the 32nd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  5. Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M. M. A., Prabhat, & Adams, R. P. (2015). Scalable Bayesian Optimization Using Deep Neural Networks. Proceedings of the 32nd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  6. Hernández-Lobato, J. M., & Adams, R. P. (2015). Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks. Proceedings of the 32nd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  7. Hernández-Lobato, J. M., Gelbart, M. A., Hoffman, M. W., Adams, R. P., & Ghahramani, Z. (2015). Predictive Entropy Search for Bayesian Optimization with Unknown Constraints. Proceedings of the 32nd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  8. Regier, J., Miller, A. C., McAuliffe, J., Adams, R. P., Hoffman, M. D., Lang, D., Schlegel, D., & Prabhat. (2015). Celeste: Variational Inference for a Generative Model of Astronomical Images. Proceedings of the 32nd International Conference on Machine Learning (ICML). [PDF] bibtex/details
  9. Rippel, O., Snoek, J., & Adams, R. P. (2015). Spectral Representations for Convolutional Neural Networks. Advances in Neural Information Processing Systems (NIPS) 28. [PDF] bibtex/details
  10. Wiltschko, A. B., Johnson, M. J., Iurilli, G., Peterson, R. E., Katon, J. M., Pashkovski, S. L., Abraira, V. E., Adams, R. P., & Datta, S. R. (2015). Mapping Sub-Second Structure in Mouse Behavior. Neuron, 88(6), 1121–1135. [PDF] bibtex/details
  11. Miller, A. C., Wu, A., Regier, J., McAuliffe, J., Lang, D., Prabhat, Schlegel, D., & Adams, R. P. (2015). A Gaussian Process Model of Quasar Spectral Energy Distributions. Advances in Neural Information Processing Systems (NIPS) 28. [PDF] bibtex/details
  12. Linderman, S. W., Johnson, M. J., & Adams, R. P. (2015). Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation. Advances in Neural Information Processing Systems (NIPS) 28. [PDF] bibtex/details
  13. Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T. D., Aspuru-Guzik, A., & Adams, R. P. (2015). Convolutional Networks on Graphs for Learning Molecular Fingerprints. Advances in Neural Information Processing Systems (NIPS) 28. [PDF] bibtex/details
  14. Linderman, S. W., & Adams, R. P. (2015). Scalable Bayesian Inference for Excitatory Point Process Networks. [PDF] bibtex/details
  15. Lehman, L.-W., Adams, R. P., Mayaud, L., Moody, G., Malhotra, A., Mark, R., & Nemati, S. (2015). A Physiological Time Series Dynamics-Based Approach to Patient Monitoring and Outcome Prediction. IEEE Journal of Biomedical and Health Informatics, 19(3), 1068–1076. [PDF] bibtex/details

2014

  1. Angelino, E. L. (2014). Accelerating Markov chain Monte Carlo via parallel predictive prefetching [PhD thesis]. Harvard University. bibtex/details
  2. Duvenaud, D., Rippel, O., Adams, R. P., & Ghahramani, Z. (2014). Avoiding Pathologies in Very Deep Networks. Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details
  3. Waterland, A., Angelino, E., Adams, R. P., Appavoo, J., & Seltzer, M. (2014). ASC: Automatically Scalable Computation. Proceedings of the 19th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). [PDF] bibtex/details
  4. Gao, X. A., Mao, A., Chen, Y., & Adams, R. P. (2014). Trick or Treat: Putting Peer Prediction to the Test. Proceedings of the 15th ACM Conference on Economics and Computation (EC). [PDF] bibtex/details
  5. Affandi, R. H., Fox, E. B., Adams, R. P., & Taskar, B. (2014). Learning the Parameters of Determinantal Point Process Kernels. Proceedings of the 31st International Conference on Machine Learning (ICML). [PDF] bibtex/details
  6. Rippel, O., Gelbart, M. A., & Adams, R. P. (2014). Learning Ordered Representations with Nested Dropout. Proceedings of the 31st International Conference on Machine Learning (ICML). [PDF] bibtex/details
  7. Snoek, J., Swersky, K., Zemel, R. S., & Adams, R. P. (2014). Input Warping for Bayesian Optimization of Non-Stationary Functions. Proceedings of the 31st International Conference on Machine Learning (ICML). [PDF] bibtex/details
  8. Miller, A. C., Bornn, L., Adams, R. P., & Goldsberry, K. (2014). Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. Proceedings of the 31st International Conference on Machine Learning (ICML). [PDF] bibtex/details
  9. Linderman, S. W., & Adams, R. P. (2014). Discovering Latent Network Structure in Point Process Data. Proceedings of the 31st International Conference on Machine Learning (ICML). [PDF] bibtex/details
  10. Maclaurin, D., & Adams, R. P. (2014). Firefly Monte Carlo: Exact MCMC with Subsets of Data. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). [PDF] bibtex/details
  11. Gelbart, M. A., Snoek, J., & Adams, R. P. (2014). Bayesian Optimization with Unknown Constraints. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). [PDF] bibtex/details
  12. Angelino, E., Kohler, E., Waterland, A., Seltzer, M., & Adams, R. P. (2014). Accelerating MCMC via Parallel Predictive Prefetching. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence (UAI). [PDF] bibtex/details
  13. Linderman, S. W., Stock, C. H., & Adams, R. P. (2014). A Framework for Studying Synaptic Plasticity with Neural Spike Train Data. Advances in Neural Information Processing Systems (NIPS) 27. [PDF] bibtex/details
  14. Swersky, K., Snoek, J., & Adams, R. P. (2014). Freeze-Thaw Bayesian Optimization. [PDF] bibtex/details
  15. Engelhardt, B. E., & Adams, R. P. (2014). Bayesian Structured Sparsity from Gaussian Fields. [PDF] bibtex/details
  16. Nishihara, R., Murray, I., & Adams, R. P. (2014). Parallel MCMC with Generalized Elliptical Slice Sampling. Journal of Machine Learning Research, 15(1), 2087–2112. [PDF] bibtex/details

2013

  1. Rippel, O., & Adams, R. P. (2013). High-Dimensional Probability Estimation with Deep Density Models. [PDF] bibtex/details
  2. Wilson, A. G., & Adams, R. P. (2013). Gaussian Process Kernels for Pattern Discovery and Extrapolation. Proceedings of the 30th International Conference on Machine Learning (ICML). [PDF] bibtex/details
  3. Waterland, A., Angelino, E., Cubuk, E. D., Kaxiras, E., Adams, R. P., Appavoo, J., & Seltzer, M. (2013). Computational Caches. Proceedings of the International Systems and Storage Conference (SYSTOR). [PDF] bibtex/details
  4. Lehman, L.-W., Nemati, S., Adams, R. P., Moody, G., Malhotra, A., & Mark, R. (2013). Tracking Progression of Patient State of Health in Critical Care Using Inferred Shared Dynamics in Physiological Time Series. Proceedings of the 35th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). [PDF] bibtex/details
  5. Nemati, S., Lehman, L.-W., & Adams, R. P. (2013). Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series. Proceedings of the 35th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). [PDF] bibtex/details
  6. Dechter, E., Malmaud, J., Adams, R. P., & Tenenbaum, J. B. (2013). Bootstrap Learning Via Modular Concept Discovery. Proceedings of the 23rd International Joint Conference on Artificial Intelligence. [PDF] bibtex/details
  7. Swersky, K., Snoek, J., & Adams, R. P. (2013). Multi-Task Bayesian Optimization. Advances in Neural Information Processing Systems (NIPS) 26. [PDF] bibtex/details
  8. Napp, N., & Adams, R. P. (2013). Message Passing Inference with Chemical Reaction Networks. Advances in Neural Information Processing Systems (NIPS) 26. [PDF] bibtex/details
  9. Snoek, J., Adams, R. P., & Zemel, R. S. (2013). A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data. Advances in Neural Information Processing Systems (NIPS) 26. [PDF] bibtex/details
  10. Zou, J. Y., Hsu, D., Parkes, D., & Adams, R. P. (2013). Contrastive Learning Using Spectral Methods. Advances in Neural Information Processing Systems (NIPS) 26. [PDF] bibtex/details
  11. Lovell, D., Malmaud, J., Adams, R. P., & Mansinghka, V. K. (2013). ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures. [PDF] bibtex/details
  12. Tse, H. T. K., Gossett, D. R., Moon, Y. S., Masaeli, M., Sohsman, M., Ying, Y., Mislick, K., Adams, R. P., Rao, J., & DiCarlo, D. (2013). Quantitative Diagnosis of Malignant Pleural Effusions by Single-Cell Mechanophenotyping. Science Translational Medicine, 5(212). [PDF] bibtex/details

2012

  1. Swersky, K., Tarlow, D., Sutskever, I., Salakhutdinov, R., Zemel, R. S., & Adams, R. P. (2012). Cardinality Restricted Boltzmann Machines. Advances in Neural Information Processing Systems (NIPS) 25. [PDF] bibtex/details
  2. Snoek, J., Adams, R. P., & Larochelle, H. (2012). On Nonparametric Guidance for Learning Autoencoder Representations. Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details
  3. Chua, J., Givoni, I., Adams, R. P., & Frey, B. (2012). Bayesian Painting by Numbers: Flexible Priors for Colour-Invariant Object Recognition. In Machine Learning for Computer Vision. Springer. [PDF] bibtex/details
  4. Dahl, G. E., Adams, R. P., & Larochelle, H. (2012). Training Restricted Boltzmann Machines on Word Observations. Proceedings of the 29th International Conference on Machine Learning (ICML). [PDF] bibtex/details
  5. Tarlow, D., & Adams, R. P. (2012). Revisiting Uncertainty in Graph Cut Solutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [PDF] bibtex/details
  6. Chua, J., Givoni, I., Adams, R. P., & Frey, B. (2012). Learning Structural Element Patch Models with Hierarchical Palettes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). [PDF] bibtex/details
  7. Tarlow, D., Swersky, K., Zemel, R. S., Adams, R. P., & Frey, B. (2012). Fast Exact Inference in Recursive Cardinality Models. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence (UAI). [PDF] bibtex/details
  8. Lehman, L.-W., Nemati, S., Adams, R. P., & Mark, R. (2012). Discovering Shared Dynamics in Physiological Signals: Application to Patient Monitoring in ICU. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). [PDF] bibtex/details
  9. Nemati, S., Lehman, L.-W., Adams, R. P., & Malhotra, A. (2012). Discovering Shared Cardiovascular Dynamics within a Patient Cohort. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). [PDF] bibtex/details
  10. Snoek, J., Adams, R. P., & Larochelle, H. (2012). Nonparametric Guidance of Autoencoder Representations Using Label Information. Journal of Machine Learning Research, 13, 2567–2588. [PDF] bibtex/details
  11. Swersky, K., Tarlow, D., Adams, R. P., Zemel, R. S., & Frey, B. (2012). Probabilistic n-choose-k Models for Classification and Ranking. Advances in Neural Information Processing Systems (NIPS) 25. [PDF] bibtex/details
  12. Zou, J. Y., & Adams, R. P. (2012). Priors for Diversity in Generative Latent Variable Models. Advances in Neural Information Processing Systems (NIPS) 25. [PDF] bibtex/details
  13. Snoek, J., Larochelle, H., & Adams, R. P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NIPS) 25. [PDF] bibtex/details
  14. Tarlow, D., Adams, R. P., & Zemel, R. S. (2012). Randomized Optimum Models for Structured Prediction. Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details

2011

  1. Adams, R. P., & Zemel, R. S. (2011). Ranking via Sinkhorn Propagation. [PDF] bibtex/details

2010

  1. Adams, R. P., Dahl, G. E., & Murray, I. (2010). Incorporating Side Information into Probabilistic Matrix Factorization Using Gaussian Processes. Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI). [PDF] bibtex/details
  2. Murray, I., Adams, R. P., & MacKay, D. J. C. (2010). Elliptical Slice Sampling. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details
  3. Adams, R. P., Wallach, H. M., & Ghahramani, Z. (2010). Learning the Structure of Deep Sparse Graphical Models. Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS). [PDF] bibtex/details
  4. Murray, I., & Adams, R. P. (2010). Slice Sampling Covariance Hyperparameters in Latent Gaussian Models. Advances in Neural Information Processing Systems (NIPS) 23. [PDF] bibtex/details
  5. Adams, R. P., Ghahramani, Z., & Jordan, M. I. (2010). Tree-Structured Stick Breaking for Hierarchical Data. Advances in Neural Information Processing Systems (NIPS) 23. [PDF] bibtex/details

2009

  1. Adams, R. P., & Ghahramani, Z. (2009). Archipelago: Nonparametric Bayesian Semi-Supervised Learning. Proceedings of the 26th International Conference on Machine Learning (ICML). [PDF] bibtex/details
  2. Adams, R. P., Murray, I., & MacKay, D. J. C. (2009). The Gaussian Process Density Sampler. Advances in Neural Information Processing Systems 21 (NIPS). [PDF] bibtex/details
  3. Adams, R. P., Murray, I., & MacKay, D. J. C. (2009). Tractable Nonparametric Bayesian Inference in Poisson Processes with Gaussian Process Intensities. Proceedings of the 26th International Conference on Machine Learning (ICML). [PDF] bibtex/details
  4. Adams, R. P., Murray, I., & MacKay, D. J. C. (2009). Nonparametric Bayesian Density Modeling with Gaussian Processes. [PDF] bibtex/details
  5. Adams, R. P. (2009). Kernel Methods for Nonparametric Bayesian Inference of Probability Densities and Point Processes [PhD thesis]. University of Cambridge. [PDF] bibtex/details

2008

  1. Adams, R. P., & Stegle, O. (2008). Gaussian Process Product Models for Nonparametric Nonstationarity. Proceedings of the 25th International Conference on Machine Learning (ICML), 1–8. [PDF] bibtex/details

2007

  1. Adams, R. P., & MacKay, D. J. C. (2007). Bayesian Online Changepoint Detection. [PDF] bibtex/details