Jan-Willem van de Meent

Working Papers

  1. Siddharth, N., Paige, B., de Meent, V., Desmaison, A., Wood, F., Goodman, N. D., … others. (2017). Learning Disentangled Representations with Semi-Supervised Deep Generative Models. ArXiv Preprint ArXiv:1706.00400.

    Variational autoencoders (VAEs) learn representations of data by jointly training a probabilistic encoder and decoder network. Typically these models encode all features of the data into a single variable. Here we are interested in learning disentangled representations that encode distinct aspects of the data into separate variables. We propose to learn such representations using model architectures that generalize from standard VAEs, employing a general graphical model structure in the encoder and decoder. This allows us to train partially-specified models that make relatively strong assumptions about a subset of interpretable variables and rely on the flexibility of neural networks to learn representations for the remaining variables. We further define a general objective for semi-supervised learning in this model class, which can be approximated using an importance sampling procedure. We evaluate our framework’s ability to learn disentangled representations, both by qualitative exploration of its generative capacity, and quantitative evaluation of its discriminative ability on a variety of models and datasets.

    @article{siddharth_arxiv_2017,
      title = {Learning Disentangled Representations with Semi-Supervised Deep Generative Models},
      author = {Siddharth, N and Paige, Brooks and de Meent, Van and Desmaison, Alban and Wood, Frank and Goodman, Noah D and Kohli, Pushmeet and Torr, Philip HS and others},
      journal = {arXiv preprint arXiv:1706.00400},
      year = {2017}
    }
    
  2. Maha Alkhairy, J.-W. van de M., Byron Wallace. (2017). Learning Disentangled Representations for Text. Working Paper.

    Learning expressive representations of text is fundamental to many tasks in natural language processing. Here we propose learning disentangled representations that characterize distinct salient aspects within texts. We define a family of hierarchical deep generative models that associate individual words with aspects and employ a bag-of-word document model to represent the distribution over words conditioned on the aspect. We implement one model in this family, an auto-encoding mixture of NVDMs which we evaluate in both a fully unsupervised and a semi-supervised setting. As a diagnostic, we also evaluate an intermediate model that performs unsupervised learning for aspect representations but splits a document into aspects in a discriminative fashion. The total perplexity of aspect NVDMs in the intermediate model is lower than that of a single NVDM with an equivalent latent dimension, showing that splitting the latent space into aspects does not only aid interpretability, but can also yield more efficient representations.

    @article{alkhairy_icmlw_2017,
      title = {Learning Disentangled Representations for Text},
      author = {Maha Alkhairy, Byron Wallace, Jan-Willem van de Meent},
      journal = {Working paper},
      year = {2017}
    }
    

Conference

  1. Tolpin, D., van de Meent, J.-W., Yang, H., & Wood, F. (2016). Design and Implementation of Probabilistic Programming Language Anglican. In Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages (pp. 6:1–6:12). New York, NY, USA: ACM. http://doi.org/10.1145/3064899.3064910

    Anglican is a probabilistic programming system designed to interoperate with Clojure and other JVM languages. We introduce the programming language Anglican, outline our design choices, and discuss in depth the implementation of the Anglican language and runtime, including macro-based compilation, extended CPS-based evaluation model, and functional representations for probabilistic paradigms, such as a distribution, a random process, and an inference algorithm. We show that a probabilistic functional language can be implemented efficiently and integrated tightly with a conventional functional language with only moderate computational overhead. We also demonstrate how advanced probabilistic modelling concepts are mapped naturally to the functional foundation.

    @inproceedings{tolpin_ifl_2016,
      author = {Tolpin, David and van de Meent, Jan-Willem and Yang, Hongseok and Wood, Frank},
      title = {Design and Implementation of Probabilistic Programming Language Anglican},
      booktitle = {Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages},
      series = {IFL 2016},
      year = {2016},
      isbn = {978-1-4503-4767-9},
      location = {Leuven, Belgium},
      pages = {6:1--6:12},
      articleno = {6},
      numpages = {12},
      url = {http://doi.acm.org/10.1145/3064899.3064910},
      doi = {10.1145/3064899.3064910},
      acmid = {3064910},
      publisher = {ACM},
      address = {New York, NY, USA}
    }
    
  2. Rainforth, T., Le, T. A., van de Meent, J.-W., Osborne, M. A., & Wood, F. (2016). Bayesian Optimization for Probabilistic Programs. In Advances in Neural Information Processing Systems (pp. 280–288).
    @inproceedings{rainforth_nips_2016,
      title = {Bayesian {O}ptimization for {P}robabilistic {P}rograms},
      author = {Rainforth, Tom and Le, Tuan Anh and van de Meent, Jan-Willem and Osborne, Michael A and Wood, Frank},
      booktitle = {Advances in Neural Information Processing Systems},
      pages = {280--288},
      year = {2016},
      annote = {https://github.com/probprog/bopp}
    }
    
  3. Rainforth, T., Naesseth, C. A., Lindsten, F., Paige, B., van de Meent, J.-W., Doucet, A., & Wood, F. (2016). Interacting Particle Markov Chain Monte Carlo. In Proceedings of The 33rd International Conference on Machine Learning, (pp. 2616–2625).

    We introduce interacting particle Markov chain Monte Carlo (iPMCMC), a PMCMC method that introduces a coupling between multiple standard and conditional sequential Monte Carlo samplers. Like related methods, iPMCMC is a Markov chain Monte Carlo sampler on an extended space. We present empirical results that show significant improvements in mixing rates relative to both non- interacting PMCMC samplers and a single PMCMC sampler with an equivalent total computational budget. An additional advantage of the iPMCMC method is that it is suitable for distributed and multi-core architectures.

    @inproceedings{rainforth_icml_2016,
      booktitle = {Proceedings of The 33rd International Conference on Machine Learning,},
      pages = {2616–2625},
      title = {{Interacting Particle Markov Chain Monte Carlo}},
      author = {Rainforth, Tom and Naesseth, Christian A. and Lindsten, Fredrik and Paige, Brooks and van de Meent, Jan-Willem and Doucet, Arnaud and Wood, Frank},
      year = {2016}
    }
    
  4. van de Meent, J.-W., Paige, B., Tolpin, D., & Wood, F. (2016). Black-Box Policy Search with Probabilistic Programs. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 1195–1204.

    In this work, we explore how probabilistic programs can be used to represent policies in sequential decision problems. In this formulation, a probabilistic program is a black-box stochastic simulator for both the problem domain and the agent. We relate classic policy gradient techniques to recently introduced black-box variational methods which generalize to probabilistic program inference. We present case studies in the Canadian traveler problem, Rock Sample, and a benchmark for optimal diagnosis inspired by Guess Who. Each study illustrates how programs can efficiently represent policies using moderate numbers of parameters.

    @article{vandemeent_aistats_2016,
      journal = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics},
      author = {van de Meent, Jan-Willem and Paige, Brooks and Tolpin, David and Wood, Frank},
      pages = {1195–1204},
      title = {{Black-Box Policy Search with Probabilistic Programs}},
      year = {2016}
    }
    
  5. Tolpin, D., van de Meent, J.-W., Paige, B., & Wood, F. (2015). Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs. In A. Appice, P. P. Rodrigues, V. Santos Costa, J. Gama, A. Jorge, & C. Soares (Eds.), Machine Learning and Knowledge Discovery in Databases (Vol. 9285, pp. 311–326). Springer International Publishing. http://doi.org/10.1007/978-3-319-23525-7_19
    @incollection{tolpin_ecml_2015,
      year = {2015},
      isbn = {978-3-319-23524-0},
      booktitle = {Machine Learning and Knowledge Discovery in Databases},
      volume = {9285},
      series = {Lecture Notes in Computer Science},
      editor = {Appice, Annalisa and Rodrigues, Pedro Pereira and Santos Costa, Vítor and Gama, João and Jorge, Alípio and Soares, Carlos},
      doi = {10.1007/978-3-319-23525-7_19},
      title = {Output-Sensitive Adaptive Metropolis-Hastings for Probabilistic Programs},
      url = {http://dx.doi.org/10.1007/978-3-319-23525-7_19},
      publisher = {Springer International Publishing},
      keywords = {Probabilistic programming; Adaptive MCMC},
      author = {Tolpin, David and van de Meent, Jan-Willem and Paige, Brooks and Wood, Frank},
      pages = {311-326},
      language = {English}
    }
    
  6. van de Meent, J.-W., Yang, H., Mansinghka, V., & Wood, F. (2015). Particle Gibbs with Ancestor Sampling for Probabilistic Programs. In Artificial Intelligence and Statistics.

    Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference. A drawback of these techniques is that they rely on importance resampling, which results in degenerate particle trajectories and a low effective sample size for variables sampled early in a program. We here develop a formalism to adapt ancestor resampling, a technique that mitigates particle degeneracy, to the probabilistic programming setting. We present empirical results that demonstrate nontrivial performance gains.

    @inproceedings{vandemeent_aistats_2015,
      archiveprefix = {arXiv},
      arxivid = {1501.06769},
      author = {van de Meent, Jan-Willem and Yang, Hongseok and Mansinghka, Vikash and Wood, Frank},
      booktitle = {Artificial Intelligence and Statistics},
      eprint = {1501.06769},
      title = {{Particle Gibbs with Ancestor Sampling for Probabilistic Programs}},
      year = {2015}
    }
    
  7. Wood, F., van de Meent, J. W., & Mansinghka, V. (2014). A new approach to probabilistic programming inference. In Artificial Intelligence and Statistics (pp. 1024–1032).
    @inproceedings{wood_aistats_2014,
      author = {Wood, F and van de Meent, JW and Mansinghka, V},
      booktitle = {Artificial Intelligence and Statistics},
      pages = {1024--1032},
      title = {{A new approach to probabilistic programming inference}},
      year = {2014}
    }
    
  8. van de Meent, J.-W., Bronson, J. E., Wood, F., Gonzalez, R. L., & Wiggins, C. H. (2013). Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data. Proceedings of the 30th International Conference on Machine Learning, 28(2), 361–369.

    We address the problem of analyzing sets of noisy time-varying signals that all report on the same process but confound straightforward analyses due to complex inter-signal heterogeneities and measurement artifacts. In particular we consider single-molecule experiments which indirectly measure the distinct steps in a biomolecular process via observations of noisy time-dependent signals such as a fluorescence intensity or bead position. Straightforward hidden Markov model (HMM) analyses attempt to characterize such processes in terms of a set of conformational states, the transitions that can occur between these states, and the associated rates at which those transitions occur; but require ad-hoc post-processing steps to combine multiple signals. Here we develop a hierarchically coupled HMM that allows experimentalists to deal with inter-signal variability in a principled and automatic way. Our approach is a generalized expectation maximization hyperparameter point estimation procedure with variational Bayes at the level of individual time series that learns an single interpretable representation of the overall data generating process.

    @article{vandemeent_icml_2013,
      archiveprefix = {arXiv},
      arxivid = {1305.3640},
      author = {van de Meent, Jan-Willem and Bronson, Jonathan E and Wood, Frank and Gonzalez, Ruben L. and Wiggins, Chris H.},
      eprint = {1305.3640},
      journal = {Proceedings of the 30th International Conference on Machine Learning},
      month = may,
      number = {2},
      pages = {361--369},
      title = {{Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule data}},
      volume = {28},
      year = {2013}
    }
    

Journal

  1. Emmett, K. J., Rosenstein, J. K., van de Meent, J.-W., Shepard, K. L., & Wiggins, C. H. (2015). Statistical Inference for Nanopore Sequencing with a Biased Random Walk Model. Biophysical Journal, 108(April), 1852–1855. http://doi.org/doi:10.1016/j.bpj.2015.03.013

    Nanopore sequencing promises long read-lengths and single-molecule resolution, but the stochastic motion of the DNA molecule inside the pore is a current barrier to high accuracy reads. We develop a method of statistical inference that explicitly accounts for this error and demonstrate that high accuracy (>99.9%) sequence inference is feasible even under highly diffusive motion by using a hidden Markov model to jointly analyze multiple stochastic reads. Using this model, we place bounds on achievable inference accuracy under a range of experimental parameters.

    @article{emmett_bpj_2015,
      author = {Emmett, Kevin J. and Rosenstein, Jacob K. and van de Meent, Jan-Willem and Shepard, Ken L. and Wiggins, Chris H.},
      journal = {Biophysical Journal},
      volume = {108},
      issue = {April},
      pages = {1852-1855},
      doi = {doi:10.1016/j.bpj.2015.03.013},
      title = {{Statistical Inference for Nanopore Sequencing with a Biased Random Walk Model}},
      year = {2015}
    }
    
  2. Johnson, S., van de Meent, J.-W., Phillips, R., Wiggins, C. H., & Linden, M. (2014). Multiple LacI-mediated loops revealed by Bayesian statistics and tethered particle motion. Nucleic Acids Research, gku563–. http://doi.org/10.1093/nar/gku563

    The bacterial transcription factor LacI loops DNA by binding to two separate locations on the DNA simultaneously. Despite being one of the best-studied model systems for transcriptional regulation, the number and conformations of loop structures accessible to LacI remain unclear, though the importance of multiple coexisting loops has been implicated in interactions between LacI and other cellular regulators of gene expression. To probe this issue, we have developed a new analysis method for tethered particle motion, a versatile and commonly used in vitro single-molecule technique. Our method, vbTPM, performs variational Bayesian inference in hidden Markov models. It learns the number of distinct states (i.e. DNA-protein conformations) directly from tethered particle motion data with better resolution than existing methods, while easily correcting for common experimental artifacts. Studying short (roughly 100 bp) LacI-mediated loops, we provide evidence for three distinct loop structures, more than previously reported in single-molecule studies. Moreover, our results confirm that changes in LacI conformation and DNA-binding topology both contribute to the repertoire of LacI-mediated loops formed in vitro, and provide qualitatively new input for models of looping and transcriptional regulation. We expect vbTPM to be broadly useful for probing complex protein-nucleic acid interactions.

    @article{johnson_nar_2014,
      archiveprefix = {arXiv},
      arxivid = {1402.0894},
      author = {Johnson, S. and van de Meent, J.-W. and Phillips, R. and Wiggins, C. H. and Linden, M.},
      doi = {10.1093/nar/gku563},
      eprint = {1402.0894},
      issn = {0305-1048},
      journal = {Nucleic Acids Research},
      pages = {gku563--},
      title = {{Multiple LacI-mediated loops revealed by Bayesian statistics and tethered particle motion}},
      year = {2014}
    }
    
  3. Gansell, A. R., van de Meent, J. W., Zairis, S., & Wiggins, C. H. (2014). Stylistic clusters and the Syrian/South Syrian tradition of first-millennium BCE Levantine ivory carving: A machine learning approach. Journal of Archaeological Science, 44, 194–205. http://doi.org/10.1016/j.jas.2013.11.005

    Thousands of first-millennium BCE ivory carvings have been excavated from Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan Tash), hundreds of miles from their Levantine production contexts. At present, their specific manufacture dates and workshop localities are unknown. Relying on subjective, visual methods, scholars have grappled with their classification and regional attribution for over a century. This study combines visual approaches with machine learning techniques to offer data-driven perspectives on the classification and attribution of this Iron Age corpus.The study sample consists of 162 sculptures of female figures that have been conventionally attributed to three main regional carving traditions: "Phoenician," "North Syrian," and "Syrian/South Syrian". We have developed an algorithm that clusters the ivories based on a combination of descriptive and anthropometric data. The resulting categories, which are based on purely statistical criteria, show good agreement with conventional art historical classifications, while revealing new insights, especially with regard to the "Syrian/South Syrian" tradition.Specifically, we have determined that objects of the Syrian/South Syrian tradition might be more closely related to Phoenician objects than to North Syrian objects. We also reconsider the classification of a subset of "Phoenician" objects, and we confirm Syrian/South Syrian stylistic subgroups, the geographic distribution of which might illuminate Neo-Assyrian acquisition networks. Additionally, we have identified the features in our cluster assignments that might be diagnostic of regional traditions. In short, our study both corroborates traditional visual classifications and demonstrates how machine learning techniques may be employed to retrieve complementary information not accessible through an exclusively visual analysis. ?? 2013 Elsevier Ltd.

    @article{gansell_jas_2014,
      archiveprefix = {arXiv},
      arxivid = {1401.0871},
      author = {Gansell, Amy Rebecca and van de Meent, Jan Willem and Zairis, Sakellarios and Wiggins, Chris H.},
      doi = {10.1016/j.jas.2013.11.005},
      eprint = {1401.0871},
      issn = {10959238},
      journal = {Journal of Archaeological Science},
      keywords = {Attribution,Clustering,Iron Age,Ivory sculpture,Levant,Machine learning,Mutual information},
      month = apr,
      pages = {194--205},
      publisher = {Elsevier Ltd},
      title = {{Stylistic clusters and the Syrian/South Syrian tradition of first-millennium BCE Levantine ivory carving: A machine learning approach}},
      volume = {44},
      year = {2014}
    }
    
  4. van de Meent, J.-W., Bronson, J. E., Wiggins, C. H., & Gonzalez, R. L. (2014). Empirical Bayes methods enable advanced population-level analyses of single-molecule FRET experiments. Biophysical Journal, 106(6), 1327–37. http://doi.org/10.1016/j.bpj.2013.12.055

    Many single-molecule experiments aim to characterize biomolecular processes in terms of kinetic models that specify the rates of transition between conformational states of the biomolecule. Estimation of these rates often requires analysis of a population of molecules, in which the conformational trajectory of each molecule is represented by a noisy, time-dependent signal trajectory. Although hidden Markov models (HMMs) may be used to infer the conformational trajectories of individual molecules, estimating a consensus kinetic model from the population of inferred conformational trajectories remains a statistically difficult task, as inferred parameters vary widely within a population. Here, we demonstrate how a recently developed empirical Bayesian method for HMMs can be extended to enable a more automated and statistically principled approach to two widely occurring tasks in the analysis of single-molecule fluorescence resonance energy transfer (smFRET) experiments: 1), the characterization of changes in rates across a series of experiments performed under variable conditions; and 2), the detection of degenerate states that exhibit the same FRET efficiency but differ in their rates of transition. We apply this newly developed methodology to two studies of the bacterial ribosome, each exemplary of one of these two analysis tasks. We conclude with a discussion of model-selection techniques for determination of the appropriate number of conformational states. The code used to perform this analysis and a basic graphical user interface front end are available as open source software.

    @article{vandemeent_bpj_2014,
      author = {van de Meent, Jan-Willem and Bronson, Jonathan E and Wiggins, Chris H and Gonzalez, Ruben L},
      doi = {10.1016/j.bpj.2013.12.055},
      issn = {1542-0086},
      journal = {Biophysical journal},
      month = mar,
      number = {6},
      pages = {1327--37},
      pmid = {24655508},
      title = {{Empirical Bayes methods enable advanced population-level analyses of single-molecule FRET experiments.}},
      volume = {106},
      year = {2014}
    }
    
  5. van de Meent, J.-W., Sederman, A. J., Gladden, L. F., & Goldstein, R. E. (2010). Measurement of cytoplasmic streaming in single plant cells by magnetic resonance velocimetry. Journal of Fluid Mechanics, 642, 5–14. http://doi.org/10.1017/S0022112009992187
    @article{vandemeent_jfm_2010,
      author = {van de Meent, Jan-Willem and Sederman, Andy J. and Gladden, Lynn F. and Goldstein, Raymond E.},
      doi = {10.1017/S0022112009992187},
      issn = {0022-1120},
      journal = {Journal of Fluid Mechanics},
      pages = {5--14},
      title = {{Measurement of cytoplasmic streaming in single plant cells by magnetic resonance velocimetry}},
      volume = {642},
      year = {2010}
    }
    
  6. Sultan, E., van de Meent, J.-W., Somfai, E., Morozov, A. N., & van Saarloos, W. (2010). Polymer rheology simulations at the meso- and macroscopic scale. Europhysics Letters, 90(6), 64002. http://doi.org/10.1209/0295-5075/90/64002
    @article{sultan_epl_2010,
      author = {Sultan, Eric and van de Meent, Jan-Willem and Somfai, Ellak and Morozov, Alexander N. and van Saarloos, Wim},
      doi = {10.1209/0295-5075/90/64002},
      issn = {0295-5075},
      journal = {Europhysics Letters},
      month = jun,
      number = {6},
      pages = {64002},
      title = {{Polymer rheology simulations at the meso- and macroscopic scale}},
      volume = {90},
      year = {2010}
    }
    
  7. van de Meent, J.-W., Tuval, I., & Goldstein, R. (2008). Nature’s Microfluidic Transporter: Rotational Cytoplasmic Streaming at High Péclet Numbers. Physical Review Letters, 101(17), 178102. http://doi.org/10.1103/PhysRevLett.101.178102
    @article{vandemeent_prl_2008,
      author = {van de Meent, Jan-Willem and Tuval, Idan and Goldstein, Raymond},
      doi = {10.1103/PhysRevLett.101.178102},
      issn = {0031-9007},
      journal = {Physical Review Letters},
      month = oct,
      number = {17},
      pages = {178102},
      title = {{Nature’s Microfluidic Transporter: Rotational Cytoplasmic Streaming at High P\'{e}clet Numbers}},
      volume = {101},
      year = {2008}
    }
    
  8. Goldstein, R. E., Tuval, I., & van de Meent, J.-W. (2008). Microfluidics of cytoplasmic streaming and its implications for intracellular transport. Proceedings of the National Academy of Sciences of the United States of America, 105(10), 3663–7. http://doi.org/10.1073/pnas.0707223105

    Found in many large eukaryotic cells, particularly in plants, cytoplasmic streaming is the circulation of their contents driven by fluid entrainment from particles carried by molecular motors at the cell periphery. In the more than two centuries since its discovery, streaming has frequently been conjectured to aid in transport and mixing of molecular species in the cytoplasm and, by implication, in cellular homeostasis, yet no theoretical analysis has been presented to quantify these processes. We show by a solution to the coupled dynamics of fluid flow and diffusion appropriate to the archetypal "rotational streaming" of algal species such as Chara and Nitella that internal mixing and the transient dynamical response to changing external conditions can indeed be enhanced by streaming, but to an extent that depends strongly on the pitch of the helical flow. The possibility that this may have a developmental consequence is illustrated by the coincidence of the exponential growth phase of Nitella and the point of maximum enhancement of those processes.

    @article{goldstein_pnas_2008,
      author = {Goldstein, Raymond E and Tuval, Idan and van de Meent, Jan-Willem},
      doi = {10.1073/pnas.0707223105},
      issn = {1091-6490},
      journal = {Proceedings of the National Academy of Sciences of the United States of America},
      month = mar,
      number = {10},
      pages = {3663--7},
      pmid = {18310326},
      title = {{Microfluidics of cytoplasmic streaming and its implications for intracellular transport.}},
      volume = {105},
      year = {2008}
    }
    
  9. van de Meent, J.-W., Morozov, A., Somfai, E., Sultan, E., & van Saarloos, W. (2008). Coherent structures in dissipative particle dynamics simulations of the transition to turbulence in compressible shear flows. Physical Review E, 78(1), 015701. http://doi.org/10.1103/PhysRevE.78.015701
    @article{vandemeent_pre_2008,
      author = {van de Meent, Jan-Willem and Morozov, Alexander and Somfai, Ell\'{a}k and Sultan, Eric and van Saarloos, Wim},
      doi = {10.1103/PhysRevE.78.015701},
      issn = {1539-3755},
      journal = {Physical Review E},
      month = jul,
      number = {1},
      pages = {015701},
      title = {{Coherent structures in dissipative particle dynamics simulations of the transition to turbulence in compressible shear flows}},
      volume = {78},
      year = {2008}
    }
    
  10. Fenistein, D., van de Meent, J.-W., & van Hecke, M. (2006). Core Precession and Global Modes in Granular Bulk Flow. Physical Review Letters, 96(11), 118001. http://doi.org/10.1103/PhysRevLett.96.118001
    @article{fenistein_prl_2006,
      author = {Fenistein, Denis and van de Meent, Jan-Willem and van Hecke, Martin},
      doi = {10.1103/PhysRevLett.96.118001},
      issn = {0031-9007},
      journal = {Physical Review Letters},
      month = mar,
      number = {11},
      pages = {118001},
      title = {{Core Precession and Global Modes in Granular Bulk Flow}},
      volume = {96},
      year = {2006}
    }
    
  11. Fenistein, D., van de Meent, J., & van Hecke, M. (2004). Universal and Wide Shear Zones in Granular Bulk Flow. Physical Review Letters, 92(9), 094301. http://doi.org/10.1103/PhysRevLett.92.094301
    @article{fenistein_prl_2004,
      author = {Fenistein, Denis and van de Meent, Jan and van Hecke, Martin},
      doi = {10.1103/PhysRevLett.92.094301},
      issn = {0031-9007},
      journal = {Physical Review Letters},
      month = mar,
      number = {9},
      pages = {094301},
      title = {{Universal and Wide Shear Zones in Granular Bulk Flow}},
      volume = {92},
      year = {2004}
    }
    

Reports

  1. van de Meent, J.-W., Paige, B., & Wood, F. (2014). Tempering by Subsampling. ArXiv e-Prints, 1401.7145.

    In this paper we demonstrate that tempering Markov chain Monte Carlo samplers for Bayesian models by recursively subsampling observations without replacement can improve the performance of baseline samplers in terms of effective sample size per computation. We present two tempering by subsampling algorithms, subsampled parallel tempering and subsampled tempered transitions. We provide an asymptotic analysis of the computational cost of tempering by subsampling, verify that tempering by subsampling costs less than traditional tempering, and demonstrate both algorithms on Bayesian approaches to learning the mean of a high dimensional multivariate Normal and estimating Gaussian process hyperparameters.

    @article{vandemeent_arxiv_2014,
      author = {van de Meent, Jan-Willem and Paige, Brooks and Wood, Frank},
      journal = {ArXiv e-prints},
      archiveprefix = {arXiv},
      eprint = {1401.7145},
      page = {1401.7145},
      title = {{Tempering by Subsampling}},
      year = {2014}
    }
    

Workshop

  1. Maha Alkhairy, J.-W. van de M., Byron Wallace. (2017). Learning Disentangled Representations for Text. Working Paper.
    @article{alkhairy_icmlw_2017,
      title = {Learning Disentangled Representations for Text},
      author = {Maha Alkhairy, Byron Wallace, Jan-Willem van de Meent},
      journal = {Working paper},
      year = {2017}
    }
    
  2. Janz, D., Paige, B., Rainforth, T., van de Meent, J.-W., & Wood, F. (2016). Probabilistic structure discovery in time series data. NIPS 2016 Workshop on Artificial Intelligence for Data Science.
    @article{janz_nipsw_2016,
      title = {Probabilistic structure discovery in time series data},
      author = {Janz, David and Paige, Brooks and Rainforth, Tom and van de Meent, Jan-Willem and Wood, Frank},
      journal = {NIPS 2016 workshop on Artificial Intelligence for Data Science},
      year = {2016}
    }
    
  3. van de Meent, J.-W., Paige, B., Tolpin, D., & Wood, F. (2016). An Interface for Black Box Learning in Probabilistic Programs. In POPL Workshop on Probabilistic Programming Semantics.
    @inproceedings{vandemeent_poplw_2016,
      author = {van de Meent, Jan-Willem and Paige, Brooks and Tolpin, David and Wood, Frank},
      booktitle = {POPL Workshop on Probabilistic Programming Semantics},
      title = {{An Interface for Black Box Learning in Probabilistic Programs}},
      year = {2016}
    }
    
  4. Rainforth, T., van de Meent, J.-W., & Wood, F. (2015). Bayesian Optimization for Probabilistic Programs (2015), NIPS workshop on Black Box Learning and Inference. In NIPS Workshop on Black Box Learning and Inference.
    @inproceedings{rainforth_nipsw_2015,
      author = {Rainforth, Tom and van de Meent, Jan-Willem and Wood, Frank},
      booktitle = {NIPS Workshop on Black Box Learning and Inference},
      title = {{Bayesian Optimization for Probabilistic Programs (2015), NIPS workshop on Black Box Learning and Inference}},
      year = {2015}
    }
    
  5. Tolpin, D., Paige, B., van de Meent, J.-W., & Wood, F. (2015). Path Finding under Uncertainty through Probabilistic Inference. In Proceedings of the 25th International Conference on Automated Planning and Scheduling, Workshop on Planning and Learning (ICAPS WPAL) (p. 1502.07314).

    We introduce a new approach to solving path-finding problems under uncertainty by representing them as probabilistic models and applying domain-independent inference algorithms to the models. This approach separates problem representation from the inference algorithm and provides a framework for efficient learning of path-finding policies. We evaluate the new approach on the Canadian Traveler Problem, which we formulate as a probabilistic model, and show how probabilistic inference allows high performance stochastic policies to be obtained for this problem.

    @inproceedings{tolpin_icapsw_2015,
      author = {Tolpin, David and Paige, Brooks and van de Meent, Jan-Willem and Wood, Frank},
      booktitle = {Proceedings of the 25th International Conference on Automated Planning and Scheduling, Workshop on Planning and Learning (ICAPS WPAL)},
      archiveprefix = {arXiv},
      arxivid = {1502.07314},
      eprint = {1502.07314},
      pages = {1502.07314},
      title = {{Path Finding under Uncertainty through Probabilistic Inference}},
      year = {2015}
    }
    
  6. van de Meent, J.-W., Yang, H., & Wood, F. (2014). Particle Gibbs with Ancestor Resampling for Probabilistic Programs. In 3rd NIPS Workshop on Probabilistic Programming.
    @inproceedings{vandemeent_nipsw_2014,
      author = {van de Meent, Jan-Willem and Yang, Hongseok and Wood, Frank},
      booktitle = {3rd {NIPS} Workshop on Probabilistic Programming},
      title = {{Particle Gibbs with Ancestor Resampling for Probabilistic Programs}},
      year = {2014}
    }