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Villani, Mattias
Alternative names
Publications (10 of 15) Show all publications
Magnusson, M., Jonsson, L. & Villani, M. (2019). DOLDA: a regularized supervised topic model for high-dimensional multi-class regression. Computational statistics (Zeitschrift)
Open this publication in new window or tab >>DOLDA: a regularized supervised topic model for high-dimensional multi-class regression
2019 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658Article in journal (Refereed) Epub ahead of print
Abstract [en]

Generating user interpretable multi-class predictions in data-rich environments with many classes and explanatory covariates is a daunting task. We introduce Diagonal Orthant Latent Dirichlet Allocation (DOLDA), a supervised topic model for multi-class classification that can handle many classes as well as many covariates. To handle many classes we use the recently proposed Diagonal Orthant probit model (Johndrow et al., in: Proceedings of the sixteenth international conference on artificial intelligence and statistics, 2013) together with an efficient Horseshoe prior for variable selection/shrinkage (Carvalho et al. in Biometrika 97:465–480, 2010). We propose a computationally efficient parallel Gibbs sampler for the new model. An important advantage of DOLDA is that learned topics are directly connected to individual classes without the need for a reference class. We evaluate the model’s predictive accuracy and scalability, and demonstrate DOLDA’s advantage in interpreting the generated predictions.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Text classification, Latent Dirichlet Allocation, Horseshoe prior, Diagonal Orthant probit model, Interpretable models
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-159217 (URN)10.1007/s00180-019-00891-1 (DOI)2-s2.0-85067414496 (Scopus ID)
Available from: 2019-08-05 Created: 2019-08-05 Last updated: 2019-08-14Bibliographically approved
Andersson, O., Sidén, P., Dahlin, J., Doherty, P. & Villani, M. (2019). Real-Time Robotic Search using Structural Spatial Point Processes. In: : . Paper presented at Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22-25, 2019.
Open this publication in new window or tab >>Real-Time Robotic Search using Structural Spatial Point Processes
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2019 (English)Conference paper, Published paper (Refereed)
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-159698 (URN)
Conference
Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), Tel Aviv, Israel, July 22-25, 2019
Available from: 2019-08-19 Created: 2019-08-19 Last updated: 2019-08-27Bibliographically approved
Magnusson, M., Jonsson, L., Villani, M. & Broman, D. (2018). Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models. Journal of Computational And Graphical Statistics, 27(2), 449-463
Open this publication in new window or tab >>Sparse Partially Collapsed MCMC for Parallel Inference in Topic Models
2018 (English)In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715, Vol. 27, no 2, p. 449-463Article in journal (Refereed) Published
Abstract [en]

Topic models, and more specifically the class of Latent Dirichlet Allocation (LDA), are widely used for probabilistic modeling of text. MCMC sampling from the posterior distribution is typically performed using a collapsed Gibbs sampler. We propose a parallel sparse partially collapsed Gibbs sampler and compare its speed and efficiency to state-of-the-art samplers for topic models on five well-known text corpora of differing sizes and properties. In particular, we propose and compare two different strategies for sampling the parameter block with latent topic indicators. The experiments show that the increase in statistical inefficiency from only partial collapsing is smaller than commonly assumed, and can be more than compensated by the speedup from parallelization and sparsity on larger corpora. We also prove that the partially collapsed samplers scale well with the size of the corpus. The proposed algorithm is fast, efficient, exact, and can be used in more modeling situations than the ordinary collapsed sampler.

Place, publisher, year, edition, pages
Taylor & Francis, 2018
Keywords
Bayesian inference, Gibbs sampling, Latent Dirichlet Allocation, Massive Data Sets, Parallel Computing, Computational complexity
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-140872 (URN)10.1080/10618600.2017.1366913 (DOI)000435688200018 ()
Funder
Swedish Foundation for Strategic Research , SSFRIT 15-0097
Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-07-20Bibliographically approved
Quiroz, M., Tran, M.-N., Villani, M. & Kohn, R. (2018). Speeding up MCMC by Delayed Acceptance and Data Subsampling. Journal of Computational And Graphical Statistics, 27(1), 12-22
Open this publication in new window or tab >>Speeding up MCMC by Delayed Acceptance and Data Subsampling
2018 (English)In: Journal of Computational And Graphical Statistics, ISSN 1061-8600, E-ISSN 1537-2715, Vol. 27, no 1, p. 12-22Article in journal (Refereed) Published
Abstract [en]

The complexity of the Metropolis–Hastings (MH) algorithm arises from the requirement of a likelihood evaluation for the full dataset in each iteration. One solution has been proposed to speed up the algorithm by a delayed acceptance approach where the acceptance decision proceeds in two stages. In the first stage, an estimate of the likelihood based on a random subsample determines if it is likely that the draw will be accepted and, if so, the second stage uses the full data likelihood to decide upon final acceptance. Evaluating the full data likelihood is thus avoided for draws that are unlikely to be accepted. We propose a more precise likelihood estimator that incorporates auxiliary information about the full data likelihood while only operating on a sparse set of the data. We prove that the resulting delayed acceptance MH is more efficient. The caveat of this approach is that the full dataset needs to be evaluated in the second stage. We therefore propose to substitute this evaluation by an estimate and construct a state-dependent approximation thereof to use in the first stage. This results in an algorithm that (i) can use a smaller subsample m by leveraging on recent advances in Pseudo-Marginal MH (PMMH) and (ii) is provably within O(m^-2) of the true posterior.

Place, publisher, year, edition, pages
Taylor & Francis Group, 2018
Keywords
Bayesian inference, Delayed acceptance MCMC, Large data, Markov chain Monte Carlo, Survey sampling
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:liu:diva-140873 (URN)10.1080/10618600.2017.1307117 (DOI)000430484300002 ()2-s2.0-85026419157 (Scopus ID)
Funder
Swedish Foundation for Strategic Research , RIT 15-0097
Note

Funding agencies: VINNOVA grant [2010-02635]; Business School Pilot Research grant; Swedish Foundation for Strategic Research [RIT 15-0097]; Australian Research Council Centre of Excellence grant [CE140100049]

Available from: 2017-09-13 Created: 2017-09-13 Last updated: 2018-05-23Bibliographically approved
Gu, X., Sidén, P., Wegmann, B., Eklund, A., Villani, M. & Knutsson, H. (2017). Bayesian Diffusion Tensor Estimation with Spatial Priors. In: CAIP 2017: Computer Analysis of Images and Patterns. Paper presented at International Conference on Computer Analysis of Images and Patterns (pp. 372-383). , 10424
Open this publication in new window or tab >>Bayesian Diffusion Tensor Estimation with Spatial Priors
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2017 (English)In: CAIP 2017: Computer Analysis of Images and Patterns, 2017, Vol. 10424, p. 372-383Conference paper, Published paper (Refereed)
Abstract [en]

Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.

Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10424
Keywords
Spatial regularization, Diffusion tensor, Spatial priors Markov chain, Monte Carlo Fractional anisotropy
National Category
Medical Engineering
Identifiers
urn:nbn:se:liu:diva-139844 (URN)10.1007/978-3-319-64689-3_30 (DOI)000432085900030 ()978-3-319-64689-3 (ISBN)978-3-319-64688-6 (ISBN)
Conference
International Conference on Computer Analysis of Images and Patterns
Note

Funding agencies: Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy); Swedish Research Council [2015-05356, 2013-5229]; National Institute of Dental and Craniof

Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2018-06-01
Maghazeh, A., Bordoloi, U. D., Villani, M., Eles, P. & Peng, Z. (2015). Perception-aware power management for mobile games via dynamic resolution scaling. In: 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD): . Paper presented at Computer-Aided Design (ICCAD), 2015 IEEE/ACM International Conference on 2-6 Nov. 2015 Austin, TX (pp. 613-620). IEEE
Open this publication in new window or tab >>Perception-aware power management for mobile games via dynamic resolution scaling
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2015 (English)In: 2015 IEEE/ACM INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN (ICCAD), IEEE , 2015, p. 613-620Conference paper, Published paper (Refereed)
Abstract [en]

Modern mobile devices provide ultra-high resolutions in their display panels. This imposes ever increasing workload on the GPU leading to high power consumption and shortened battery life. In this paper, we first show that resolution scaling leads to significant power savings. Second, we propose a perception-aware adaptive scheme that sets the resolution during game play. We exploit the fact that game players are often willing to trade quality for longer battery life. Our scheme uses decision theory, where the predicted user perception is combined with a novel asymmetric loss function that encodes users' alterations in their willingness to save power.

Place, publisher, year, edition, pages
IEEE, 2015
Series
ICCAD-IEEE ACM International Conference on Computer-Aided Design, ISSN 1933-7760
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-124543 (URN)10.1109/ICCAD.2015.7372626 (DOI)000368929600084 ()978-1-4673-8388-2 (ISBN)
Conference
Computer-Aided Design (ICCAD), 2015 IEEE/ACM International Conference on 2-6 Nov. 2015 Austin, TX
Available from: 2016-02-02 Created: 2016-02-02 Last updated: 2018-12-07
Dahlin, J., Schön, T. B. & Villani, M. (2014). Approximate inference in state space models with intractable likelihoods using Gaussian process optimisation.
Open this publication in new window or tab >>Approximate inference in state space models with intractable likelihoods using Gaussian process optimisation
2014 (English)Report (Other academic)
Abstract [en]

We propose a novel method for MAP parameter inference in nonlinear state space models with intractable likelihoods. The method is based on a combination of Gaussian process optimisation (GPO), sequential Monte Carlo (SMC) and approximate Bayesian computations (ABC). SMC and ABC are used to approximate the intractable likelihood by using the similarity between simulated realisations from the model and the data obtained from the system. The GPO algorithm is used for the MAP parameter estimation given noisy estimates of the log-likelihood. The proposed parameter inference method is evaluated in three problems using both synthetic and real-world data. The results are promising, indicating that the proposed algorithm converges fast and with reasonable accuracy compared with existing methods.

Publisher
p. 25
Series
LiTH-ISY-R, ISSN 1400-3902 ; 3075
Keywords
Approximate Bayesian computations, Gaussian process optimisation, Bayesian parameter inference, alpha-stable distribution
National Category
Probability Theory and Statistics Control Engineering Signal Processing
Identifiers
urn:nbn:se:liu:diva-106198 (URN)LiTH-ISY-R-3075 (ISRN)
Funder
Swedish Research Council, 621-2013-5524
Available from: 2014-04-28 Created: 2014-04-28 Last updated: 2016-05-04Bibliographically approved
Eklund, A., Dufort, P., Villani, M. & LaConte, S. (2014). BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs. Frontiers in Neuroinformatics, 8(24)
Open this publication in new window or tab >>BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs
2014 (English)In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 8, no 24Article in journal (Refereed) Published
Abstract [en]

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful graphics processing units (GPUs) to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL (Open Computing Language) that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further, dramatic speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github (https://github.com/wanderine/BROCCOLI/).

Place, publisher, year, edition, pages
Progressive Frontiers Press, 2014
Keywords
Neuroimaging, fMRI, Spatial normalization, GPU, CUDA, OpenCL, Image registration, Permutation test
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-105298 (URN)10.3389/fninf.2014.00024 (DOI)000348106800002 ()
Available from: 2014-03-17 Created: 2014-03-17 Last updated: 2017-12-05Bibliographically approved
Ukhov, I., Villani, M., Eles, P. & Peng, Z. (2014). Statistical Analysis of Process Variation Based on Indirect Measurements for Electronic System Design. In: 2014 19TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC): . Paper presented at 19th Asia and South Pacific Design Automation Conference (ASP-DAC 2014), SunTec, Singapore, January 20-23, 2014 (pp. 436-442). New York: IEEE conference proceedings
Open this publication in new window or tab >>Statistical Analysis of Process Variation Based on Indirect Measurements for Electronic System Design
2014 (English)In: 2014 19TH ASIA AND SOUTH PACIFIC DESIGN AUTOMATION CONFERENCE (ASP-DAC), New York: IEEE conference proceedings, 2014, p. 436-442Conference paper, Published paper (Refereed)
Abstract [en]

We present a framework for the analysis of process variation across semiconductor wafers. The framework is capable of quantifying the primary parameters affected by process variation, e.g., the effective channel length, which is in contrast with the former techniques wherein only secondary parameters were considered, e.g., the leakage current. Instead of taking direct measurements of the quantity of interest, we employ Bayesian inference to draw conclusions based on indirect observations, e.g., on temperature. The proposed approach has low costs since no deployment of expensive test structures might be needed or only a small subset of the test equipments already deployed for other purposes might need to be activated. The experimental results present an assessment of our framework for a wide range of configurations.

Place, publisher, year, edition, pages
New York: IEEE conference proceedings, 2014
Series
Asia and South Pacific Design Automation Conference Proceedings, ISSN 2153-6961
National Category
Computer Sciences
Identifiers
urn:nbn:se:liu:diva-106737 (URN)10.1109/ASPDAC.2014.6742930 (DOI)000350791700081 ()2-s2.0-84897898869 (Scopus ID)978-1-4799-2816-3 (ISBN)
Conference
19th Asia and South Pacific Design Automation Conference (ASP-DAC 2014), SunTec, Singapore, January 20-23, 2014
Available from: 2014-05-20 Created: 2014-05-20 Last updated: 2018-01-11Bibliographically approved
Giordani, P., Jacobson, T., von Schedvin, E. & Villani, M. (2014). Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios. Journal of financial and quantitative analysis, 49(4), 1071-1099
Open this publication in new window or tab >>Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios
2014 (English)In: Journal of financial and quantitative analysis, ISSN 0022-1090, E-ISSN 1756-6916, Vol. 49, no 4, p. 1071-1099Article in journal (Refereed) Published
Abstract [en]

We demonstrate improvements in predictive power when introducing spline functions to take account of highly nonlinear relationships between firm failure and leverage, earnings, and liquidity in a logistic bankruptcy model. Our results show that modeling excessive nonlinearities yields substantially improved bankruptcy predictions, on the order of 70%-90%, compared with a standard logistic model. The spline model provides several important and surprising insights into nonmonotonic bankruptcy relationships. We find that low-leveraged as well as highly profitable firms are riskier than those given by a standard model, possibly a manifestation of credit rationing and excess cash-flow volatility.

Place, publisher, year, edition, pages
Cambridge University Press (CUP): HSS Journals, 2014
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-114453 (URN)10.1017/S0022109014000623 (DOI)000348372700009 ()
Available from: 2015-02-20 Created: 2015-02-20 Last updated: 2017-12-04
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