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ORCIDGuido Sanguinetti
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- affiliation: SISSA: Trieste, Italy
- affiliation: University of Edinburgh, UK
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2020 – today
- 2023
[c37]Riccardo Giuseppe Margiotta, Sebastian Goldt, Guido Sanguinetti:
Attacks on Online Learners: a Teacher-Student Analysis. NeurIPS 2023- [c36]Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti:
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity. UAI 2023: 88-98 - [i14]Riccardo Giuseppe Margiotta, Sebastian Goldt, Guido Sanguinetti:
Attacks on Online Learners: a Teacher-Student Analysis. CoRR abs/2305.11132 (2023) - [i13]Viplove Arora, Daniele Irto, Sebastian Goldt, Guido Sanguinetti:
Quantifying lottery tickets under label noise: accuracy, calibration, and complexity. CoRR abs/2306.12190 (2023) - 2022
- [j42]Christos Maniatis
, Catalina A. Vallejos, Guido Sanguinetti:
SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data. PLoS Comput. Biol. 18(6) (2022) - [c35]Svitlana Braichenko, Ramon Grima, Guido Sanguinetti:
Bayesian Learning of Effective Chemical Master Equations in Crowded Intracellular Conditions. CMSB 2022: 239-258 - [c34]Ginevra Carbone, Luca Bortolussi, Guido Sanguinetti:
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks. IJCNN 2022: 1-8 - [i12]Luca Bortolussi, Ginevra Carbone, Luca Laurenti, Andrea Patane, Guido Sanguinetti, Matthew Wicker:
On the Robustness of Bayesian Neural Networks to Adversarial Attacks. CoRR abs/2207.06154 (2022) - 2021
- [c33]Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi:
Random Projections for Improved Adversarial Robustness. IJCNN 2021: 1-7 - [i11]Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi:
Random Projections for Improved Adversarial Robustness. CoRR abs/2102.09230 (2021) - [i10]Ginevra Carbone, Guido Sanguinetti, Luca Bortolussi:
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks. CoRR abs/2102.11010 (2021) - 2020
- [j41]Giulio Caravagna, Guido Sanguinetti, Trevor A. Graham, Andrea Sottoriva:
The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data. BMC Bioinform. 21(1): 531 (2020) - [c32]Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patané, Luca Bortolussi, Guido Sanguinetti:
Robustness of Bayesian Neural Networks to Gradient-Based Attacks. NeurIPS 2020 - [e4]Guido Sanguinetti, David Safránek:
Proceedings of SASB 2018, the 7th International Workshop on Static Analysis and Systems Biology, University of Edinburgh, September 7, 2016. Electronic Notes in Theoretical Computer Science 335, Elsevier 2020 [contents] - [i9]Ginevra Carbone, Matthew Wicker, Luca Laurenti, Andrea Patane, Luca Bortolussi, Guido Sanguinetti:
Robustness of Bayesian Neural Networks to Gradient-Based Attacks. CoRR abs/2002.04359 (2020)
2010 – 2019
- 2019
- [j40]Michael Everett Rule, David Schnoerr, Matthias H. Hennig, Guido Sanguinetti:
Neural field models for latent state inference: Application to large-scale neuronal recordings. PLoS Comput. Biol. 15(11) (2019) - [j39]Michalis Michaelides, Jane Hillston, Guido Sanguinetti:
Statistical Abstraction for Multi-scale Spatio-temporal Systems. ACM Trans. Model. Comput. Simul. 29(4): 22:1-22:29 (2019) - [c31]Ankit Gupta, Mustafa Khammash, Guido Sanguinetti:
Bayesian Parameter Estimation for Stochastic Reaction Networks from Steady-State Observations. CMSB 2019: 342-346 - [c30]Kaan Öcal, Ramon Grima, Guido Sanguinetti:
Wasserstein Distances for Estimating Parameters in Stochastic Reaction Networks. CMSB 2019: 347-351 - [e3]Luca Bortolussi, Guido Sanguinetti:
Computational Methods in Systems Biology - 17th International Conference, CMSB 2019, Trieste, Italy, September 18-20, 2019, Proceedings. Lecture Notes in Computer Science 11773, Springer 2019, ISBN 978-3-030-31303-6 [contents] - [i8]Michalis Michaelides, Jane Hillston, Guido Sanguinetti:
Geometric fluid approximation for general continuous-time Markov chains. CoRR abs/1901.11417 (2019) - 2018
- [j38]Chantriolnt-Andreas Kapourani, Guido Sanguinetti:
BPRMeth: a flexible Bioconductor package for modelling methylation profiles. Bioinform. 34(14): 2485-2486 (2018) - [j37]Michael Everett Rule, Guido Sanguinetti:
Autoregressive Point Processes as Latent State-Space Models: A Moment-Closure Approach to Fluctuations and Autocorrelations. Neural Comput. 30(10) (2018) - [j36]Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti:
ProPPA: Probabilistic Programming for Stochastic Dynamical Systems. ACM Trans. Model. Comput. Simul. 28(1): 3:1-3:23 (2018) - [c29]Dimitrios Milios, Guido Sanguinetti, David Schnoerr:
Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference. QEST 2018: 289-305 - [c28]Luca Bortolussi, Guido Sanguinetti, Simone Silvetti:
Bayesian Statistical parametric Verification and synthesis by Machine Learning. WSC 2018: 381-394 - [c27]Guido Sanguinetti, David Safránek:
Preface. SASB 2018: 1-2 - [i7]Luca Bortolussi, Guido Sanguinetti:
Intrinsic Geometric Vulnerability of High-Dimensional Artificial Intelligence. CoRR abs/1811.03571 (2018) - 2017
- [j35]Ezio Bartocci, Luca Bortolussi, Tomás Brázdil, Dimitrios Milios, Guido Sanguinetti:
Policy learning in continuous-time Markov decision processes using Gaussian Processes. Perform. Evaluation 116: 84-100 (2017) - [j34]Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti:
Unbiased Bayesian inference for population Markov jump processes via random truncations. Stat. Comput. 27(4): 991-1002 (2017) - [c26]Michalis Michaelides, Jane Hillston, Guido Sanguinetti:
Statistical Abstraction for Multi-scale Spatio-Temporal Systems. QEST 2017: 243-258 - [i6]Giulio Caravagna, Daniele Ramazzotti, Guido Sanguinetti:
On learning the structure of Bayesian Networks and submodular function maximization. CoRR abs/1706.02386 (2017) - [i5]Dimitrios Milios, Guido Sanguinetti, David Schnoerr:
Probabilistic Model Checking for Continuous Time Markov Chains via Sequential Bayesian Inference. CoRR abs/1711.01863 (2017) - 2016
- [j33]Chantriolnt-Andreas Kapourani, Guido Sanguinetti:
Higher order methylation features for clustering and prediction in epigenomic studies. Bioinform. 32(17): 405-412 (2016) - [j32]Yuanhua Huang, Guido Sanguinetti:
Statistical modeling of isoform splicing dynamics from RNA-seq time series data. Bioinform. 32(19): 2965-2972 (2016) - [j31]Saulius Lukauskas, Roberto Visintainer, Guido Sanguinetti, Gabriele Beate Schweikert:
DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks. BMC Bioinform. 17(S-16): 53-63 (2016) - [j30]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
Smoothed model checking for uncertain Continuous-Time Markov Chains. Inf. Comput. 247: 235-253 (2016) - [c25]Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti:
Matching Models Across Abstraction Levels with Gaussian Processes. CMSB 2016: 49-66 - [c24]Michalis Michaelides, Dimitrios Milios, Jane Hillston, Guido Sanguinetti:
Property-Driven State-Space Coarsening for Continuous Time Markov Chains. QEST 2016: 3-18 - [c23]Ezio Bartocci, Luca Bortolussi, Tomás Brázdil, Dimitrios Milios, Guido Sanguinetti:
Policy Learning for Time-Bounded Reachability in Continuous-Time Markov Decision Processes via Doubly-Stochastic Gradient Ascent. QEST 2016: 244-259 - [i4]Ezio Bartocci, Luca Bortolussi, Tomás Brázdil, Dimitrios Milios, Guido Sanguinetti:
Policy learning for time-bounded reachability in Continuous-Time Markov Decision Processes via doubly-stochastic gradient ascent. CoRR abs/1605.09703 (2016) - [i3]Michalis Michaelides, Dimitrios Milios, Jane Hillston, Guido Sanguinetti:
Property-driven State-Space Coarsening for Continuous Time Markov Chains. CoRR abs/1606.01111 (2016) - 2015
- [j29]Tom R. Mayo, Gabriele Beate Schweikert, Guido Sanguinetti:
M3D: a kernel-based test for spatially correlated changes in methylation profiles. Bioinform. 31(6): 809-816 (2015) - [j28]Vân Anh Huynh-Thu, Guido Sanguinetti:
Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinform. 31(10): 1614-1622 (2015) - [j27]Daniel Trejo-Baños, Andrew J. Millar, Guido Sanguinetti:
A Bayesian approach for structure learning in oscillating regulatory networks. Bioinform. 31(22): 3617-3624 (2015) - [j26]Luca Bortolussi, Guido Sanguinetti:
Learning and Designing Stochastic Processes from Logical Constraints. Log. Methods Comput. Sci. 11(2) (2015) - [j25]Ezio Bartocci, Luca Bortolussi, Laura Nenzi, Guido Sanguinetti:
System design of stochastic models using robustness of temporal properties. Theor. Comput. Sci. 587: 3-25 (2015) - [c22]Daniel Trejo-Baños, Andrew J. Millar, Guido Sanguinetti:
Experimental Design for Inference over the A. thaliana Circadian Clock Network. CMSB 2015: 28-39 - [c21]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
Efficient Stochastic Simulation of Systems with Multiple Time Scales via Statistical Abstraction. CMSB 2015: 40-51 - [c20]Ezio Bartocci, Luca Bortolussi, Dimitrios Milios, Laura Nenzi, Guido Sanguinetti:
Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic. HSB 2015: 156-172 - [c19]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
U-Check: Model Checking and Parameter Synthesis Under Uncertainty. QEST 2015: 89-104 - [c18]Luca Bortolussi, Dimitrios Milios, Guido Sanguinetti:
Machine Learning Methods in Statistical Model Checking and System Design - Tutorial. RV 2015: 323-341 - 2014
- [c17]Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti:
Data-Driven Statistical Learning of Temporal Logic Properties. FORMATS 2014: 23-37 - [c16]Sara Bufo, Ezio Bartocci, Guido Sanguinetti, Massimo Borelli, Umberto Lucangelo, Luca Bortolussi:
Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients. ISoLA (2) 2014: 391-403 - [c15]Luca Bortolussi, Guido Sanguinetti:
A Statistical Approach for Computing Reachability of Non-linear and Stochastic Dynamical Systems. QEST 2014: 41-56 - [c14]Anastasis Georgoulas, Jane Hillston, Dimitrios Milios, Guido Sanguinetti:
Probabilistic Programming Process Algebra. QEST 2014: 249-264 - [i2]Luca Bortolussi, Guido Sanguinetti:
Smoothed Model Checking for Uncertain Continuous Time Markov Chains. CoRR abs/1402.1450 (2014) - 2013
- [b1]Andrew Zammit-Mangion, Michael Dewar, Visakan Kadirkamanathan, Anaid Flesken, Guido Sanguinetti:
Modeling Conflict Dynamics with Spatio-temporal Data. Springer Briefs in Applied Sciences and Technology, Springer 2013, ISBN 978-3-319-01037-3, pp. I-VIII, 1-74 - [j24]Andrea Ocone, Andrew J. Millar, Guido Sanguinetti:
Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics. Bioinform. 29(7): 910-916 (2013) - [j23]Grigorios Skolidis, Guido Sanguinetti:
Semisupervised Multitask Learning With Gaussian Processes. IEEE Trans. Neural Networks Learn. Syst. 24(12): 2101-2112 (2013) - [c13]Anastasis Georgoulas, Jane Hillston, Guido Sanguinetti:
ABC-Fun: A Probabilistic Programming Language for Biology. CMSB 2013: 150-163 - [c12]Botond Cseke, Manfred Opper, Guido Sanguinetti:
Approximate inference in latent Gaussian-Markov models from continuous time observations. NIPS 2013: 971-979 - [c11]Luca Bortolussi, Guido Sanguinetti:
Learning and Designing Stochastic Processes from Logical Constraints. QEST 2013: 89-105 - [c10]Ezio Bartocci, Luca Bortolussi, Laura Nenzi, Guido Sanguinetti:
On the Robustness of Temporal Properties for Stochastic Models. HSB 2013: 3-19 - [c9]Andrea Ocone, Guido Sanguinetti:
A stochastic hybrid model of a biological filter. HAS 2013: 100-108 - [i1]Ezio Bartocci, Luca Bortolussi, Guido Sanguinetti:
Learning Temporal Logical Properties Discriminating ECG models of Cardiac Arrhytmias. CoRR abs/1312.7523 (2013) - 2012
- [j22]Grigorios Skolidis, Katja Hansen, Guido Sanguinetti, Matthias Rupp:
Multi-task learning for pKa prediction. J. Comput. Aided Mol. Des. 26(7): 883-895 (2012) - [j21]Grigorios Skolidis, Guido Sanguinetti:
A Case Study on Meta-Generalising: A Gaussian Processes Approach. J. Mach. Learn. Res. 13: 691-721 (2012) - [j20]Andrew Zammit-Mangion, Guido Sanguinetti, Visakan Kadirkamanathan:
Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations. IEEE Trans. Signal Process. 60(7): 3449-3459 (2012) - [c8]Anastasis Georgoulas, Allan Clark, Andrea Ocone, Stephen Gilmore, Guido Sanguinetti:
A subsystems approach for parameter estimation of ODE models of hybrid systems. HSB 2012: 30-41 - 2011
- [j19]Hafiz Muhammad Shahzad Asif, Guido Sanguinetti:
Large-scale learning of combinatorial transcriptional dynamics from gene expression. Bioinform. 27(9): 1277-1283 (2011) - [j18]Andrea Ocone, Guido Sanguinetti:
Reconstructing transcription factor activities in hierarchical transcription network motifs. Bioinform. 27(20): 2873-2879 (2011) - [j17]Maurizio Filippone, Guido Sanguinetti:
Approximate inference of the bandwidth in multivariate kernel density estimation. Comput. Stat. Data Anal. 55(12): 3104-3122 (2011) - [j16]Andrew Zammit-Mangion, Ke Yuan, Visakan Kadirkamanathan, Mahesan Niranjan, Guido Sanguinetti:
Online Variational Inference for State-Space Models with Point-Process Observations. Neural Comput. 23(8): 1967-1999 (2011) - [j15]Grigorios Skolidis, Guido Sanguinetti:
Bayesian Multitask Classification With Gaussian Process Priors. IEEE Trans. Neural Networks 22(12): 2011-2021 (2011) - [j14]Maurizio Filippone, Guido Sanguinetti:
A Perturbative Approach to Novelty Detection in Autoregressive Models. IEEE Trans. Signal Process. 59(3): 1027-1036 (2011) - [c7]Florian Stimberg, Manfred Opper, Guido Sanguinetti, Andreas Ruttor:
Inference in continuous-time change-point models. NIPS 2011: 2717-2725 - 2010
- [j13]Manfred Opper, Guido Sanguinetti:
Learning combinatorial transcriptional dynamics from gene expression data. Bioinform. 26(13): 1623-1629 (2010) - [j12]Hafiz Muhammad Shahzad Asif, Matthew D. Rolfe, Jeffrey Green, Neil D. Lawrence, Magnus Rattray, Guido Sanguinetti:
TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinform. 26(20): 2635-2636 (2010) - [j11]Michael Dewar, Visakan Kadirkamanathan, Manfred Opper, Guido Sanguinetti:
Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster. BMC Syst. Biol. 4: 21 (2010) - [j10]Maurizio Filippone, Guido Sanguinetti:
Information theoretic novelty detection. Pattern Recognit. 43(3): 805-814 (2010) - [c6]Manfred Opper, Andreas Ruttor, Guido Sanguinetti:
Approximate inference in continuous time Gaussian-Jump processes. NIPS 2010: 1831-1839 - [p1]Andreas Ruttor, Guido Sanguinetti, Manfred Opper:
Approximate Inference for Stochastic Reaction processes. Learning and Inference in Computational Systems Biology 2010: 277-296 - [e2]Neil D. Lawrence, Mark A. Girolami, Magnus Rattray, Guido Sanguinetti:
Learning and Inference in Computational Systems Biology. Computational molecular biology, MIT Press 2010, ISBN 978-0-262-01386-4 [contents]
2000 – 2009
- 2009
- [j9]Guido Sanguinetti, Andreas Ruttor, Manfred Opper, Cédric Archambeau:
Switching regulatory models of cellular stress response. Bioinform. 25(10): 1280-1286 (2009) - [j8]Richard D. Pearson, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence, Magnus Rattray:
puma: a Bioconductor package for propagating uncertainty in microarray analysis. BMC Bioinform. 10 (2009) - [c5]Paola Lecca, Alida Palmisano, Corrado Priami, Guido Sanguinetti:
A new probabilistic generative model of parameter inference in biochemical networks. SAC 2009: 758-765 - [e1]Visakan Kadirkamanathan, Guido Sanguinetti, Mark A. Girolami, Mahesan Niranjan, Josselin Noirel:
Pattern Recognition in Bioinformatics, 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Lecture Notes in Computer Science 5780, Springer 2009, ISBN 978-3-642-04030-6 [contents] - 2008
- [j7]Guido Sanguinetti, Josselin Noirel, Phillip C. Wright:
MMG: a probabilistic tool to identify submodules of metabolic pathways. Bioinform. 24(8): 1078-1084 (2008) - [j6]Josselin Noirel, Guido Sanguinetti, Phillip C. Wright:
Identifying differentially expressed subnetworks with MMG. Bioinform. 24(23): 2792-2793 (2008) - [j5]Guido Sanguinetti:
Dimensionality Reduction of Clustered Data Sets. IEEE Trans. Pattern Anal. Mach. Intell. 30(3): 535-540 (2008) - 2007
- [c4]Manfred Opper, Guido Sanguinetti:
Variational inference for Markov jump processes. NIPS 2007: 1105-1112 - 2006
- [j4]Magnus Rattray, Xuejun Liu, Guido Sanguinetti, Marta Milo, Neil D. Lawrence:
Propagating uncertainty in microarray data analysis. Briefings Bioinform. 7(1): 37-47 (2006) - [j3]Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence:
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinform. 22(14): 1753-1759 (2006) - [j2]Guido Sanguinetti, Neil D. Lawrence, Magnus Rattray:
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinform. 22(22): 2775-2781 (2006) - [c3]Guido Sanguinetti, Magnus Rattray, Neil D. Lawrence:
Identifying Submodules of Cellular Regulatory Networks. CMSB 2006: 155-168 - [c2]Guido Sanguinetti, Neil D. Lawrence:
Missing Data in Kernel PCA. ECML 2006: 751-758 - [c1]Neil D. Lawrence, Guido Sanguinetti, Magnus Rattray:
Modelling transcriptional regulation using Gaussian Processes. NIPS 2006: 785-792 - 2005
- [j1]Guido Sanguinetti, Marta Milo, Magnus Rattray, Neil D. Lawrence:
Accounting for probe-level noise in principal component analysis of microarray data. Bioinform. 21(19): 3748-3754 (2005)
Coauthor Index
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