default search action
Guido Sanguinetti
Person information
- affiliation: SISSA: Trieste, Italy
- affiliation: University of Edinburgh, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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
SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data. PLoS Comput. Biol. 18(6) (2022) - [c35]Svitlana Braichenko
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
On the Robustness of Bayesian Neural Networks to Adversarial Attacks. CoRR abs/2207.06154 (2022) - 2021
- [c33]Ginevra Carbone, Guido Sanguinetti
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
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
Neural field models for latent state inference: Application to large-scale neuronal recordings. PLoS Comput. Biol. 15(11) (2019) - [j39]Michalis Michaelides
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
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
ProPPA: Probabilistic Programming for Stochastic Dynamical Systems. ACM Trans. Model. Comput. Simul. 28(1): 3:1-3:23 (2018) - [c29]Dimitrios Milios, Guido Sanguinetti
Probabilistic Model Checking for Continuous-Time Markov Chains via Sequential Bayesian Inference. QEST 2018: 289-305 - [c28]Luca Bortolussi
Bayesian Statistical parametric Verification and synthesis by Machine Learning. WSC 2018: 381-394 - [c27]Guido Sanguinetti
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
Policy learning in continuous-time Markov decision processes using Gaussian Processes. Perform. Evaluation 116: 84-100 (2017) - [j34]Anastasis Georgoulas
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
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
Higher order methylation features for clustering and prediction in epigenomic studies. Bioinform. 32(17): 405-412 (2016) - [j32]Yuanhua Huang
Statistical modeling of isoform splicing dynamics from RNA-seq time series data. Bioinform. 32(19): 2965-2972 (2016) - [j31]Saulius Lukauskas
DGW: an exploratory data analysis tool for clustering and visualisation of epigenomic marks. BMC Bioinform. 17(S-16): 53-63 (2016) - [j30]Luca Bortolussi
Smoothed model checking for uncertain Continuous-Time Markov Chains. Inf. Comput. 247: 235-253 (2016) - [c25]Giulio Caravagna
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
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
A Bayesian approach for structure learning in oscillating regulatory networks. Bioinform. 31(22): 3617-3624 (2015) - [j26]Luca Bortolussi
Learning and Designing Stochastic Processes from Logical Constraints. Log. Methods Comput. Sci. 11(2) (2015) - [j25]Ezio Bartocci
System design of stochastic models using robustness of temporal properties. Theor. Comput. Sci. 587: 3-25 (2015) - [c22]Daniel Trejo-Baños
Experimental Design for Inference over the A. thaliana Circadian Clock Network. CMSB 2015: 28-39 - [c21]Luca Bortolussi
Efficient Stochastic Simulation of Systems with Multiple Time Scales via Statistical Abstraction. CMSB 2015: 40-51 - [c20]Ezio Bartocci
Studying Emergent Behaviours in Morphogenesis Using Signal Spatio-Temporal Logic. HSB 2015: 156-172 - [c19]Luca Bortolussi
U-Check: Model Checking and Parameter Synthesis Under Uncertainty. QEST 2015: 89-104 - [c18]Luca Bortolussi
Machine Learning Methods in Statistical Model Checking and System Design - Tutorial. RV 2015: 323-341 - 2014
- [c17]Ezio Bartocci
Data-Driven Statistical Learning of Temporal Logic Properties. FORMATS 2014: 23-37 - [c16]Sara Bufo, Ezio Bartocci, Guido Sanguinetti
Temporal Logic Based Monitoring of Assisted Ventilation in Intensive Care Patients. ISoLA (2) 2014: 391-403 - [c15]Luca Bortolussi
A Statistical Approach for Computing Reachability of Non-linear and Stochastic Dynamical Systems. QEST 2014: 41-56 - [c14]Anastasis Georgoulas
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
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
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
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
Learning and Designing Stochastic Processes from Logical Constraints. QEST 2013: 89-105 - [c10]Ezio Bartocci
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
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
Variational Estimation in Spatiotemporal Systems From Continuous and Point-Process Observations. IEEE Trans. Signal Process. 60(7): 3449-3459 (2012) - [c8]Anastasis Georgoulas
A subsystems approach for parameter estimation of ODE models of hybrid systems. HSB 2012: 30-41 - 2011
- [j19]Hafiz Muhammad Shahzad Asif
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
Approximate inference of the bandwidth in multivariate kernel density estimation. Comput. Stat. Data Anal. 55(12): 3104-3122 (2011) - [j16]Andrew Zammit-Mangion
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
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
TFInfer: a tool for probabilistic inference of transcription factor activities. Bioinform. 26(20): 2635-2636 (2010) - [j11]Michael Dewar, Visakan Kadirkamanathan
Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster. BMC Syst. Biol. 4: 21 (2010) - [j10]Maurizio Filippone
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
Switching regulatory models of cellular stress response. Bioinform. 25(10): 1280-1286 (2009) - [j8]Richard D. Pearson
puma: a Bioconductor package for propagating uncertainty in microarray analysis. BMC Bioinform. 10 (2009) - [c5]Paola Lecca
A new probabilistic generative model of parameter inference in biochemical networks. SAC 2009: 758-765 - [e1]Visakan Kadirkamanathan
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
MMG: a probabilistic tool to identify submodules of metabolic pathways. Bioinform. 24(8): 1078-1084 (2008) - [j6]Josselin Noirel, Guido Sanguinetti
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
Propagating uncertainty in microarray data analysis. Briefings Bioinform. 7(1): 37-47 (2006) - [j3]Guido Sanguinetti
A probabilistic dynamical model for quantitative inference of the regulatory mechanism of transcription. Bioinform. 22(14): 1753-1759 (2006) - [j2]Guido Sanguinetti
Probabilistic inference of transcription factor concentrations and gene-specific regulatory activities. Bioinform. 22(22): 2775-2781 (2006) - [c3]Guido Sanguinetti
Identifying Submodules of Cellular Regulatory Networks. CMSB 2006: 155-168 - [c2]Guido Sanguinetti
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
Accounting for probe-level noise in principal component analysis of microarray data. Bioinform. 21(19): 3748-3754 (2005)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-10-07 21:14 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: