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Maurizio Filippone
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2020 – today
- 2024
- [j26]Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone:
Improved Random Features for Dot Product Kernels. J. Mach. Learn. Res. 25: 235:1-235:75 (2024) - [c59]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i39]Theodore Papamarkou, Maria Skoularidou, Konstantina Palla, Laurence Aitchison, Julyan Arbel, David B. Dunson, Maurizio Filippone, Vincent Fortuin, Philipp Hennig, José Miguel Hernández-Lobato, Aliaksandr Hubin, Alexander Immer, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i38]Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone, Vassili Kitsios, Terry O'Kane:
Variational DAG Estimation via State Augmentation With Stochastic Permutations. CoRR abs/2402.02644 (2024) - [i37]Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone, Balázs Kégl:
A Multi-step Loss Function for Robust Learning of the Dynamics in Model-based Reinforcement Learning. CoRR abs/2402.03146 (2024) - [i36]Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone:
Robust Classification by Coupling Data Mollification with Label Smoothing. CoRR abs/2406.01494 (2024) - 2023
- [j25]Giulio Franzese
, Simone Rossi
How Much Is Enough? A Study on Diffusion Times in Score-Based Generative Models. Entropy 25(4): 633 (2023) - [c58]Jonas Wacker, Ruben Ohana, Maurizio Filippone:
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel. AISTATS 2023: 5181-5212 - [c57]Ba-Hien Tran, Babak Shahbaba
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. ICML 2023: 34409-34430 - [c56]Bogdan L. Kozyrskiy, Dimitrios Milios, Maurizio Filippone:
Imposing Functional Priors on Bayesian Neural Networks. ICPRAM 2023: 450-457 - [c55]Giulio Franzese, Giulio Corallo, Simone Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi:
Continuous-Time Functional Diffusion Processes. NeurIPS 2023 - [c54]Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone:
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models. NeurIPS 2023 - [i35]Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone:
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes. CoRR abs/2302.04534 (2023) - [i34]Giulio Franzese, Simone Rossi, Dario Rossi, Markus Heinonen, Maurizio Filippone, Pietro Michiardi:
Continuous-Time Functional Diffusion Processes. CoRR abs/2303.00800 (2023) - [i33]Davit Gogolashvili, Matteo Zecchin, Motonobu Kanagawa, Marios Kountouris, Maurizio Filippone:
When is Importance Weighting Correction Needed for Covariate Shift Adaptation? CoRR abs/2303.04020 (2023) - [i32]Ba-Hien Tran, Giulio Franzese, Pietro Michiardi, Maurizio Filippone:
One-Line-of-Code Data Mollification Improves Optimization of Likelihood-based Generative Models. CoRR abs/2305.18900 (2023) - [i31]Abdelhakim Benechehab, Giuseppe Paolo, Albert Thomas, Maurizio Filippone, Balázs Kégl:
Multi-timestep models for Model-based Reinforcement Learning. CoRR abs/2310.05672 (2023) - [i30]Andrew Zammit-Mangion, Michael D. Kaminski, Ba-Hien Tran, Maurizio Filippone, Noel Cressie
Spatial Bayesian Neural Networks. CoRR abs/2311.09491 (2023) - 2022
- [j24]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone:
All You Need is a Good Functional Prior for Bayesian Deep Learning. J. Mach. Learn. Res. 23: 74:1-74:56 (2022) - [c53]Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
Revisiting the Effects of Stochasticity for Hamiltonian Samplers. ICML 2022: 6744-6778 - [c52]Jonas Wacker, Maurizio Filippone:
Local Random Feature Approximations of the Gaussian Kernel. KES 2022: 987-996 - [c51]Bogdan L. Kozyrskiy, Dimitrios Milios, Maurizio Filippone:
Variational Bootstrap for Classification. KES 2022: 1222-1231 - [c50]Davit Gogolashvili, Bogdan L. Kozyrskiy, Maurizio Filippone:
Locally Smoothed Gaussian Process Regression. KES 2022: 2717-2726 - [i29]Jonas Wacker, Motonobu Kanagawa, Maurizio Filippone:
Improved Random Features for Dot Product Kernels. CoRR abs/2201.08712 (2022) - [i28]Jonas Wacker, Ruben Ohana, Maurizio Filippone:
Complex-to-Real Random Features for Polynomial Kernels. CoRR abs/2202.02031 (2022) - [i27]Jonas Wacker, Maurizio Filippone:
Local Random Feature Approximations of the Gaussian Kernel. CoRR abs/2204.05667 (2022) - [i26]Giulio Franzese, Simone Rossi
How Much is Enough? A Study on Diffusion Times in Score-based Generative Models. CoRR abs/2206.05173 (2022) - [i25]Davit Gogolashvili, Bogdan L. Kozyrskiy, Maurizio Filippone:
Locally Smoothed Gaussian Process Regression. CoRR abs/2210.09998 (2022) - 2021
- [j23]Giulio Franzese
A Scalable Bayesian Sampling Method Based on Stochastic Gradient Descent Isotropization. Entropy 23(11): 1426 (2021) - [c49]Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations. AISTATS 2021: 1837-1845 - [c48]Raphaël Azorin
Towards a generic deep learning pipeline for traffic measurements. StudentWorkshop@CoNEXT 2021: 5-6 - [c47]Graziano Mita, Maurizio Filippone, Pietro Michiardi:
An Identifiable Double VAE For Disentangled Representations. ICML 2021: 7769-7779 - [c46]Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone:
Sparse within Sparse Gaussian Processes using Neighbor Information. ICML 2021: 10369-10378 - [c45]Matthieu Da Silva-Filarder, Andrea Ancora, Maurizio Filippone, Pietro Michiardi:
Multimodal Variational Autoencoders for Sensor Fusion and Cross Generation. ICMLA 2021: 1069-1076 - [c44]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone:
Model Selection for Bayesian Autoencoders. NeurIPS 2021: 19730-19742 - [i24]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Pietro Michiardi, Edwin V. Bonilla, Maurizio Filippone:
Model Selection for Bayesian Autoencoders. CoRR abs/2106.06245 (2021) - [i23]Giulio Franzese, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
A Unified View of Stochastic Hamiltonian Sampling. CoRR abs/2106.16200 (2021) - 2020
- [j22]Rémi Domingues
A comparative evaluation of novelty detection algorithms for discrete sequences. Artif. Intell. Rev. 53(5): 3787-3812 (2020) - [c43]Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi:
LIBRE: Learning Interpretable Boolean Rule Ensembles. AISTATS 2020: 245-255 - [c42]Ruben Ohana, Jonas Wacker, Jonathan Dong, Sébastien Marmin, Florent Krzakala
Kernel Computations from Large-Scale Random Features Obtained by Optical Processing Units. ICASSP 2020: 9294-9298 - [c41]Simone Rossi, Sébastien Marmin, Maurizio Filippone:
Walsh-Hadamard Variational Inference for Bayesian Deep Learning. NeurIPS 2020 - [c40]Rosa Candela, Pietro Michiardi, Maurizio Filippone, Maria A. Zuluaga:
Model Monitoring and Dynamic Model Selection in Travel Time-Series Forecasting. ECML/PKDD (4) 2020: 513-529 - [i22]Simone Rossi, Markus Heinonen, Edwin V. Bonilla, Zheyang Shen, Maurizio Filippone:
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations. CoRR abs/2003.03080 (2020) - [i21]Dimitrios Milios, Pietro Michiardi, Maurizio Filippone:
A Variational View on Bootstrap Ensembles as Bayesian Inference. CoRR abs/2006.04548 (2020) - [i20]Giulio Franzese, Rosa Candela, Dimitrios Milios, Maurizio Filippone, Pietro Michiardi:
Isotropic SGD: a Practical Approach to Bayesian Posterior Sampling. CoRR abs/2006.05087 (2020) - [i19]Graziano Mita, Maurizio Filippone, Pietro Michiardi:
Learning Optimal Conditional Priors For Disentangled Representations. CoRR abs/2010.09360 (2020) - [i18]Gia-Lac Tran, Dimitrios Milios, Pietro Michiardi, Maurizio Filippone:
Sparse within Sparse Gaussian Processes using Neighbor Information. CoRR abs/2011.05041 (2020) - [i17]Ba-Hien Tran, Simone Rossi, Dimitrios Milios, Maurizio Filippone:
All You Need is a Good Functional Prior for Bayesian Deep Learning. CoRR abs/2011.12829 (2020)
2010 – 2019
- 2019
- [j21]Marco Lorenzi
Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer's disease. NeuroImage 190: 56-68 (2019) - [c39]Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone:
Calibrating Deep Convolutional Gaussian Processes. AISTATS 2019: 1554-1563 - [c38]Simone Rossi, Pietro Michiardi, Maurizio Filippone:
Good Initializations of Variational Bayes for Deep Models. ICML 2019: 5487-5497 - [c37]Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman:
Pseudo-Extended Markov chain Monte Carlo. NeurIPS 2019: 4314-4324 - [c36]Duc-Trung Nguyen, Maurizio Filippone
Exact gaussian process regression with distributed computations. SAC 2019: 1286-1295 - [i16]Remi Domingues, Pietro Michiardi, Jérémie Barlet, Maurizio Filippone:
A comparative evaluation of novelty detection algorithms for discrete sequences. CoRR abs/1902.10940 (2019) - [i15]Simone Rossi, Sébastien Marmin, Maurizio Filippone:
Walsh-Hadamard Variational Inference for Bayesian Deep Learning. CoRR abs/1905.11248 (2019) - [i14]Rosa Candela, Giulio Franzese, Maurizio Filippone, Pietro Michiardi:
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD. CoRR abs/1910.09466 (2019) - [i13]Ruben Ohana
Kernel computations from large-scale random features obtained by Optical Processing Units. CoRR abs/1910.09880 (2019) - [i12]Graziano Mita, Paolo Papotti, Maurizio Filippone, Pietro Michiardi:
LIBRE: Learning Interpretable Boolean Rule Ensembles. CoRR abs/1911.06537 (2019) - [i11]Simone Rossi, Sébastien Marmin, Maurizio Filippone:
Efficient Approximate Inference with Walsh-Hadamard Variational Inference. CoRR abs/1912.00015 (2019) - 2018
- [j20]Mu Niu, Benn Macdonald, Simon Rogers, Maurizio Filippone
Statistical inference in mechanistic models: time warping for improved gradient matching. Comput. Stat. 33(2): 1091-1123 (2018) - [j19]Remi Domingues
Deep Gaussian Process autoencoders for novelty detection. Mach. Learn. 107(8-10): 1363-1383 (2018) - [j18]Remi Domingues, Maurizio Filippone
A comparative evaluation of outlier detection algorithms: Experiments and analyses. Pattern Recognit. 74: 406-421 (2018) - [c35]Paul de Kerret, David Gesbert
Team Deep Neural Networks for Interference Channels. ICC Workshops 2018: 1-6 - [c34]Marco Lorenzi, Maurizio Filippone:
Constraining the Dynamics of Deep Probabilistic Models. ICML 2018: 3233-3242 - [c33]Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. NeurIPS 2018: 6008-6018 - [i10]Gia-Lac Tran, Edwin V. Bonilla, John P. Cunningham, Pietro Michiardi, Maurizio Filippone:
Calibrating Deep Convolutional Gaussian Processes. CoRR abs/1805.10522 (2018) - [i9]Dimitrios Milios, Raffaello Camoriano, Pietro Michiardi, Lorenzo Rosasco, Maurizio Filippone:
Dirichlet-based Gaussian Processes for Large-scale Calibrated Classification. CoRR abs/1805.10915 (2018) - [i8]Simone Rossi, Pietro Michiardi, Maurizio Filippone:
Good Initializations of Variational Bayes for Deep Models. CoRR abs/1810.08083 (2018) - [i7]Sébastien Marmin, Maurizio Filippone:
Variational Calibration of Computer Models. CoRR abs/1810.12177 (2018) - 2017
- [c32]Kurt Cutajar, Edwin V. Bonilla, Pietro Michiardi, Maurizio Filippone:
Random Feature Expansions for Deep Gaussian Processes. ICML 2017: 884-893 - [c31]Yufei Han, Maurizio Filippone
Mini-batch spectral clustering. IJCNN 2017: 3888-3895 - [c30]Jack K. Fitzsimons, Diego Granziol, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone
Entropic Trace Estimates for Log Determinants. ECML/PKDD (1) 2017: 323-338 - [c29]Jack K. Fitzsimons, Kurt Cutajar, Maurizio Filippone, Michael A. Osborne, Stephen J. Roberts:
Bayesian Inference of Log Determinants. UAI 2017 - [c28]Karl Krauth, Edwin V. Bonilla, Kurt Cutajar, Maurizio Filippone:
AutoGP: Exploring the Capabilities and Limitations of Gaussian Process Models. UAI 2017 - [i6]Jack K. Fitzsimons, Kurt Cutajar, Michael A. Osborne, Stephen J. Roberts, Maurizio Filippone:
Bayesian Inference of Log Determinants. CoRR abs/1704.01445 (2017) - [i5]Jack K. Fitzsimons, Diego Granziol, Kurt Cutajar, Michael A. Osborne, Maurizio Filippone, Stephen J. Roberts:
Entropic Trace Estimates for Log Determinants. CoRR abs/1704.07223 (2017) - [i4]Paul de Kerret, David Gesbert, Maurizio Filippone:
Decentralized Deep Scheduling for Interference Channels. CoRR abs/1711.00625 (2017) - 2016
- [c27]Mu Niu, Simon Rogers, Maurizio Filippone
Parameter Inference in Differential Equation Models of Biopathways Using Time Warped Gradient Matching. CIBB 2016: 145-159 - [c26]Umberto Noè, Weiwei Chen, Maurizio Filippone
Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation Using Statistical Emulation. CIBB 2016: 184-198 - [c25]Mu Niu, Simon Rogers, Maurizio Filippone, Dirk Husmeier:
Fast Parameter Inference in Nonlinear Dynamical Systems using Iterative Gradient Matching. ICML 2016: 1699-1707 - [c24]Kurt Cutajar, Michael A. Osborne, John P. Cunningham, Maurizio Filippone:
Preconditioning Kernel Matrices. ICML 2016: 2529-2538 - [c23]Xiaoyu Xiong, Maurizio Filippone
Looking Good With Flickr Faves: Gaussian Processes for Finding Difference Makers in Personality Impressions. ACM Multimedia 2016: 412-415 - [i3]Yufei Han, Yun Shen, Maurizio Filippone:
Mini-Batch Spectral Clustering. CoRR abs/1607.02024 (2016) - 2015
- [j17]Matteo Dell'Amico
On User Availability Prediction and Network Applications. IEEE/ACM Trans. Netw. 23(4): 1300-1313 (2015) - [c22]Matteo Dell'Amico
Monte Carlo Strength Evaluation: Fast and Reliable Password Checking. CCS 2015: 158-169 - [c21]Maurizio Filippone, Raphael Engler:
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE). ICML 2015: 1015-1024 - [c20]James Hensman, Alexander G. de G. Matthews, Maurizio Filippone, Zoubin Ghahramani:
MCMC for Variationally Sparse Gaussian Processes. NIPS 2015: 1648-1656 - 2014
- [j16]Maurizio Filippone
Pseudo-Marginal Bayesian Inference for Gaussian Processes. IEEE Trans. Pattern Anal. Mach. Intell. 36(11): 2214-2226 (2014) - [j15]Samuel Kim, Fabio Valente, Maurizio Filippone
Predicting Continuous Conflict Perceptionwith Bayesian Gaussian Processes. IEEE Trans. Affect. Comput. 5(2): 187-200 (2014) - [c19]Maurizio Filippone
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC. ICPR 2014: 614-619 - [c18]Andrew D. O'Harney, Andre F. Marquand, Katya Rubia
Pseudo-Marginal Bayesian Multiple-Class Multiple-Kernel Learning for Neuroimaging Data. ICPR 2014: 3185-3190 - [i2]Matteo Dell'Amico, Maurizio Filippone, Pietro Michiardi, Yves Roudier:
On User Availability Prediction and Network Applications. CoRR abs/1404.7688 (2014) - 2013
- [j14]Maurizio Filippone
A comparative evaluation of stochastic-based inference methods for Gaussian process models. Mach. Learn. 93(1): 93-114 (2013) - [j13]Yin Zhao, Jongrae Kim
Aggregation Algorithm Towards Large-Scale Boolean Network Analysis. IEEE Trans. Autom. Control. 58(8): 1976-1985 (2013) - [j12]Derong Liu
Editorial A Successful Change From TNN to TNNLS and a Very Successful Year. IEEE Trans. Neural Networks Learn. Syst. 24(1): 1-7 (2013) - [c17]Frank Dondelinger, Dirk Husmeier, Simon Rogers, Maurizio Filippone:
ODE parameter inference using adaptive gradient matching with Gaussian processes. AISTATS 2013: 216-228 - [i1]Maurizio Filippone, Mark A. Girolami:
Exact-Approximate Bayesian Inference for Gaussian Processes. CoRR abs/1310.0740 (2013) - 2012
- [c16]Gelareh Mohammadi
From speech to personality: mapping voice quality and intonation into personality differences. ACM Multimedia 2012: 789-792 - [c15]Samuel Kim, Maurizio Filippone
Predicting the conflict level in television political debates: an approach based on crowdsourcing, nonverbal communication and gaussian processes. ACM Multimedia 2012: 793-796 - [c14]Matteo Campo, Anna Polychroniou, Hugues Salamin, Maurizio Filippone
Towards Causal Modeling of Human Behavior. WIRN 2012: 337-344 - 2011
- [j11]Maurizio Filippone
Approximate inference of the bandwidth in multivariate kernel density estimation. Comput. Stat. Data Anal. 55(12): 3104-3122 (2011) - [j10]Maurizio Filippone
Discussion of the paper: "Sampling schemes for generalized linear Dirichlet process random effects models" by M. Kyung, J. Gill, and G. Casella. Stat. Methods Appl. 20(3): 295-297 (2011) - [j9]Maurizio Filippone
Simulated annealing for supervised gene selection. Soft Comput. 15(8): 1471-1482 (2011) - [j8]Maurizio Filippone
A Perturbative Approach to Novelty Detection in Autoregressive Models. IEEE Trans. Signal Process. 59(3): 1027-1036 (2011) - 2010
- [j7]Maurizio Filippone
Information theoretic novelty detection. Pattern Recognit. 43(3): 805-814 (2010) - [j6]Maurizio Filippone
Applying the Possibilistic c-Means Algorithm in Kernel-Induced Spaces. IEEE Trans. Fuzzy Syst. 18(3): 572-584 (2010)
2000 – 2009
- 2009
- [j5]Maurizio Filippone
Dealing with non-metric dissimilarities in fuzzy central clustering algorithms. Int. J. Approx. Reason. 50(2): 363-384 (2009) - [j4]Maurizio Filippone, Francesco Masulli, Stefano Rovetta:
Clustering in the membership embedding space. Int. J. Knowl. Eng. Soft Data Paradigms 1(4): 363-375 (2009) - [j3]Stefano Rovetta
Soft ranking in clustering. Neurocomputing 72(7-9): 2028-2031 (2009) - [j2]Francesco Camastra
A comparative evaluation of nonlinear dynamics methods for time series prediction. Neural Comput. Appl. 18(8): 1021-1029 (2009) - [c13]Stefano Rovetta
The discriminating power of random features. WIRN 2009: 3-10 - 2008
- [j1]Maurizio Filippone
A survey of kernel and spectral methods for clustering. Pattern Recognit. 41(1): 176-190 (2008) - [c12]Maurizio Filippone
Stability and Performances in Biclustering Algorithms. CIBB 2008: 91-101 - [c11]Daniel Barbará, Carlotta Domeniconi, Zoran Duric, Maurizio Filippone
Detecting Suspicious Behavior in Surveillance Images. ICDM Workshops 2008: 891-900 - [c10]Maurizio Filippone
An Experimental Comparison of Kernel Clustering Methods. WIRN 2008: 118-126 - 2007
- [c9]Elio Canestrelli
Local Learning of Tide Level Time Series using a Fuzzy Approach. IJCNN 2007: 1813-1818 - [c8]Francesco Camastra, Maurizio Filippone:
SVM-Based Time Series Prediction with Nonlinear Dynamics Methods. KES (3) 2007: 300-307 - [c7]Stefano Rovetta, Francesco Masulli, Maurizio Filippone:
Membership Embedding Space Approach and Spectral Clustering. KES (3) 2007: 901-908 - [c6]Maurizio Filippone, Francesco Masulli, Stefano Rovetta:
Possibilistic Clustering in Feature Space. WILF 2007: 219-226 - 2006
- [c5]Maurizio Filippone, Francesco Masulli, Stefano Rovetta, Sushmita Mitra
Possibilistic Approach to Biclustering: An Application to Oligonucleotide Microarray Data Analysis. CMSB 2006: 312-322 - [c4]Maurizio Filippone, Francesco Masulli, Stefano Rovetta:
Supervised Classification and Gene Selection Using Simulated Annealing. IJCNN 2006: 3566-3571 - 2005
- [c3]Maurizio Filippone
Unsupervised Gene Selection and Clustering Using Simulated Annealing. WILF 2005: 229-235 - [c2]Stefano Rovetta
Soft Rank Clustering. WIRN/NAIS 2005: 207-213 - 2004
- [c1]Maurizio Filippone, Francesco Masulli, Stefano Rovetta:
ERAF: A R Package for Regression and Forecasting. WIRN 2004: 165-173
Coauthor Index
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