Kernels for Periodic Time Series Arising in Astronomy. - Gabriel Wachman; Roni Khardon; Pavlos Protopapas; Charles R. Alcock (ECML/PKDD (2), 2009)

Event Discovery in Time Series. - Dan Preston; Pavlos Protopapas; Carla E. Brodley (SDM, 2009)

Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars - Umaa Rebbapragada; Pavlos Protopapas; Carla E. Brodley; Charles R. Alcock (CoRR, 2009)

Finding anomalous periodic time series. - Umaa Rebbapragada; Pavlos Protopapas; Carla E. Brodley; Charles R. Alcock (Mach. Learn., 2009)

Discovering arbitrary event types in time series. - Dan Preston; Pavlos Protopapas; Carla E. Brodley (Stat. Anal. Data Min., 2009)

Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures. - Eamonn J. Keogh; Li Wei 001; Xiaopeng Xi; Michail Vlachos; Sang-Hee Lee 003; Pavlos Protopapas (VLDB J., 2009)

Period detection in light curves from astronomical objects using correntropy. - Pablo A. Estévez; Pablo Huijse; Pablo Zegers; José C. Príncipe; Pavlos Protopapas (IJCNN, 2010)

Shift-Invariant Grouped Multi-task Learning for Gaussian Processes. - Yuyang Wang; Roni Khardon; Pavlos Protopapas (ECML/PKDD (3), 2010)

Estimation of periodicity in non-uniformly sampled astronomical data using a 2D kernel in correntropy. - Bibhu Prasad Mishra; José C. Príncipe; Pablo A. Estévez; Pavlos Protopapas (MLSP, 2011)

Nonparametric Bayesian Estimation of Periodic Functions - Yuyang Wang; Roni Khardon; Pavlos Protopapas (CoRR, 2011)

Period Estimation in Astronomical Time Series Using Slotted Correntropy. - Pablo Huijse; Pablo A. Estévez; Pablo Zegers; José Carlos Príncipe; Pavlos Protopapas (IEEE Signal Process. Lett., 2011)

Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes - Yuyang Wang; Roni Khardon; Pavlos Protopapas (CoRR, 2012)

An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves. - Pablo Huijse; Pablo A. Estévez; Pavlos Protopapas; Pablo Zegers; José Carlos Príncipe (IEEE Trans. Signal Process., 2012)

Automatic Classification of Variable Stars in Catalogs with missing data. - Karim Pichara; Pavlos Protopapas (CoRR, 2013)

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases. - Pablo Huijse; Pablo A. Estévez; Pavlos Protopapas; José C. Príncipe; Pablo Zegers (IEEE Comput. Intell. Mag., 2014)

A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set. - Pavlos Protopapas; Pablo Huijse; Pablo A. Estévez; Pablo Zegers; José C. Príncipe (CoRR, 2014)

Supervised detection of anomalous light-curves in massive astronomical catalogs. - Isadora nun; Karim Pichara; Pavlos Protopapas; Dae-Won Kim (CoRR, 2014)

Optimizing the Multiclass F-Measure via Biconcave Programming. - Harikrishna Narasimhan; Weiwei Pan; Purushottam Kar; Pavlos Protopapas; Harish G. Ramaswamy (ICDM, 2016)

Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice. - Xide Xia; Pavlos Protopapas; Finale Doshi-Velez (SDM, 2016)

Clustering Based Feature Learning on Variable Stars. - Cristóbal Mackenzie; Karim Pichara; Pavlos Protopapas (CoRR, 2016)

Robust period estimation using mutual information for multi-band light curves in the synoptic survey era. - Pablo Huijse; Pablo A. Estévez; Francisco Förster; Scott F. Daniel; Andrew J. Connolly; Pavlos Protopapas; Rodrigo Carrasco; José C. Príncipe (CoRR, 2017)

Modeling the Effects of Students' Interactions with Immersive Simulations using Markov Switching Systems. - Nicholas Hoernle; Ya'akov Gal; Barbara J. Grosz; Pavlos Protopapas; Andee Rubin (EDM, 2018)

Efficient Optimization of Echo State Networks for Time Series Datasets. - Jacob Reinier Maat; Nikos Gianniotis; Pavlos Protopapas (IJCNN, 2018)

T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling. - Giorgia Ramponi; Pavlos Protopapas; Marco Brambilla 001; Ryan Janssen (CoRR, 2018)

Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models. - Marouan Belhaj; Pavlos Protopapas; Weiwei Pan (CoRR, 2018)

Big Data Scalability of BayesPhylogenies on Harvard's Ozone 12k Cores. - M. Manjunathaiah; Andrew Meade; R. Thavarajan; Pavlos Protopapas; R. Dave (BIOINFORMATICS, 2019)

A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification. - Belén Saldías-Fuentes; Pavlos Protopapas; Karim Pichara B. (SDM, 2019)

An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves. - Christian Pieringer; Karim Pichara; Márcio Catelán; Pavlos Protopapas (CoRR, 2019)

Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning. - Alessandro Bianchi; Moreno Raimondo Vendra; Pavlos Protopapas; Marco Brambilla 001 (CoRR, 2019)

Physical Symmetries Embedded in Neural Networks. - Marios Mattheakis; Pavlos Protopapas; David Sondak; Marco Di Giovanni 001; Efthimios Kaxiras (CoRR, 2019)

Adversarial Variational Domain Adaptation. - Manuel Pérez-Carrasco; Guillermo Cabrera-Vives; Pavlos Protopapas; Nicolas Astorga; Marouan Belhaj (CoRR, 2019)

An Information Theory Approach on Deciding Spectroscopic Follow Ups. - Javiera Astudillo; Pavlos Protopapas; Karim Pichara; Pablo Huijse (CoRR, 2019)

Streaming Classification of Variable Stars. - Lukas Zorich; Karim Pichara; Pavlos Protopapas (CoRR, 2019)

Finding Multiple Solutions of ODEs with Neural Networks. - Marco Di Giovanni 001; David Sondak; Pavlos Protopapas; Marco Brambilla 001 (AAAI Spring Symposium: MLPS, 2020)

MPCC: Matching Priors and Conditionals for Clustering. - Nicolás Astorga; Pablo Huijse; Pavlos Protopapas; Pablo A. Estévez (ECCV (23), 2020)

Gender Classification and Bias Mitigation in Facial Images. - Wenying Wu; Panagiotis Michalatos; Pavlos Protopapas; Zheng Yang (WebSci, 2020)

Hamiltonian Neural Networks for solving differential equations. - Marios Mattheakis; David Sondak; Akshunna S. Dogra; Pavlos Protopapas (CoRR, 2020)

Scalable End-to-end Recurrent Neural Network for Variable star classification. - Ignacio Becker; Karim Pichara; Márcio Catelán; Pavlos Protopapas; Carlos Aguirre; Fatemeh Nikzat (CoRR, 2020)

Gravitational Wave Detection and Information Extraction via Neural Networks. - Gerson R. Santos; Marcela P. Figueiredo; Antonio de Pádua Santos; Pavlos Protopapas; Tiago A. E. Ferreira (CoRR, 2020)

Application of Machine Learning to Predict the Risk of Alzheimer's Disease: An Accurate and Practical Solution for Early Diagnostics. - Courtney Cochrane; David Castineira; Nisreen Shiban; Pavlos Protopapas (CoRR, 2020)

Solving Differential Equations Using Neural Network Solution Bundles. - Cedric Flamant; Pavlos Protopapas; David Sondak (CoRR, 2020)

Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks. - Dylan Randle; Pavlos Protopapas; David Sondak (CoRR, 2020)

Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread. - Alessandro Paticchio; Tommaso Scarlatti; Marios Mattheakis; Pavlos Protopapas; Marco Brambilla 001 (CoRR, 2020)

Unsupervised Neural Networks for Quantum Eigenvalue Problems. - Henry Jin; Marios Mattheakis; Pavlos Protopapas (CoRR, 2020)

NeuroDiffEq: A Python package for solving differential equations with neural networks. - Feiyu Chen; David Sondak; Pavlos Protopapas; Marios Mattheakis; Shuheng Liu 001; Devansh Agarwal; Marco Di Giovanni 001 (J. Open Source Softw., 2020)

A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function. - Tiago A. E. Ferreira; Marios Mattheakis; Pavlos Protopapas (CoRR, 2021)

The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves. - Cristobal Donoso-Oliva; Guillermo Cabrera-Vives; Pavlos Protopapas; Rodrigo Carrasco-Davis; Pablo A. Estévez (CoRR, 2021)

Encoding Involutory Invariance in Neural Networks. - Anwesh Bhattacharya; Marios Mattheakis; Pavlos Protopapas (CoRR, 2021)

Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems. - Shaan Desai; Marios Mattheakis; David Sondak; Pavlos Protopapas; Stephen J. Roberts (CoRR, 2021)

Unsupervised Reservoir Computing for Solving Ordinary Differential Equations. - Marios Mattheakis; Hayden Joy; Pavlos Protopapas (CoRR, 2021)

One-Shot Transfer Learning of Physics-Informed Neural Networks. - Shaan Desai; Marios Mattheakis; Hayden Joy; Pavlos Protopapas; Stephen J. Roberts (CoRR, 2021)

Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow. - Haitz Sáez de Ocáriz Borde; David Sondak; Pavlos Protopapas (CoRR, 2021)

Uncertainty Quantification in Neural Differential Equations. - Olga Graf; Pablo Flores; Pavlos Protopapas; Karim Pichara (CoRR, 2021)

Adversarial Sampling for Solving Differential Equations with Neural Networks. - Kshitij Parwani; Pavlos Protopapas (CoRR, 2021)

Building astroBERT, a language model for Astronomy & Astrophysics. - Félix Grèzes; Sergi Blanco-Cuaresma; Alberto Accomazzi; Michael J. Kurtz; Golnaz Shapurian; Edwin A. Henneken; Carolyn S. Grant; Donna M. Thompson; Roman Chyla; Stephen McDonald; Timothy W. Hostetler; Matthew R. Templeton; et al. (CoRR, 2021)

Encoding Involutory Invariances in Neural Networks. - Anwesh Bhattacharya; Marios Mattheakis; Pavlos Protopapas (IJCNN, 2022)

Physics-Informed Neural Networks for Quantum Eigenvalue Problems. - Henry Jin; Marios Mattheakis; Pavlos Protopapas (IJCNN, 2022)

Con2DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations. - Manuel Pérez-Carrasco; Pavlos Protopapas; Guillermo Cabrera-Vives (CoRR, 2022)

ASTROMER: A transformer-based embedding for the representation of light curves. - Cristobal Donoso-Oliva; Ignacio Becker; Pavlos Protopapas; Guillermo Cabrera-Vives; Vishnu M.; Harsh Vardhan (CoRR, 2022)

Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks. - Germán García-Jara; Pavlos Protopapas; Pablo A. Estévez (CoRR, 2022)

Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems. - Shuheng Liu 001; Xiyue Huang; Pavlos Protopapas (CoRR, 2022)

RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization. - Hayden Joy; Marios Mattheakis; Pavlos Protopapas (CoRR, 2022)

DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks. - Blake Bullwinkel; Dylan Randle; Pavlos Protopapas; David Sondak (CoRR, 2022)

Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows. - Raphaël Pellegrin; Blake Bullwinkel; Marios Mattheakis; Pavlos Protopapas (CoRR, 2022)

Improving astroBERT using Semantic Textual Similarity. - Félix Grèzes; Thomas Allen; Sergi Blanco-Cuaresma; Alberto Accomazzi; Michael J. Kurtz; Golnaz Shapurian; Edwin A. Henneken; Carolyn S. Grant; Donna M. Thompson; Timothy W. Hostetler; Matthew R. Templeton; Kelly E. Lockhart; et al. (CoRR, 2022)

Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks. - Olga Graf; Pablo Flores; Pavlos Protopapas; Karim Pichara (CoRR, 2022)

Gravitational wave signal recognition and ring-down time estimation via Artificial Neural Networks. - Gerson R. Santos; Antonio de Pádua Santos; Pavlos Protopapas; Tiago A. E. Ferreira (Expert Syst. Appl., 2022)

Residual-based error bound for physics-informed neural networks. - Shuheng Liu 001; Xiyue Huang; Pavlos Protopapas (UAI, 2023)

One-Shot Transfer Learning for Nonlinear ODEs. - Wanzhou Lei; Pavlos Protopapas; Joy Parikh (CoRR, 2023)

Generating Images of the M87* Black Hole Using GANs. - Arya Mohan; Pavlos Protopapas; Keerthi Kunnumkai; Cecilia Garraffo; Lindy Blackburn; Koushik Chatterjee; Sheperd S. Doeleman; Razieh Emami; Christian M. Fromm; Yosuke Mizuno; Angelo Ricarte (CoRR, 2023)

Reservoir Computing for Solving Ordinary Differential Equations. - Marios Mattheakis; Hayden Joy; Pavlos Protopapas (Int. J. Artif. Intell. Tools, 2023)

IoT Malware Data Augmentation using a Generative Adversarial Network. - John Carter; Spiros Mancoridis; Pavlos Protopapas; Erick Galinkin (HICSS, 2024)

Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings. - John Carter; Spiros Mancoridis; Pavlos Protopapas; Erick Galinkin (HICSS, 2024)

Gravitational Duals from Equations of State. - Yago Bea; Raúl Jiménez; David Mateos; Shuheng Liu 001; Pavlos Protopapas; Pedro Tarancón-Álvarez; Pablo Tejerina-Pérez (CoRR, 2024)

Exact and approximate error bounds for physics-informed neural networks. - Augusto T. Chantada; Pavlos Protopapas; Luca Gomez Bachar; Susana J. Landau; Claudia G. Scóccola (CoRR, 2024)

Improved Uncertainty Quantification in Physics-Informed Neural Networks Using Error Bounds and Solution Bundles. - Pablo Flores; Olga Graf; Pavlos Protopapas; Karim Pichara (UAI, 2025)

Efficient PINNs: Multi-Head Unimodular Regularization of the Solutions Space. - Pedro Tarancón-Álvarez; Pablo Tejerina-Pérez; Raúl Jiménez; Pavlos Protopapas (CoRR, 2025)

Stiff Transfer Learning for Physics-Informed Neural Networks. - Emilien Seiler; Wanzhou Lei; Pavlos Protopapas (CoRR, 2025)

Astromer 2. - Cristobal Donoso-Oliva; Ignacio Becker; Pavlos Protopapas; Guillermo Cabrera-Vives; Martina Cádiz-Leyton; Daniel Moreno-Cartagena (CoRR, 2025)

Recent Advances of NeuroDiffEq - An Open-Source Library for Physics-Informed Neural Networks. - Shuheng Liu 001; Pavlos Protopapas; David Sondak; Feiyu Chen (CoRR, 2025)

Applying Vision Transformers on Spectral Analysis of Astronomical Objects. - Luis Felipe Strano Moraes; Ignacio Becker; Pavlos Protopapas; Guillermo Cabrera-Vives (CoRR, 2025)

Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer. - Daniel Moreno-Cartagena; Guillermo Cabrera-Vives; Alejandra Muñoz Arancibia; Pavlos Protopapas; Francisco Förster; Márcio Catelán; Amelia Bayo; Pablo A. Estévez; Paula Sánchez-Sáez; Franz E. Bauer; M. Pavez-Herrera; Lorena Hernández-García; et al. (CoRR, 2025)