Kalousis, Alexandros

39 publications

AAAI 2025 GLAD: Improving Latent Graph Generative Modeling with Simple Quantization Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis

ICLRW 2025 Hybrid Generative Modeling for Incomplete Physics: Deep Grey-Box Meets Optimal Transport Gurjeet Sangra Singh, Maciej Falkiewicz, Alexandros Kalousis

AISTATS 2025 MING: A Functional Approach to Learning Molecular Generative Models Van Khoa Nguyen, Maciej Falkiewicz, Giangiacomo Mercatali, Alexandros Kalousis

TMLR 2024 Discrete Graph Auto-Encoder Yoann Boget, Magda Gregorova, Alexandros Kalousis

ICMLW 2024 GLAD: Improving Latent Graph Generative Modeling with Simple Quantization Van Khoa Nguyen, Yoann Boget, Frantzeska Lavda, Alexandros Kalousis

ICML 2024 Mimicking Better by Matching the Approximate Action Distribution Joao Candido Ramos, Lionel Blondé, Naoya Takeishi, Alexandros Kalousis

NeurIPS 2023 Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability Maciej Falkiewicz, Naoya Takeishi, Imahn Shekhzadeh, Antoine Wehenkel, Arnaud Delaunoy, Gilles Louppe, Alexandros Kalousis

AISTATS 2023 Deep Grey-Box Modeling with Adaptive Data-Driven Models Toward Trustworthy Estimation of Theory-Driven Models Naoya Takeishi, Alexandros Kalousis

ACML 2022 Graph Annotation Generative Adversarial Networks Yoann Boget, Magda Gregorova, Alexandros Kalousis

MLJ 2022 Lipschitzness Is All You Need to Tame Off-Policy Generative Adversarial Imitation Learning Lionel Blondé, Pablo Strasser, Alexandros Kalousis

ECML-PKDD 2021 Conditional Neural Relational Inference for Interacting Systems Joao A. Candido Ramos, Lionel Blondé, Stéphane Armand, Alexandros Kalousis

ICLR 2021 Kanerva++: Extending the Kanerva Machine with Differentiable, Locally Block Allocated Latent Memory Jason Ramapuram, Yan Wu, Alexandros Kalousis

ICLRW 2021 Learned Transform Compression with Optimized Entropy Encoding Magda Gregorova, Marc Desaules, Alexandros Kalousis

NeurIPS 2021 Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling Naoya Takeishi, Alexandros Kalousis

NeurIPSW 2021 Variational Autoencoder with Differentiable Physics Engine for Human Gait Analysis and Synthesis Naoya Takeishi, Alexandros Kalousis

NeurIPS 2020 Goal-Directed Generation of Discrete Structures with Conditional Generative Models Amina Mollaysa, Brooks Paige, Alexandros Kalousis

ACML 2019 Learning to Augment with Feature Side-Information Amina Mollaysa, Alexandros Kalousis, Eric Bruno, Maurits Diephuis

AISTATS 2019 Sample-Efficient Imitation Learning via Generative Adversarial Nets Lionel Blondé, Alexandros Kalousis

ECML-PKDD 2018 Large-Scale Nonlinear Variable Selection via Kernel Random Features Magda Gregorová, Jason Ramapuram, Alexandros Kalousis, Stéphane Marchand-Maillet

UAI 2018 Structured Nonlinear Variable Selection Magda Gregorova, Alexandros Kalousis, Stéphane Marchand-Maillet

ECML-PKDD 2017 Forecasting and Granger Modelling with Non-Linear Dynamical Dependencies Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet

ACML 2017 Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks Magda Gregorová, Alexandros Kalousis, Stéphane Marchand-Maillet

ICML 2017 Regularising Non-Linear Models Using Feature Side-Information Amina Mollaysa, Pablo Strasser, Alexandros Kalousis

MLJ 2016 Factorizing LambdaMART for Cold Start Recommendations Phong Nguyen, Jun Wang, Alexandros Kalousis

ICML 2015 Information Geometry and Minimum Description Length Networks Ke Sun, Jun Wang, Alexandros Kalousis, Stephan Marchand-Maillet

NeurIPS 2015 Space-Time Local Embeddings Ke Sun, Jun Wang, Alexandros Kalousis, Stephane Marchand-Maillet

ICML 2014 Two-Stage Metric Learning Jun Wang, Ke Sun, Fei Sha, Stéphane Marchand-Maillet, Alexandros Kalousis

JAIR 2014 Using Meta-Mining to Support Data Mining Workflow Planning and Optimization P. Nguyen, Melanie Hilario, Alexandros Kalousis

ICML 2013 Convex Formulations of Radius-Margin Based Support Vector Machines Huyen Do, Alexandros Kalousis

AISTATS 2012 A Metric Learning Perspective of SVM: On the Relation of LMNN and SVM Huyen Do, Alexandros Kalousis, Jun Wang, Adam Woznica

ECML-PKDD 2012 Learning Neighborhoods for Metric Learning Jun Wang, Adam Woznica, Alexandros Kalousis

NeurIPS 2012 Parametric Local Metric Learning for Nearest Neighbor Classification Jun Wang, Alexandros Kalousis, Adam Woznica

NeurIPS 2011 Metric Learning with Multiple Kernels Jun Wang, Huyen T. Do, Adam Woznica, Alexandros Kalousis

ECML-PKDD 2009 Feature Weighting Using Margin and Radius Based Error Bound Optimization in SVMs Huyen Do, Alexandros Kalousis, Melanie Hilario

ECML-PKDD 2009 Margin and Radius Based Multiple Kernel Learning Huyen Do, Alexandros Kalousis, Adam Woznica, Melanie Hilario

ICML 2007 Learning to Combine Distances for Complex Representations Adam Woznica, Alexandros Kalousis, Melanie Hilario

MLJ 2004 On Data and Algorithms: Understanding Inductive Performance Alexandros Kalousis, João Gama, Melanie Hilario

ICML 2003 Representational Issues in Meta-Learning Alexandros Kalousis, Melanie Hilario

ECML-PKDD 2001 Estimating the Predictive Accuracy of a Classifier Hilan Bensusan, Alexandros Kalousis