Natasa Tagasovska

According to our database, Natasa Tagasovska authored at least 17 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design.

Natasa Tagasovska, Ji Won Park, Matthieu Kirchmeyer, Nathan C. Frey, Andrew Martin Watkins, Aya Abdelsalam Ismail, Arian Rokkum Jamasb, Edith Lee, Tyler Bryson, Stephen Ra, Kyunghyun Cho

CoRR, 2024

Uncertainty modeling for fine-tuned implicit functions.

Anna Susmelj, Mael Macuglia, Natasa Tagasovska, Reto Sutter, Sebastiano Caprara, Jean-Philippe Thiran, Ender Konukoglu

CoRR, 2024

Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient.

Natasa Tagasovska, Vladimir Gligorijevic, Kyunghyun Cho, Andreas Loukas

CoRR, 2024

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks.

Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty.

Michael Maser, Natasa Tagasovska, Jae Hyeon Lee, Andrew M. Watkins

CoRR, 2023

Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling.

Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev

Proceedings of the Conference on Causal Learning and Reasoning, 2023

Retrospective Uncertainties for Deep Models using Vine Copulas.

Natasa Tagasovska, Firat Ozdemir, Axel Brando

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences.

Natasa Tagasovska, Nathan C. Frey, Andreas Loukas, Isidro Hötzel, Julien Lafrance-Vanasse, Ryan Lewis Kelly, Yan Wu, Arvind Rajpal, Richard Bonneau, Kyunghyun Cho, Stephen Ra, Vladimir Gligorijevic

CoRR, 2022

Vision paper: causal inference for interpretable and robust machine learning in mobility analysis.

Yanan Xin, Natasa Tagasovska, Fernando Pérez-Cruz, Martin Raubal

Proceedings of the 30th International Conference on Advances in Geographic Information Systems, 2022

2021

Uncertainty Surrogates for Deep Learning.

Radhakrishna Achanta, Natasa Tagasovska

CoRR, 2021

2020

Deep Smoothing of the Implied Volatility Surface.

Damien Ackerer, Natasa Tagasovska, Thibault Vatter

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery.

Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Single-Model Uncertainties for Deep Learning.

Natasa Tagasovska, David Lopez-Paz

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders.

Natasa Tagasovska, Damien Ackerer, Thibault Vatter

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Generative Models for Simulating Mobility Trajectories.

Vaibhav Kulkarni, Natasa Tagasovska, Thibault Vatter, Benoît Garbinato

CoRR, 2018

Frequentist uncertainty estimates for deep learning.

Natasa Tagasovska, David Lopez-Paz

CoRR, 2018

2017

Distributed clustering of categorical data using the information bottleneck framework.

Natasa Tagasovska, Periklis Andritsos

Inf. Syst., 2017