Natasa Tagasovska

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 22

Top Topics

Causal Discovery

Categorical Data

Representational Power

Top Venues

Publications

Anna Susmelj, Mael Macuglia, Natasa Tagasovska, Reto Sutter, Sebastiano Caprara, Jean-Philippe Thiran, Ender Konukoglu
Uncertainty modeling for fine-tuned implicit functions. CoRR (2024)
Natasa Tagasovska, Vladimir Gligorijevic, Kyunghyun Cho, Andreas Loukas
Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient. CoRR (2024)
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
Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. CoRR (2024)
Natasa Tagasovska, Firat Ozdemir, Axel Brando
Retrospective Uncertainties for Deep Models using Vine Copulas. AISTATS (2023)
Michael Maser, Natasa Tagasovska, Jae Hyeon Lee, Andrew M. Watkins
MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty. CoRR (2023)
Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CLeaR (2023)
Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks. CoRR (2023)
Natasa Tagasovska, Firat Ozdemir, Axel Brando
Retrospective Uncertainties for Deep Models using Vine Copulas. CoRR (2023)
Yanan Xin, Natasa Tagasovska, Fernando Perez-Cruz, Martin Raubal
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis. CoRR (2022)
Yanan Xin, Natasa Tagasovska, Fernando Perez-Cruz, Martin Raubal
Vision paper: causal inference for interpretable and robust machine learning in mobility analysis. SIGSPATIAL/GIS (2022)
Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev
Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CoRR (2022)
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
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. CoRR (2022)
Radhakrishna Achanta, Natasa Tagasovska
Uncertainty Surrogates for Deep Learning. CoRR (2021)
Damien Ackerer, Natasa Tagasovska, Thibault Vatter
Deep Smoothing of the Implied Volatility Surface. NeurIPS (2020)
Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. ICML (2020)
Natasa Tagasovska, Damien Ackerer, Thibault Vatter
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. NeurIPS (2019)
Natasa Tagasovska, Damien Ackerer, Thibault Vatter
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. CoRR (2019)
Natasa Tagasovska, David Lopez-Paz
Single-Model Uncertainties for Deep Learning. NeurIPS (2019)
Damien Ackerer, Natasa Tagasovska, Thibault Vatter
Deep Smoothing of the Implied Volatility Surface. CoRR (2019)
Vaibhav Kulkarni, Natasa Tagasovska, Thibault Vatter, Benoît Garbinato
Generative Models for Simulating Mobility Trajectories. CoRR (2018)
Natasa Tagasovska, David Lopez-Paz
Frequentist uncertainty estimates for deep learning. CoRR (2018)
Natasa Tagasovska, Periklis Andritsos
Distributed clustering of categorical data using the information bottleneck framework. Inf. Syst. 72 (2017)