Distributed clustering of categorical data using the information bottleneck framework. - Natasa Tagasovska; Periklis Andritsos (Inf. Syst., 2017)

Frequentist uncertainty estimates for deep learning. - Natasa Tagasovska; David Lopez-Paz (CoRR, 2018)

Generative Models for Simulating Mobility Trajectories. - Vaibhav Kulkarni; Natasa Tagasovska; Thibault Vatter; Benoît Garbinato (CoRR, 2018)

Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. - Natasa Tagasovska; Damien Ackerer; Thibault Vatter (NeurIPS, 2019)

Single-Model Uncertainties for Deep Learning. - Natasa Tagasovska; David Lopez-Paz (NeurIPS, 2019)

Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. - Natasa Tagasovska; Valérie Chavez-Demoulin; Thibault Vatter (ICML, 2020)

Deep Smoothing of the Implied Volatility Surface. - Damien Ackerer; Natasa Tagasovska; Thibault Vatter (NeurIPS, 2020)

Uncertainty Surrogates for Deep Learning. - Radhakrishna Achanta; Natasa Tagasovska (CoRR, 2021)

Vision paper: causal inference for interpretable and robust machine learning in mobility analysis. - Yanan Xin 001; Natasa Tagasovska; Fernando Pérez-Cruz; Martin Raubal (SIGSPATIAL/GIS, 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 027; Arvind Rajpal; Richard Bonneau; Kyunghyun Cho; Stephen Ra; Vladimir Gligorijevic (CoRR, 2022)

Retrospective Uncertainties for Deep Models using Vine Copulas. - Natasa Tagasovska; Firat Ozdemir; Axel Brando (AISTATS, 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 (CLeaR, 2023)

MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty. - Michael Maser; Natasa Tagasovska; Jae Hyeon Lee; Andrew M. Watkins (CoRR, 2023)

BOtied: Multi-objective Bayesian optimization with tied multivariate ranks. - Ji Won Park; Natasa Tagasovska; Michael Maser; Stephen Ra; Kyunghyun Cho (ICML, 2024)

Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient. - Natasa Tagasovska; Vladimir Gligorijevic; Kyunghyun Cho; Andreas Loukas (NeurIPS, 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 (ICLR, 2025)

Supervised Contrastive Block Disentanglement. - Taro Makino; Ji Won Park; Natasa Tagasovska; Takamasa Kudo; Paula Coelho; Jan-Christian Huetter; Heming Yao; Burkhard Hoeckendorf; Ana Carolina Leote; Stephen Ra; David Richmond; Kyunghyun Cho; et al. (CoRR, 2025)