Natasa Tagasovska

26 papers A* 7A 1Journal 16Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Supervised Contrastive Block Disentanglement. CoRR 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, Aviv Regev, Romain Lopez
2025	A*	`conf`	Uncertainty modeling for fine-tuned implicit functions. ICLR Anna Susmelj, Mael Macuglia, Natasa Tagasovska, Reto Sutter, Sebastiano Caprara, Jean-Philippe Thiran, Ender Konukoglu
2024	J	`jnl`	Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design. CoRR 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
2024	A*	`conf`	BOtied: Multi-objective Bayesian optimization with tied multivariate ranks. ICML Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
2024	A*	`conf`	Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient. NeurIPS Natasa Tagasovska, Vladimir Gligorijevic, Kyunghyun Cho, Andreas Loukas
2024	J	`jnl`	Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient. CoRR Natasa Tagasovska, Vladimir Gligorijevic, Kyunghyun Cho, Andreas Loukas
2024	J	`jnl`	Uncertainty modeling for fine-tuned implicit functions. CoRR Anna Susmelj, Mael Macuglia, Natasa Tagasovska, Reto Sutter, Sebastiano Caprara, Jean-Philippe Thiran, Ender Konukoglu
2023	J	`jnl`	BOtied: Multi-objective Bayesian optimization with tied multivariate ranks. CoRR Ji Won Park, Natasa Tagasovska, Michael Maser, Stephen Ra, Kyunghyun Cho
2023	—	`conf`	Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CLeaR Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev
2023	J	`jnl`	MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty. CoRR Michael Maser, Natasa Tagasovska, Jae Hyeon Lee, Andrew M. Watkins
2023	A	`conf`	Retrospective Uncertainties for Deep Models using Vine Copulas. AISTATS Natasa Tagasovska, Firat Ozdemir, Axel Brando
2023	J	`jnl`	Retrospective Uncertainties for Deep Models using Vine Copulas. CoRR Natasa Tagasovska, Firat Ozdemir, Axel Brando
2022	J	`jnl`	A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences. CoRR 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
2022	J	`jnl`	Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling. CoRR Romain Lopez, Natasa Tagasovska, Stephen Ra, Kyunghyun Cho, Jonathan K. Pritchard, Aviv Regev
2022	J	`jnl`	Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis. CoRR Yanan Xin, Natasa Tagasovska, Fernando Pérez-Cruz, Martin Raubal
2022	—	`conf`	Vision paper: causal inference for interpretable and robust machine learning in mobility analysis. SIGSPATIAL/GIS Yanan Xin, Natasa Tagasovska, Fernando Pérez-Cruz, Martin Raubal
2021	J	`jnl`	Uncertainty Surrogates for Deep Learning. CoRR Radhakrishna Achanta, Natasa Tagasovska
2020	A*	`conf`	Deep Smoothing of the Implied Volatility Surface. NeurIPS Damien Ackerer, Natasa Tagasovska, Thibault Vatter
2020	A*	`conf`	Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery. ICML Natasa Tagasovska, Valérie Chavez-Demoulin, Thibault Vatter
2019	A*	`conf`	Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. NeurIPS Natasa Tagasovska, Damien Ackerer, Thibault Vatter
2019	J	`jnl`	Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders. CoRR Natasa Tagasovska, Damien Ackerer, Thibault Vatter
2019	J	`jnl`	Deep Smoothing of the Implied Volatility Surface. CoRR Damien Ackerer, Natasa Tagasovska, Thibault Vatter
2019	A*	`conf`	Single-Model Uncertainties for Deep Learning. NeurIPS Natasa Tagasovska, David Lopez-Paz
2018	J	`jnl`	Frequentist uncertainty estimates for deep learning. CoRR Natasa Tagasovska, David Lopez-Paz
2018	J	`jnl`	Generative Models for Simulating Mobility Trajectories. CoRR Vaibhav Kulkarni, Natasa Tagasovska, Thibault Vatter, Benoît Garbinato
2017	J	`jnl`	Distributed clustering of categorical data using the information bottleneck framework. Inf. Syst. Natasa Tagasovska, Periklis Andritsos