| Single-model uncertainties for deep learning N Tagasovska, D Lopez-Paz NeurIPS 2019, 2019 | 328 | 2019 |
| Deep Smoothing of the Implied Volatility Surface D Ackerer, N Tagasovska, T Vatter NeurIPS 2020, 2020 | 54 | 2020 |
| Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders N Tagasovska, D Ackerer, T Vatter NeurIPS 2019, 2019 | 44 | 2019 |
| Distinguishing Cause from Effect Using Quantiles: Bivariate Quantile Causal Discovery N Tagasovska, V Chavez-Demoulin, T Vatter ICML 2020, 2020 | 43* | 2020 |
| Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling R Lopez*, N Tagasovska*, S Ra, K Cho, J Pritchard, A Regev 2nd Conference on Causal Learning and Reasoning (CLeaR), 2022 | 40 | 2022 |
| Generative Models for Simulating Mobility Trajectories V Kulkarni, N Tagasovska, T Vatter, B Garbinato Workshop on Modeling and Decision-Making in the Spatiotemporal Domain, 32nd …, 2018 | 39 | 2018 |
| Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis Y Xin, N Tagasovska, F Perez-Cruz, M Raubal ACM SIGSPATIAL 2022, 2022 | 13 | 2022 |
| A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences N Tagasovska, NC Frey, A Loukas, I Hötzel, J Lafrance-Vanasse, RL Kelly, ... NeurIPS 2022 AI for Science workshop, 2022 | 10 | 2022 |
| Distributed clustering of categorical data using the information bottleneck framework N Tagasovska, P Andritsos Information Systems 72, 161-178, 2017 | 10 | 2017 |
| Bimodal feature-based fusion for real-time emotion recognition in a mobile context S Gievska, K Koroveshovski, N Tagasovska 2015 International Conference on Affective Computing and Intelligent …, 2015 | 10 | 2015 |
| Antibody DomainBed: Out-of-Distribution Generalization in Therapeutic Protein Design N Tagasovska, JW Park, M Kirchmeyer, NC Frey, AM Watkins, AA Ismail, ... arXiv preprint arXiv:2407.21028, 2024 | 5 | 2024 |
| Efficiency comparison of DFT/IDFT algorithms by evaluating diverse hardware implementations, parallelization prospects and possible improvements D Efnusheva, N Tagasovska, A Tentov, M Kalendar Proc. Second International Conference on Applied Innovations in IT, Germany, 2014 | 5 | 2014 |
| Uncertainty modeling for fine-tuned implicit functions A Susmelj, M Macuglia, N Tagasovska, R Sutter, S Caprara, JP Thiran, ... arXiv preprint arXiv:2406.12082, 2024 | 2 | 2024 |
| Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient N Tagasovska, V Gligorijević, K Cho, A Loukas NeurIPS 2024, 2024 | 2 | 2024 |
| BOtied: Multi-objective Bayesian optimization with tied multivariate ranks JW Park*, N Tagasovska*, M Maser, S Ra, K Cho ICML 2024, 2024 | 2 | 2024 |
| Retrospective Uncertainties for Deep Models using Vine Copulas N Tagasovska, F Ozdemir, A Brando AISTATS 2023, 2023 | 2 | 2023 |
| Performances of LEON3 IP Core in WiGig Environment on Receiving Side N Tagasovska, P Grnarova, A Tentov, D Efnusheva New Trends in Networking, Computing, E-learning, Systems Sciences, and …, 2015 | 1 | 2015 |
| An Efficient 64-Point IFFT Hardware Module Design D Efnusheva, A Tentov, N Tagasovska New Trends in Networking, Computing, E-learning, Systems Sciences, and …, 2015 | 1 | 2015 |
| MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models M Maser, N Tagasovska, JH Lee, AM Watkins NeurIPS 2023 AI for Science Workshop, 0 | 1 | |
| COUNTERFACTUAL GENERATION OF MOLECULAR CONFORMATIONS N Tagasovska, MR Maser US Patent App. 18/680,089, 2024 | | 2024 |
