| On Disentangled Representations Learned from Correlated Data F Träuble, E Creager, N Kilbertus, F Locatello, A Dittadi, A Goyal, ... ICML 2021, 2021 | 67 | 2021 |
| On the Transfer of Disentangled Representations in Realistic Settings A Dittadi*, F Träuble*, F Locatello, M Wüthrich, V Agrawal, O Winther, ... ICLR 2021, 2021 | 54 | 2021 |
| Diffusion Models for Video Prediction and Infilling T Höppe, A Mehrjou, S Bauer, D Nielsen, A Dittadi TMLR, 2022 | 20 | 2022 |
| Assaying Out-Of-Distribution Generalization in Transfer Learning F Wenzel, A Dittadi, PV Gehler, CJ Simon-Gabriel, M Horn, D Zietlow, ... NeurIPS 2022, 2022 | 17 | 2022 |
| Generalization and Robustness Implications in Object-Centric Learning A Dittadi, S Papa, M De Vita, B Schölkopf, O Winther, F Locatello ICML 2022, 2022 | 16 | 2022 |
| Full-Body Motion From a Single Head-Mounted Device: Generating SMPL Poses From Partial Observations A Dittadi, S Dziadzio, D Cosker, B Lundell, TJ Cashman, J Shotton ICCV 2021, 2021 | 16 | 2021 |
| The Role of Pretrained Representations for the OOD Generalization of Reinforcement Learning Agents A Dittadi*, F Träuble*, M Wüthrich, F Widmaier, P Gehler, O Winther, ... ICLR 2022, 2022 | 13* | 2022 |
| Planning from Pixels in Atari with Learned Symbolic Representations A Dittadi, FK Drachmann, T Bolander AAAI 2021, 2021 | 12 | 2021 |
| Towards Hierarchical Discrete Variational Autoencoders V Liévin, A Dittadi, L Maaløe, O Winther 2nd Symposium on Advances in Approximate Bayesian Inference (AABI 2019), 2019 | 9 | 2019 |
| Image Super-Resolution With Deep Variational Autoencoders D Chira*, I Haralampiev*, O Winther, A Dittadi*, V Liévin* ECCV Workshop on Advances in Image Manipulation, 2022 | 5 | 2022 |
| Learning to Plan from Raw Data in Grid-based Games A Dittadi, T Bolander, O Winther GCAI 2018, 2018 | 5 | 2018 |
| Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds V Liévin, A Dittadi, A Christensen, O Winther NeurIPS 2020, 2020 | 4 | 2020 |
| Semi-Supervised Variational Autoencoder for Survival Prediction S Pálsson*, S Cerri*, A Dittadi*, K Van Leemput MICCAI Brain Lesion Workshop, 2019 | 4 | 2019 |
| Inductive Biases for Object-Centric Representations in the Presence of Complex Textures S Papa, O Winther, A Dittadi UAI Workshop on Causal Representation Learning, 2022 | 3* | 2022 |
| LAVAE: Disentangling Location and Appearance A Dittadi, O Winther NeurIPS Workshop on Perception as Generative Reasoning, 2019 | 3 | 2019 |
| Spreading and repetitions in satellite MAC protocols A Biason, A Dittadi, M Zorzi 2016 IEEE International Conference on Communications (ICC), 1-6, 2016 | 2 | 2016 |
| DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability C Eastwood*, AL Nicolicioiu*, J von Kügelgen*, A Kekić, F Träuble, ... ICLR 2023, 2023 | 1* | 2023 |
| Conditional Generation of Medical Time Series for Extrapolation to Underrepresented Populations S Bing, A Dittadi, S Bauer*, P Schwab* PLOS Digital Health, 2022 | 1 | 2022 |
| Boxhead: A Dataset for Learning Hierarchical Representations Y Chen, F Träuble, A Dittadi, S Bauer, B Schölkopf NeurIPS Workshop on Shared Visual Representations in Human and Machine …, 2021 | 1 | 2021 |
| Assessing Neural Network Robustness via Adversarial Pivotal Tuning PE Christensen, V Snæbjarnarson, A Dittadi, S Belongie, S Benaim arXiv preprint arXiv:2211.09782, 2022 | | 2022 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichVerified email at tuebingen.mpg.de
