Publikationen

Hier finden Sie eine Auswahl der WhiteBox-Publikationen

  • Thomas, T., Straub, D., Tatai, F., Shene, M., Tosik, T., Kersting, K., & Rothkopf, C. (2024). Modeling dataset bias in machine-learned theories of economic decision making. pdf (external)
    Nature Human Behaviour.
  • Zečević, M., Dhami, D.S., & Kersting, K. (2024). Structural Causal Models Reveal Confounder Bias in Linear Program Modelling. pdf (wird in neuem Tab geöffnet) (external)
    Machine Learning Journal (MLJ).
  • Brack, M., Schramowski, P., Deiseroth, B., & Kersting, K. (2023). ILLUME: Rationalizing Vision-Language Models through Human Interactions. pdf (wird in neuem Tab geöffnet) (external)
    In Proceedings of the 40th International Conference on Machine Learning (ICML).
  • Dill, S., Li, S.Z., Rohr, M., Sharbafi, M., & Antink, C.H. (2023). Automatic Generation of Labeled Data for Video-Based Human Pose Analysis via NLP applied to YouTube Subtitles. pdf (external)
    45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), doi: 10.1109/EMBC40787.2023.10340044.
  • Friedrich, F., Stammer, W., Schramowski, P., Kersting, K. (2023). A typology for exploring the mitigation of shortcut behaviour. pdf (external)
    Nature Machine Intelligence 5:319-330.
  • Hesse, R., Schaub-Meyer, S., & Roth, S (2023). Content-Adaptive Downsampling in Convolutional Neural Networks. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, pp. 4544-4553
  • Kadner, F., Thomas, T., Hoppe, D., Rothkopf, C. A. (2023). Improving saliency models’ predictions of the next fixation with humans’ intrinsic cost of gaze shifts. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
  • Scholl, P., Firouzi, V., Karimi, M.T., Seyfarth, A., Sharbafi, M.A.(2023). Virtual Pivot Point Model Predicts Instability in Parkinsonian Gaits. pdf (external)
    2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 5261-5266, doi: 10.1109/SMC53992.2023.10394155.
  • Schramowski, P., Brack, M., Deiseroth, B., & Kersting, K. (2023). Safe Latent Diffusion: Mitigating Inappropriate Degeneration in Diffusion Models. pdf (wird in neuem Tab geöffnet) (external)
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  • Schultze, S., Withöft, A., Abdenebaoui, L., & Boll, S. (2023). Explaining Image Aesthetics Assessment: An Interactive Approach. pdf (external)
    In Proceedings of the 2023 ACM International Conference on Multimedia Retrieval (ICMR '23).
  • Sidheekh, S., Kersting, K., & Natarajan, S. (2023). Probabilistic Flow Circuits: Towards Unified Deep Models for Tractable Probabilistic Inference. pdf (wird in neuem Tab geöffnet) (external)
    In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI).
  • Skryagin, A., Ochs, D., Dhami, D.S., & Kersting, K. (2023). Scalable Neural-Probabilistic Answer Set Programming. pdf (external)
    Journal of Artificial Intelligence Research (JAIR) 78:579–617.
  • Straub, D., Schultheis, M., Koeppl, H., & Rothkopf, C. A. (2023). Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs. pdf (wird in neuem Tab geöffnet) (external)
    In Advances in Neural Information Processing Systems (NeurIPS 2023), 36.
  • Ventola, F., Braun, S., Yu, Z., Mundt, M., & Kersting, K. (2023). Probabilistic Circuits That Know What They Don't Know. pdf (wird in neuem Tab geöffnet) (external)
    In Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI).
  • Willig, M., Zečević, M., Dhami, D.S., & Kersting, K. (2023). Do Not Marginalize Mechanisms, Rather Consolidate!. pdf (external)
    In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS).
  • Yu, Z., Trapp, M., & Kersting, K. (2023). Characteristic Circuits. pdf (external)
    In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS).
  • Zečević, M., Dhami, D.S., Kersting, K. (2023). Not All Causal Inference is the Same. pdf (external)
    Transactions on Machine Learning Research (TMLR).
  • Zečević, M., Willig, M., Dhami, D.S., & Kersting, K. (2023). Causal Parrots. Large Language Models May Talk Causality But Are Not Causal. pdf (external)
    Transactions on Machine Learning Research (TMLR).
  • Firouzi, V., Mohseni, O., Sharbafi, MA. (2022). Model-based Control for Gait Assistance in the Frontal Plane.
    2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)
  • Mohseni, O., Schmidt, P., Seyfarth, A., Sharbafi, MA. (2022). Unified GRF-based control for adjusting hopping frequency with various robot configurations.
    In: Journal of Advanced Robotics, Volume 36, 2022 – Issue 13, Special Issue on Adaptive Motion of Animals and Machines.
  • Ploeger, K., Peters, J. (2022). Controlling the cascade: Kinematic planning for n-ball toss juggling. pdf (wird in neuem Tab geöffnet) (external)
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))
  • Schramowski, P., Turan, C., Andersen, N., Rothkopf, C. A., & Kersting, K. (2022). Large pre-trained language models contain human-like biases of what is right and wrong to do. pdf (wird in neuem Tab geöffnet) (external)
    Nature Machine Intelligence, 4(3), 258-268
  • Schultheis, M., Rothkopf, C. A., & Koeppl, H. (2022). Reinforcement Learning with Non-Exponential Discounting. pdf (wird in neuem Tab geöffnet) (external)
    Advances in Neural Information Processing Systems, 35, 3649-3662.
  • Skryagin, A., Stammer, W., Ochs, D., Singh Dhami, D., Kersting, K. (2022). Neural-Probabilistic Answer Set Programming. pdf (wird in neuem Tab geöffnet) (external)
    In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning (KR).
  • Stammer, W., Memmel, M., Schramowski, P., Kersting, K. (2022). Interactive Disentanglement: Learning Concepts by Interacting with their Prototype Representations. pdf (wird in neuem Tab geöffnet) (external)
    In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Straub, D., Schultheis, M., & Rothkopf, C. A. (2022). Inferring implicit sensorimotor costs by inverse optimal control with signal dependent noise. pdf (wird in neuem Tab geöffnet) (external)
    Computational and Systems Neuroscience (COSYNE)
  • Trick, S., Rothkopf, S.A. (2022). Bayesian classifier fusion with an explicit model of correlation. pdf (wird in neuem Tab geöffnet) (external)
    International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 151:2282-2310, 2022
  • Yu, Z., Ventola, F., Thoma, N., Singh Dhami, D., Mundt, M., Kersting, K. (2022): Predictive Whittle Networks for Time Series. pdf (external)
    In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 180:2320-2330.
  • Schultheis, M., Straub, D., Rothkopf, C.A. (2021).
    Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System. pdf (wird in neuem Tab geöffnet) (external)
    Pre-proceedings of Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
  • Stammer, W., Schramowski, P., & Kristian Kersting. (2021).
    Right for the Right Concept: Revising Neuro-Symbolic Concepts by Interacting with their Explanations. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
  • Ventola, F., Dhami, D. S., & Kristian Kersting. (2021).
    Generative Clausal Networks: Relational Decision Trees as Probabilistic Circuits. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of the 30th International Conference on Inductive Logic Programming (ILP).
  • Yu, Z., Ventola, F., & Kristian Kersting. (2021).
    Whittle Networks: A Deep Likelihood Model for Time Series. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of the 38th International Conference on Machine Learning (ICML) , 12177–12186.
  • Yu, Z., Zhu, M., Trapp, M., Skryagin, A., & Kristian Kersting. (2021).
    Leveraging Probabilistic Circuits for Nonparametric Multi-Output Regression. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence (UAI).
  • Zečević, M., Dhami, D. S., Karanam, A., Natarajan, S., & Kristian Kersting. (2021).
    Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models. pdf (wird in neuem Tab geöffnet) (external)
    Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS 2021).