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Safety- and Efficiency- aligned Learning Technical Report Coercing LLMs to do and reveal (almost) anything Geiping, J., Stein, A., Shu, M., Saifullah, K., Wen, Y., Goldstein, T. 2024 (Submitted) URL BibTeX
Empirical Inference Article Connectome-constrained networks predict neural activity across the fly visual system Lappalainen, J. K., Tschopp, F. D., Prakhya, S., McGill, M., Nern, A., Shinomiya, K., Takemura, S., Gruntman, E., Macke, J. H., Turaga, S. C. Nature, 634:1132–1140, 2024 (Published)
We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question10. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected—a universally observed feature of biological neural networks across species and brain regions.
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Learning and Dynamical Systems Article Controlling Participation in Federated Learning with Feedback Cummins, M., Er, G. D., Muehlebach, M. 2024 (Submitted) BibTeX
Empirical Inference Conference Paper DeViL: Decoding Vision features into Language Dani, M., Rio-Torto, I., Alaniz, S., Akata, Z. In Lecture Notes in Computer Science, vol 14264, 363–377, 45th Annual Conference of the German-Association-for-Pattern-Recognition (DAGM GCPR), 2024 (Published) DOI URL BibTeX
Human Aspects of Machine Learning Conference Paper Designing Ambiguity Sets for Distributionally Robust Optimization Using Structural Causal Optimal Transport Ehyaei, A. R., Farnadi, G., Samadi, S. In Proceedings of the AAAI Conference on Artificial Intelligence, 2024 (Published) BibTeX
Perceiving Systems Article Editor’s Note: Special Issue on Computer Vision Approach for Animal Tracking and Modeling Park, H. S., Rhodin, H., Kanazawa, A., Neverova, N., Nobuhara, S., Black, M., Zuffi, S. Journal of Computer Vision, 132, 2024 (Published) DOI URL BibTeX
Learning and Dynamical Systems Article Event-Based Federated Q-Learning Er, D., Muehlebach, M. Workshop on Foundations of RL and Control, International Conference on Machine Learning, 2024 (Published) BibTeX
Neural Capture and Synthesis Conference Paper FaceTalk: Audio-Driven Motion Diffusion for Neural Parametric Head Models Aneja, S., Thies, J., Dail, A., Niessner, M. In Proceedings 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21263-21273, IEEE, CVPR, 2024 (Published) DOI URL BibTeX
Safety- and Efficiency- aligned Learning Technical Report Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion Souri, H., Bansal, A., Kazemi, H., Fowl, L., Saha, A., Geiping, J., Wilson, A. G., Chellappa, R., Goldstein, T., Goldblum, M. 2024 (Submitted) URL BibTeX
Article Generating realistic neurophysiological time series with denoising diffusion probabilistic models Vetter, J., Macke, J. H., Gao, R. Patterns, 5(9), 2024 (Published) DOI URL BibTeX
Rationality Enhancement Article Identifying Resource-Rational Heuristics for Risky Choice Krueger, P., Callaway, F., Gul, S., Griffiths, T., Lieder, F. Psychological Review, 2024 (Published) DOI URL BibTeX
Physical Intelligence Article Individual and collective manipulation of multifunctional bimodal droplets in three dimensions Sun, M., Sun, B., Park, M., Yang, S., Wu, Y., Zhang, M., Kang, W., Yoon, J., Zhang, L., Sitti, M. Science Advances, 10(19):eadp1439, American Association for the Advancement of Science, 2024 (Published) BibTeX
Embodied Vision Ph.D. Thesis Investigating Shape Priors, Relationships, and Multi-Task Cues for Object-level Scene Understanding Elich, C. ETH Zürich, Zurich, 2024 (Published)
Humans are proficient at intuitively identifying objects and reasoning about their diverse properties from complex visual observations. Despite significant advances in artificial intelligence, computers have yet to achieve a comparable level of understanding, which is crucial for effective reasoning about tasks and interactions within an environment. In this thesis, we explore the benefits of various visual cues when dealing with key challenges in scene understanding, specifically focusing on weak supervision, finding view correspondence, and paradigms for simultaneously learning multiple tasks. We begin by investigating cues that reduce the need for full supervision. In particular, we propose an approach for learning multi-object 3D scene decomposition and object-wise properties from single images with only weak supervision. Our method utilizes a recurrent encoder to infer a latent representation for each object and a differentiable renderer to obtain a training signal. To guide the training process and constrain the search space of possible solutions, we leverage prior knowledge through pre-trained 3D shape spaces. Subsequently, we investigate the benefits of reasoning about relations between objects to learn more distinct object representations that allow for matching object detections across viewpoint changes. To address this, we introduce an approach that employs graph neural networks to learn matching features based on appearance as well as inter- and cross-frame relations. We conduct comparisons with keypoint-based methods and propose a methodology to combine these approaches, aiming to achieve overall improved performance. Finally, we consider the challenge of multi-task learning and analyze related paradigms in the context of basic single-task learning. In particular, we study the impact of the choice of optimizer, the role of gradient conflicts, and the effects on the transferability of features learned through either learning setup on common image corruptions. Our findings reveal surprising similarities between single-task and multi-task learning, suggesting that methods and techniques from one field could be advantageously applied to the other.
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Safety- and Efficiency- aligned Learning Conference Paper Investigating Style Similarity in Diffusion Models Somepalli, G., Gupta, A., Gupta, K., Palta, S., Goldblum, M., Geiping, J., Shrivastava, A., Goldstein, T. In European Conference on Computer Vision (ECCV 2024), LNCS, Springer Cham, 2024 (Published) URL BibTeX
Article LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry Chen, W., Chen, L., Wang, R., Pollefeys, M. Proceedings 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 19844-19853, 1, 1, YeaakEMl, 2024 (Accepted) DOI URL BibTeX
Safety- and Efficiency- aligned Learning Conference Paper LMD3: Language Model Data Density Dependence Kirchenbauer, J., Honke, G., Somepalli, G., Geiping, J., Lee, K., Ippolito, D., Goldstein, T., Andre, D. In Proceedings of the First Conference on Language Modeling, First Conference on Language Modeling, 2024 (Published) URL BibTeX
Empirical Inference Miscellaneous Language Model Alignment in Multilingual Trolley Problems Jin, Z., Levine, S., Kleiman-Weiner, M., Piatti, G., Liu, J., Gonzalez, F., Ortu, F., Strausz, A., Sachan, M., Mihalcea, R., Choi, Y., Schölkopf, B. 2024 (Published) URL BibTeX
Empirical Inference Article Low-power scalable multilayer optoelectronic neural networks enabled with incoherent light Song, A., Kottapalli, S. N. M., Goyal, R., Schoelkopf, B., Fischer, P. Nature Communications, 15:10692, 2024 (Published)
Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches. This study introduces a multilayer optoelectronic computing framework that alternates between optical and optoelectronic layers to implement matrix-vector multiplications and rectified linear functions, respectively. Our framework is designed for real-time, parallelized operations, leveraging 2D arrays of LEDs and photodetectors connected via independent analog electronics. We experimentally demonstrate this approach using a system with a three-layer network with two hidden layers and operate it to recognize images from the MNIST database with a recognition accuracy of 92% and classify classes from a nonlinear spiral data with 86% accuracy. By implementing multiple layers of a deep neural network simultaneously, our approach significantly reduces the number of read-ins and read-outs required and paves the way for scalable optical accelerators requiring ultra low energy.
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Embodied Vision Ph.D. Thesis Methods for Learning Adaptive and Symbolic Forward Models for Control and Planning Achterhold, J. M. Eberhard Karls Universität Tübingen, Tübingen, 2024 (Published)
Learning-based methods for sequential decision making, i.e., methods which leverage data, have shown the ability to solve complex problems in recent years. This includes control of dynamical systems, as well as mastering games such as Go and StarCraft. In addition, these methods often promise to be applicable to a wide variety of problems. A subclass of these methods are model-based methods. They leverage data to learn a model which allows predicting the evolution of a dynamical system to control. In recent research, it was shown that these methods, in contrast to model-free methods, require less data to be trained. In addition, model-based methods allow re-using the dynamics model when the task to be solved has changed, and straightforward adaptation to changes in the system’s dynamics. One particular focus of this thesis is on learning dynamics models which can data-efficiently adapt to changes in the system’s dynamics, as well as the efficient collection of data to adapt a learned model. In this regard, two novel methods are presented. In the application domain of autonomous robot navigation, in which both parameters of the robot and the terrain are subject to change, a novel method comprising an adaptive dynamics model is presented and evaluated on a simulated environment. A further advantage of model-based methods is the ability to incorporate physical prior knowledge for model design. In this thesis, we demonstrate that leveraging physical prior knowledge is advantageous for the task of tracking and predicting the motion of a table tennis ball, respecting its spin. However, model-based methods, in particular planning with learned models, have to cope with certain challenges. For long prediction horizons, which are required if the effect of an action is apparent only far in the future, model errors accumulate. In addition, model-based planning is commonly computationally intensive, which is problematic if high-frequency, reactive control is required. In this thesis, a method is presented to alleviate these problems. To this end, we propose a two-layered hierarchical method. Model-based planning is only applied on the higher layer on symbolic abstractions. On the lower-layer, model-free reactive control is used. We show successful application of this method to board games which can only be interacted with through a robotic manipulator, e.g., a robotic arm, which requires high-frequency reactive control.
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Physical Intelligence Article Micro- and nanofabrication of dynamic hydrogels with multichannel information Zhang, M., Lee, Y., Zheng, Z., Khan, M. T. A., Lyu, X., Byun, J., Giessen, H., Sitti, M. 2024 (Published) BibTeX
Empirical Inference Article Neonatal apnea and hypopnea prediction in infants with Robin sequence with neural additive models for time series Vetter, J., Lim, K., Dijkstra, T. M. H., Dargaville, P. A., Kohlbacher, O., Macke, J. H., Poets, C. F. PLOS Health Digital, 3(12):e0000678, 2024 (Accepted) DOI URL BibTeX
Safety- and Efficiency- aligned Learning Conference Paper Object Recognition as Next Token Prediction Yue, K., Chen, B., Geiping, J., Li, H., Goldstein, T., Lim, S. In IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), CVPR, 2024 (Published) DOI URL BibTeX
Safety- and Efficiency- aligned Learning Conference Paper On the Reliability of Watermarks for Large Language Models Kirchenbauer, J., Geiping, J., Wen, Y., Shu, M., Saifullah, K., Kong, K., Fernando, K., Saha, A., Goldblum, M., Goldstein, T. In The Twelfth International Conference on Learning Representations, ICLR 2024, The Twelfth International Conference on Learning Representations, 2024 (Published) URL BibTeX
Learning and Dynamical Systems Article Online Performance Optimization of Nonlinear Systems: A Gray-Box Approach He, Z., Muehlebach, M., Bolognani, S., Dörfler, F. Workshop on Foundations of RL and Control, International Conference on Machine Learning, 2024 (Published) BibTeX
Learning and Dynamical Systems Article Optimal Transport with Constraints: From Mirror Descent to Classical Mechanics Ibrahim, A. A., Muehlebach, M., De Bacco, C. Physical Review Letters, 133:057401 , 2024 (Published) DOI BibTeX
Physical Intelligence Article Perturbing Dynamics of Active Emulsions and Their Collectives Khan, M. T. A., Gardi, G., Soon, R. H., Zhang, M., Sitti, M. arXiv preprint arXiv:2405.05889, 2024 (Submitted) BibTeX
Safety- and Efficiency- aligned Learning Conference Paper Privacy Backdoors: Enhancing Membership Inference through Poisoning Pre-trained Models Wen, Y., Marchyok, L., Hong, S., Geiping, J., and Goldstein, T., Carlini, N. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, 2024 (Published) URL BibTeX
Article Reinforcement learning based autonomous multi-rotor landing on moving platforms Ahmad, P. G. &. A. 2024 (Published) BibTeX
Article Results from the autoPET challenge on fully automated lesion\nsegmentation in oncologic PET/CT imaging Gatidis, S., Frueh, M., Fabritius, M. P., Gu, S., Nikolaou, K., La Fougere, C., Ye, J., He, J., Peng, Y., Bi, L., Ma, J., Wang, B., Zhang, J., Huang, Y., Heiliger, L., Marinov, Z., Stiefelhagen, R., Egger, J., Kleesiek, J., Sibille, L., et al. Nature Machine Intelligence, 6(11):1396–1405, 2024 (Published)
Automated detection of tumour lesions on positron emission tomography–computed tomography (PET/CT) image data is a clinically relevant but highly challenging task. Progress in this field has been hampered in the past owing to the lack of publicly available annotated data and limited availability of platforms for inter-institutional collaboration. Here we describe the results of the autoPET challenge, a biomedical image analysis challenge aimed to motivate research in the field of automated PET/CT image analysis. The challenge task was the automated segmentation of metabolically active tumour lesions on whole-body 18F-fluorodeoxyglucose PET/CT. Challenge participants had access to a large publicly available annotated PET/CT dataset for algorithm training. All algorithms submitted to the final challenge phase were based on deep learning methods, mostly using three-dimensional U-Net architectures. Submitted algorithms were evaluated on a private test set composed of 150 PET/CT studies from two institutions. An ensemble model of the highest-ranking algorithms achieved favourable performance compared with individual algorithms. Algorithm performance was dependent on the quality and quantity of data and on algorithm design choices, such as tailored post-processing of predicted segmentations. Future iterations of this challenge will focus on generalization and clinical translation.
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Autonomous Learning Conference Paper SENSEI: Semantic Exploration Guided by Foundation Models to Learn Versatile World Models Sancaktar, C., Gumbsch, C., Zadaianchuk, A., Kolev, P., Martius, G. In The Training Agents with Foundation Models Workshop at RLC, 2024, indicates equal contribution (Published)
Exploring useful behavior is a keystone of reinforcement learning (RL). Existing approaches to intrinsic motivation, following general principles such as information gain, mostly uncover low-level interactions. In contrast, children’s play suggests that they engage in semantically meaningful high-level behavior by imitating or interacting with their caregivers. Recent work has focused on using foundation models to inject these semantic biases into exploration. However, these methods often rely on unrealistic assumptions, such as environments already embedded in language or access to high-level actions. To bridge this gap, we propose SEmaNtically Sensible ExploratIon (Sensei), a framework to equip model-based RL agents with intrinsic motivation for semantically meaningful behavior. To do so, we distill an intrinsic reward signal of interestingness from Vision Language Model (VLM) annotations. The agent learns to predict and maximize these intrinsic rewards using a world model learned directly from intrinsic rewards, image observations, and low-level actions. We show that in both robotic and video game-like simulations Sensei manages to discover a variety of meaningful behaviors. We believe Sensei provides a general tool for integrating feedback from foundation models into autonomous agents, a crucial research direction as openly available VLMs become more powerful.
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Physics for Inference and Optimization Article Similarity and economy of scale in urban transportation networks and\noptimal transport-based infrastructures Leite, D., De Bacco, C. Nature Communications, 15:7981, 2024 (Published) DOI URL BibTeX
Safety- and Efficiency- aligned Learning Conference Paper Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text Hans, A., Schwarzschild, A., Cherepanova, V., Kazemi, H., Saha, A., Goldblum, M., Geiping, J., Goldstein, T. In Proceedings of Machine Learning Research, Proceedings of the Forty-First International Conference on Machine Learning , Forty-First International Conference on Machine Learning , 2024 (Published) URL BibTeX
Conference Paper Terminating Differentiable Tree Experts Jonathan Thomm, M. H. G. C. A. T. B. S. &. A. R. In 2024 (Published) BibTeX
Learning and Dynamical Systems Article Toward a Systems Theory of Algorithms Doerfler, F., He, Z., Belgioioso, G., Bolognani, S., Lygeros, J., Muehlebach, M. IEEE CONTROL SYSTEMS LETTERS, 8:1198 - 1210, 2024 (Published) DOI URL BibTeX
Safety- and Efficiency- aligned Learning Conference Paper Transformers Can Do Arithmetic with the Right Embeddings McLeish, S. M., Bansal, A., Stein, A., Jain, N., Kirchenbauer, J., Bartoldson, B. R., Kailkhura, B., Bhatele, A., Geiping, J., Schwarzschild, A., Goldstein, T. In Proceedings of the Thirty-Eighth Annual Conference on Neural Information Processing Systems, Thirty-Eighth Annual Conference on Neural Information Processing Systems, 2024 (Published) URL BibTeX
Empirical Inference Conference Paper Waffling around for Performance: Visual Classification with Random Words and Broad Concepts Roth, K., Kim, J. M., Koepke, A. S., Vinyals, O., Schmid, C., Akata, Z. In Proceedings 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 15700-15711, 2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2023), 2024 (Published) DOI URL BibTeX
Human Aspects of Machine Learning Conference Paper Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness. Ehyaei, A. R., Farnadi, G., Samadi, S. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024 (Published) BibTeX
Perceiving Systems Conference Paper Emotional Speech-Driven Animation with Content-Emotion Disentanglement Daněček, R., Chhatre, K., Tripathi, S., Wen, Y., Black, M. J., Bolkart, T. In SIGGRAPH Asia 2023 Conference Papers, Association for Computing Machinery , New York, NY, SIGGRAPH Asia, December 2023 (Published)
To be widely adopted, 3D facial avatars must be animated easily, realistically, and directly from speech signals. While the best recent methods generate 3D animations that are synchronized with the input audio, they largely ignore the impact of emotions on facial expressions. Realistic facial animation requires lip-sync together with the natural expression of emotion. To that end, we propose EMOTE (Expressive Model Optimized for Talking with Emotion), which generates 3D talking-head avatars that maintain lip-sync from speech while enabling explicit control over the expression of emotion. To achieve this, we supervise EMOTE with decoupled losses for speech (i.e., lip-sync) and emotion. These losses are based on two key observations: (1) deformations of the face due to speech are spatially localized around the mouth and have high temporal frequency, whereas (2) facial expressions may deform the whole face and occur over longer intervals. Thus, we train EMOTE with a per-frame lip-reading loss to preserve the speech-dependent content, while supervising emotion at the sequence level. Furthermore, we employ a content-emotion exchange mechanism in order to supervise different emotions on the same audio, while maintaining the lip motion synchronized with the speech. To employ deep perceptual losses without getting undesirable artifacts, we devise a motion prior in the form of a temporal VAE. Due to the absence of high-quality aligned emotional 3D face datasets with speech, EMOTE is trained with 3D pseudo-ground-truth extracted from an emotional video dataset (i.e., MEAD). Extensive qualitative and perceptual evaluations demonstrate that EMOTE produces speech-driven facial animations with better lip-sync than state-of-the-art methods trained on the same data, while offering additional, high-quality emotional control.
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Autonomous Learning Conference Paper On Imitation in Mean-field Games Ramponi, G., Kolev, P., Olivier, P., He, N., Laurière, M., Geist, M. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 1-12, Curran Associates Inc., NeurIPS, December 2023 (Published)
We explore the problem of imitation learning (IL) in the context of mean-field games (MFGs), where the goal is to imitate the behavior of a population of agents following a Nash equilibrium policy according to some unknown payoff function. IL in MFGs presents new challenges compared to single-agent IL, particularly when both the reward function and the transition kernel depend on the population distribution. In this paper, departing from the existing literature on IL for MFGs, we introduce a new solution concept called the Nash imitation gap. Then we show that when only the reward depends on the population distribution, IL in MFGs can be reduced to single-agent IL with similar guarantees. However, when the dynamics is population-dependent, we provide a novel upper-bound that suggests IL is harder in this setting. To address this issue, we propose a new adversarial formulation where the reinforcement learning problem is replaced by a mean-field control (MFC) problem, suggesting progress in IL within MFGs may have to build upon MFC.
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Empirical Inference Conference Paper Optimistic Active Exploration of Dynamical Systems Sukhija, B., Treven, L., Sancaktar, C., Blaes, S., Coros, S., Krause, A. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 1-32, Curran Associates Inc. , NeurIPS, December 2023 (Published) DOI URL BibTeX
Empirical Inference Article Machine-Learning-Aided Prediction of Brain Metastases Development in Non-Small-Cell Lung Cancers Visonà, G., Spiller, L. M., Hahn, S., Hattingen, E., Vogl, T. J., Schweikert, G., Bankov, K., Demes, M., Reis, H., Wild, P., Zeiner, P. S., Acker, F., Sebastian, M., Wenger, K. J. Clinical lung cancer, 24(8):e311-e322, December 2023 (Published) DOI BibTeX
Empirical Inference Conference Paper A Measure-Theoretic Axiomatisation of Causality Park, J., Buchholz, S., Schölkopf, B., Muandet, K. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:28510-28540, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX
Empirical Inference Conference Paper Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good Gonzalez*, F., Jin*, Z., Schölkopf, B., Hope, T., Sachan, M., Mihalcea, R. Findings of the Association for Computational Linguistics: EMNLP 2023, 415-438, (Editors: Houda Bouamor and Juan Pino and Kalika Bali), Association for Computational Linguistics, December 2023, *equal contribution (Published) DOI BibTeX
Physics for Inference and Optimization Article Bilevel Optimization for Traffic Mitigation in Optimal Transport Networks Lonardi, A., De Bacco, C. Physical Review Letter, 131:267401, December 2023 (Published) Preprint Code Paper DOI URL BibTeX
Empirical Inference Conference Paper CLadder: Assessing Causal Reasoning in Language Models Jin*, Z., Chen*, Y., Leeb*, F., Gresele*, L., Kamal, O., Lyu, Z., Blin, K., Gonzalez, F., Kleiman-Weiner, M., Sachan, M., Schölkopf, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:31038-31065, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *main contributors (Published) URL BibTeX
Empirical Inference Conference Paper Can semi-supervised learning use all the data effectively? A lower bound perspective Tifrea*, A., Yüce*, G., Sanyal, A., Yang, F. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:21960-21982, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Causal Component Analysis Liang, W., Kekić, A., von Kügelgen, J., Buchholz, S., Besserve, M., Gresele*, L., Schölkopf*, B. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:32481-32520, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *shared last author (Published) URL BibTeX
Empirical Inference Conference Paper Causal Modeling with Stationary Diffusions Lorch, L., Krause*, A., Schölkopf*, B. Causal Representation Learning Workshop at NeurIPS 2023, December 2023, *equal supervision (Published) URL BibTeX
Empirical Inference Conference Paper Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Data Guo*, S., Tóth*, V., Schölkopf, B., Huszár, F. Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:36463-36475, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023, *equal contribution (Published) URL BibTeX
Empirical Inference Conference Paper Causal normalizing flows: from theory to practice Javaloy, A., Sanchez-Martin, P., Valera, I. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 36:58833-58864, (Editors: A. Oh and T. Neumann and A. Globerson and K. Saenko and M. Hardt and S. Levine), Curran Associates, Inc., 37th Annual Conference on Neural Information Processing Systems, December 2023 (Published) URL BibTeX