Publications

Empirical Inference Conference Paper From Causal to Concept-Based Representation Learning Rajendran*, G., Buchholz*, S., Aragam, B., Schölkopf, B., Ravikumar, P. K. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:101250-101296, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Learning Partitions from Context Buchholz, S. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:140066-140112, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving Didolkar, A. R., Goyal, A., Ke, N. R., Guo, S., Valko, M., Lillicrap, T. P., Rezende, D. J., Bengio, Y., Mozer, M. C., Arora, S. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:19783-19812, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper A Generative Model of Symmetry Transformations Allingham, J. U., Mlodozeniec, B. K., Padhy, S., Antorán, J., Krueger, D., Turner, R. E., Nalisnick, E., Hernández-Lobato, J. M. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:91091-91130, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Article A Randomized Controlled Trial on Anonymizing Reviewers to Each Other in Peer Review Discussions Rastogi, C., Song, X., Jin, Z., Stelmakh, I., Daumé III, H., Zhang, K., Shah, N. B. PLOS ONE, 19(12), Public Library of Science, December 2024 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art Hernandez, A., Brinkmann, L., Serna, I., Rahaman, N., Alhaija, H. A., Yakura, H., Sola, M. C., Schölkopf, B., Rahwan, I. NeurIPS 2024 Workshop on Creativity and Generative AI, December 2024 (Published) arXiv BibTeX

Empirical Inference Conference Paper Causal vs. Anticausal merging of predictors Garrido Mejia, S., Blöbaum, P., Schölkopf, B., Janzing, D. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) , 37:1402-1427, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Ph.D. Thesis Causality for Natural Language Processing Jin, Z. University of Tübingen, Germany, December 2024, (ELLIS PhD student program) (Published) URL BibTeX

Empirical Inference Conference Paper Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias Chen*, Y., Vethavikashini*, C. R., Mattern*, J., Mihalcea, R., Jin, Z. NeurIPS 2024 Workshop on Causality and Language Models (CaLM), December 2024, *equal contribution (Published) DOI URL BibTeX

Empirical Inference Conference Paper Cooperate or Collapse: Emergence of Sustainability in a Society of LLM Agents Piatti*, G., Jin*, Z., Kleiman-Weiner*, M., Schölkopf, B., Sachan, M., Mihalcea, R. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:111715-111759, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Do Finetti: On Causal Effects for Exchangeable Data Guo, S., Zhang, C., Muhan, K., Huszár*, F., Schölkopf*, B. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:127317-127345, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024, *equal supervision (Published) URL BibTeX

Empirical Inference Conference Paper Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes Lin, J. A., Padhy, S., Mlodozeniec, B. K., Antorán, J., Hernández-Lobato, J. M. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) , 37:15460-15496, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Inferring stochastic low-rank recurrent neural networks from neural data Pals, M., Sağtekin, A. E., Pei, F., Gloeckler, M., Macke, J. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) , 37:18225-18264, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Latent Diffusion for Neural Spiking Data Kapoor, J., Schulz, A., Vetter, J., Pei, F., Gao, R., Macke, J. H. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:118119-118154, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Limits of Transformer Language Models on Learning to Compose Algorithms Thomm, J., Camposampiero, G., Terzic, A., Hersche, M., Schölkopf, B., Rahimi, A. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:7631-7674, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks Chen, W., Ge, H. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) , 37:97562-97586, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper On Affine Homotopy between Language Encoders Chan, R., Bourmasmoud, R., Svete, A., Ren, Y., Guo, Q., Jin, Z., Ravfogel, S., Sachan, M., Schölkopf, B., El-Assady, M., Cotterell, R. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:73337-73365, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Shaving Weights with Occam’s Razor: Bayesian Sparsification for Neural Networks using the Marginal Likelihood Dhahri, R., Immer, A., Charpentier, B., Günnemann, S., Fortuin, V. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:24959-24989, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation Vetter, J., Moss, G., Schröder, C., Gao, R., Macke, J. H. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:88772-88806, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Theoretical Characterisation of the Gauss Newton Conditioning in Neural Networks Zhao*, J., Singh*, S. P., Lucchi, A. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:114965-115000, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper What Makes and Breaks Safety Fine-tuning? A Mechanistic Study Jain, S., Lubana, E. S., Oksuz, K., Joy, T., Torr, P., Sanyal, A., Dokania, P. K. Advances in Neural Information Processing Systems 37 (NeurIPS 2024), 37:93406-93478, (Editors: A. Globerson and L. Mackey and D. Belgrave and A. Fan and U. Paquet and J. Tomczak and C. Zhang), Curran Associates, Inc., 38th Annual Conference on Neural Information Processing Systems, December 2024 (Published) URL BibTeX

Haptic Intelligence Autonomous Learning Empirical Inference Miscellaneous Demonstration: Minsight - A Soft Vision-Based Tactile Sensor for Robotic Fingertips Andrussow, I., Sun, H., Martius, G., Kuchenbecker, K. J. Hands-on demonstration presented at the Conference on Robot Learning (CoRL), Munich, Germany, November 2024 (Published)

Abstract ›

Beyond vision and hearing, tactile sensing enhances a robot's ability to dexterously manipulate unfamiliar objects and safely interact with humans. Giving touch sensitivity to robots requires compact, robust, affordable, and efficient hardware designs, especially for high-resolution tactile sensing. We present a soft vision-based tactile sensor engineered to meet these requirements. Comparable in size to a human fingertip, Minsight uses machine learning to output high-resolution directional contact force distributions at 60 Hz. Minsight's tactile force maps enable precise sensing of fingertip contacts, which we use in this hands-on demonstration to allow a 3-DoF robot arm to physically track contact with a user's finger. While observing the colorful image captured by Minsight's internal camera, attendees can experience how its ability to detect delicate touches in all directions facilitates real-time robot interaction.

BibTeX

Empirical Inference Conference Paper Diffusion-based learning of contact plans for agile locomotion Dh’Edin, V., Ravi, A. K. C., Jordana, A., Zhu, H., Meduri, A., Righetti, L., Schölkopf, B., Khadiv, M. IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids), 637-644, IEEE, November 2024 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Do LLMs Think Fast and Slow? A Causal Study on Sentiment Analysis Lyu*, Z., Jin*, Z., Gonzalez, F., Mihalcea, R., Schölkopf, B., Sachan, M. Findings of the Association for Computational Linguistics: EMNLP, 9353-9372, (Editors: Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen), Association for Computational Linguistics, November 2024, *equal contribution (Published) DOI URL BibTeX

Empirical Inference Conference Paper Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis Jenny*, D. F., Billeter*, Y., Schölkopf, B., Jin, Z. Proceedings of the Third Workshop on NLP for Positive Impact, 152-178, (Editors: Dementieva, Daryna and Ignat, Oana and Jin, Zhijing and Mihalcea, Rada and Piatti, Giorgio and Tetreault, Joel and Wilson, Steven and Zhao, Jieyu), Association for Computational Linguistics, November 2024, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Implicit Personalization in Language Models: A Systematic Study Jin, Z., Heil, N., Liu, J., Dhuliawala, S., Qi, Y., Schölkopf, B., Mihalcea, R., Sachan, M. Findings of the Association for Computational Linguistics: EMNLP, 12309-12325, (Editors: Yaser Al-Onaizan and Mohit Bansal and Yun-Nung Chen), Association for Computational Linguistics, November 2024 (Published) DOI URL BibTeX

Empirical Inference Ph.D. Thesis On Principled Modeling of Inductive Bias in Machine Learning Liu, W. University of Cambridge, UK, Cambridge, November 2024, (Cambridge-Tübingen-Fellowship-Program, ELLIS PhD student program) (Published) BibTeX

Empirical Inference Conference Paper The Odyssey of Commonsense Causality: From Foundational Benchmarks to Cutting-Edge Reasoning Cui, S., Jin, Z., Schölkopf, B., Faltings, B. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), 16722-16763, (Editors: Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung), Association for Computational Linguistics, November 2024 (Published) URL BibTeX

Empirical Inference Conference Paper RP1M: A Large-Scale Motion Dataset for Piano Playing with Bi-Manual Dexterous Robot Hands Zhao*, Y., Chen*, L., Schneider, J., Gao, Q., Kannala, J., Schölkopf, B., Pajarinen, J., Büchler, D. Proceedings of the 8th Annual Conference on Robot Learning (CoRL), 270:5184-5203, Proceedings of Machine Learning Research, (Editors: Agrawal, Pulkit and Kroemer, Oliver and Burgard, Wolfram), PMLR, Conference on Robot Learning, November 2024, *equal contribution (Published) URL BibTeX

Empirical Inference Article A Probabilistic Model behind Self-Supervised Learning Bizeul, A., Schölkopf, B., Allen, C. Transactions on Machine Learning Research, October 2024 (Published) PDF URL BibTeX

Empirical Inference Article How developments in natural language processing help us in understanding human behaviour Mihalcea, R., Biester, L., Boyd, R. L., Jin, Z., Perez-Rosas, V., Wilson, S., Pennebaker, J. W. Nature Human Behaviour, 8(10):1877-1889, Nature Publishing Group UK London, October 2024 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Redesigning Information Markets in the Era of Language Models Weiss, M., Rahaman, N., Wüthrich, M., Bengio, Y., Li, L. E., Schölkopf, B., Pal, C. First Conference on Language Modeling (COLM), arXiv:2403.14443, October 2024 (Published)

Abstract ›

This work addresses the buyer's inspection paradox for information markets. The paradox is that buyers need to access information to determine its value, while sellers need to limit access to prevent theft. To study this, we introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants. The central mechanism enabling this marketplace is the agents' dual capabilities: they not only have the capacity to assess the quality of privileged information but also come equipped with the ability to forget. This ability to induce amnesia allows vendors to grant temporary access to proprietary information, significantly reducing the risk of unauthorized retention while enabling agents to accurately gauge the information's relevance to specific queries or tasks. To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information. Concretely, our experiments (a) uncover biases in language models leading to irrational behavior and evaluate techniques to mitigate these biases, (b) investigate how price affects demand in the context of informational goods, and (c) show that inspection and higher budgets both lead to higher quality outcomes.

arXiv URL BibTeX

Empirical Inference Conference Paper GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs Gao, G., Liu, W., Chen, A., Geiger, A., Schölkopf, B. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21295-21304, IEEE, Piscataway, NJ, CVPR, September 2024 (Published) DOI URL BibTeX

Empirical Inference Ph.D. Thesis Advances in Probabilistic Methods for Deep Learning Immer, A. ETH Zurich, Switzerland, September 2024, CLS PhD Program (Published) BibTeX

Empirical Inference Master Thesis Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art Hernandez, A. Technical University of Munich, Germany, September 2024 (Published) BibTeX

Haptic Intelligence Empirical Inference Optics and Sensing Laboratory Software Workshop Article Fiber-Optic Shape Sensing Using Neural Networks Operating on Multispecklegrams Cao, C. G. L., Javot, B., Bhattarai, S., Bierig, K., Oreshnikov, I., Volchkov, V. V. IEEE Sensors Journal, 24(17):27532-27540, September 2024 (Published)

Abstract ›

Application of machine learning techniques on fiber speckle images to infer fiber deformation allows the use of an unmodified multimode fiber to act as a shape sensor. This approach eliminates the need for complex fiber design or construction (e.g., Bragg gratings and time-of-flight). Prior work in shape determination using neural networks trained on a finite number of possible fiber shapes (formulated as a classification task), or trained on a few continuous degrees of freedom, has been limited to reconstruction of fiber shapes only one bend at a time. Furthermore, generalization to shapes that were not used in training is challenging. Our innovative approach improves generalization capabilities, using computer vision-assisted parameterization of the actual fiber shape to provide a ground truth, and multiple specklegrams per fiber shape obtained by controlling the input field. Results from experimenting with several neural network architectures, shape parameterization, number of inputs, and specklegram resolution show that fiber shapes with multiple bends can be accurately predicted. Our approach is able to generalize to new shapes that were not in the training set. This approach of end-to-end training on parameterized ground truth opens new avenues for fiber-optic sensor applications. We publish the datasets used for training and validation, as well as an out-of-distribution (OOD) test set, and encourage interested readers to access these datasets for their own model development.

DOI BibTeX

Empirical Inference Autonomous Learning Conference Paper Learning to Control Emulated Muscles in Real Robots: A Software Test Bed for Bio-Inspired Actuators in Hardware Schumacher, P., Krause, L., Schneider, J., Büchler, D., Martius, G., Haeufle, D. In Proceedings 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob), 806-813, IEEE, 10th International Conference on Biomedical Robotics and Biomechatronics (BioRob), September 2024 (Published) arXiv DOI URL BibTeX

Empirical Inference Conference Paper Competition of Mechanisms: Tracing How Language Models Handle Facts and Counterfactuals Ortu*, F., Jin*, Z., Doimo, D., Sachan, M., Cazzaniga, A., Schölkopf, B. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL) , Volume 1, Long Papers:8420-8436, (Editors: Lun-Wei Ku and Andre Martins and Vivek Srikumar), Association for Computational Linguistics, August 2024, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Article Leveraging Task Structures for Improved Identifiability in Neural Network Representations Chen*, W., Horwood*, J., Heo, J., Hernández-Lobato, J. M. Transactions on Machine Learning Research, August 2024, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Modelling Variability in Human Annotator Simulation Wu*, W., Chen*, W., Zhang, C., Woodland, P. C. Findings of the Association for Computational Linguistics (ACL), 1139-1157, (Editors: Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek), Association for Computational Linguistics, August 2024, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Moûsai: Efficient Text-to-Music Diffusion Models Schneider, F., Kamal, O., Jin, Z., Schölkopf, B. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL), Volume 1: Long Papers:8050-8068, (Editors: Lun-Wei Ku and Andre Martins and Vivek Srikumar), Association for Computational Linguistics, August 2024 (Published) URL BibTeX

Empirical Inference Conference Paper CausalCite: A Causal Formulation of Paper Citations Agrawal, I., Jin, Z., Mokhtarian, E., Guo, S., Chen, Y., Sachan, M., Schölkopf, B. Findings of the Association for Computational Linguistics (ACL), 8395-8410, (Editors: Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek), Association for Computational Linguistics, August 2024 (Published) arXiv URL BibTeX

Empirical Inference Ph.D. Thesis A Measure-Theoretic Axiomatisation of Causality and Kernel Regression Park, J. University of Tübingen, Germany, July 2024 (Published) BibTeX

Empirical Inference Conference Paper A Sparsity Principle for Partially Observable Causal Representation Learning Xu, D., Yao, D., Lachapelle, S., Taslakian, P., von Kügelgen, J., Locatello, F., Magliacane, S. Proceedings of the 41st International Conference on Machine Learning (ICML), 235:55389-55433, Proceedings of Machine Learning Research, (Editors: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix), PMLR, July 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Accuracy on the wrong line: On the pitfalls of noisy data for OOD generalisation Sanyal, A., Hu, Y., Yu, Y., Ma, Y., Wang, Y., Schölkopf, B. ICML 2024 Next Generation of AI Safety Workshop (Oral), July 2024 (Published) arXiv PDF BibTeX

Empirical Inference Conference Paper All-in-one simulation-based inference Gloeckler, M., Deistler, M., Weilbach, C. D., Wood, F., Macke, J. H. Proceedings of the 41st International Conference on Machine Learning (ICML), 235:15735-15766, Proceedings of Machine Learning Research, (Editors: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix), PMLR, July 2024 (Published) URL BibTeX

Learning and Dynamical Systems Empirical Inference Article Deep Backtracking Counterfactuals for Causally Compliant Explanations Kladny, K., Kügelgen, J. V., Schölkopf, B., Muehlebach, M. Transactions on Machine Learning Research, July 2024 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Detecting and Identifying Selection Structure in Sequential Data Zheng, Y., Tang, Z., Qiu, Y., Schölkopf, B., Zhang, K. Proceedings of the 41st International Conference on Machine Learning (ICML), 235:61498-61525, Proceedings of Machine Learning Research, (Editors: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix), PMLR, July 2024 (Published) URL BibTeX

Empirical Inference Conference Paper Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for ODEs Beck, J., Bosch, N., Deistler, M., Kadhim, K. L., Macke, J. H., Hennig, P., Berens, P. Proceedings of the 41st International Conference on Machine Learning (ICML), 235:3305-3326, Proceedings of Machine Learning Research, (Editors: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix), PMLR, July 2024 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Diffusive Gibbs Sampling Chen*, W., Zhang*, M., Paige, B., Hernández-Lobato, J. M., Barber, D. Proceedings of the 41st International Conference on Machine Learning (ICML), 235:7731-7747, Proceedings of Machine Learning Research, (Editors: Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix), PMLR, July 2024, *equal contribution (Published) URL BibTeX

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