Publications

Empirical Inference Conference Paper Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data Dal Corso, J., Kofler, A., Cortellazzi, M., Bruzzone, L., Schölkopf, B. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), August 2026 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Echoes of the Prior: A Computational Phenomenology of Forgetting Gao, G., Schölkopf, B., Geiger, A. Proceedings of the ACM on Computer Graphics and Interactive Techniques: PACM-CGIT, SIGGRAPH, July 2026 (Accepted) Project BibTeX

Empirische Inferenz Conference Paper On the Emergence and Test-Time Use of Structural Information in Large Language Models Chen, M. C., Miller, M., Schölkopf, B., Guo, S. 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), July 2026 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Differentiable Simulation of Hard Contacts with Soft Gradients for Learning and Control Paulus*, A., Geist*, A. R., Schumacher*, P., Rappenecker, S., Musil, V., Martius, G. The Fourteenth International Conference on Learning Representations (ICLR), April 2026, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Estimating Joint Interventional Distributions from Marginal Interventional Data Garrido Mejia, S., Kirschbaum, E., Kekić, A., Schölkopf, B., Mastakouri, A. A. Proceedings of the Fifth Conference on Causal Learning and Reasoning, 323:1-23, PMLR, 5th Conference on Causal Learning and Reasoning, April 2026 (To be published) arXiv BibTeX

Empirical Inference Conference Paper Learning Nonlinear Causal Reductions to Explain Reinforcement Learning Policies Kekić, A., Schneider, J., Büchler, D., Schölkopf*, B., Besserve*, M. The Fourteenth International Conference on Learning Representations (ICLR), April 2026, *joint supervision (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Making Complex Reasoning Student-Friendly: A Hybrid LLM-to-SLM Distillation Framework Yang, Y., He, Y., Liu, J., Jin, Z. 1st Workshop on Scaling Post-training for LLMs at the Fourteenth International Conference on Learning Representations (ICLR), SPOT@ICLR, April 2026 (Published) URL BibTeX

Empirical Inference Conference Paper Position: Science is Collaborative—LLM for Science Should Be Too Zhang, T. J., Jiang, W., Guzman Piedrahita, D., Yang, Y., Lu, S., Schölkopf, B., Jin, Z. ICLR 2026 – 2nd Workshop on Foundation Models for Science: Real-World Impact and Science-First Design , ICLR - Workshop FM4Science, April 2026 (Published) URL BibTeX

Empirical Inference Conference Paper Proper Velocity Neural Networks Chen*, Z., Su*, Z., Schölkopf, B., Sebe, N. The Fourteenth International Conference on Learning Representations (ICLR), April 2026, *equal contribution (Published) URL BibTeX

Empirical Inference Conference Paper Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles Ni*, Z., Li*, Y., Qiu*, Z., Schölkopf, B., Guo, H., Liu, W., Liu, S. The Fourteenth International Conference on Learning Representations (ICLR), April 2026, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper STRIDE: Training Data Attribution Can Be Estimated In Activation Space Harrasse, A., Dagli, R., Abdullah, A., Jin, Z. ICLR 2026 Workshop on Scientific Methods for Understanding Deep Learning (Sci4DL), Sci4DL at ICLR, April 2026 (Published) URL BibTeX

Empirical Inference Deep Models and Optimization Conference Paper Scaling Behavior of Discrete Diffusion Language Models von Rütte, D., Fluri, J., Pooladzandi, O., Schölkopf, B., Hofmann, T., Orvieto, A. The Fourteenth International Conference on Learning Representations (ICLR), April 2026 (Published) arXiv URL BibTeX

Empirical Inference Robust Machine Learning Conference Paper Skill Learning via Policy Diversity Yields Identifiable Representations for Reinforcement Learning Reizinger*, P., Mucsányi*, B., Guo*, S., Eysenbach, B., Schölkopf, B., Brendel, W. The Fourteenth International Conference on Learning Representations (ICLR), April 2026, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests Pandey, P. S., Le, H. S., Bhardwaj, D., Mihalcea, R., Zhijing, J. The Fourteenth International Conference on Learning Representations (ICLR), April 2026 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Training with Honeypots: Reshaping How LLMs Fail Simko, S., Pandey, P. S., Jin, Z., Schölkopf, B. ICLR 2026 Workshop on Principled Design for Trustworthy AI - Interpretability, Robustness, and Safety across Modalities, ICLR 2026 Trustworthy AI, April 2026 (Published) URL BibTeX

Empirical Inference Conference Paper Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models Guzman Piedrahita*, D., Strauss*, I., Schölkopf, B., Mihalcea, R., Jin, Z. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 593-652, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026, *equal contribution (Published) DOI URL BibTeX

Empirical Inference Conference Paper How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities Kassem, A. M., Schölkopf, B., Jin, Z. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 7496-7507, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Sim-to-Real for Muscle-Actuated Robots via Learned Actuator Models Schneider, J., Mahajan, M., Chen, L., Guist, S., Schölkopf, B., Posner, I., Büchler, D. 2nd German Robotics Conference (GRC), March 2026 (Published) URL BibTeX

Empirical Inference Conference Paper Taming Object Hallucinations with Verified Atomic Confidence Estimation Liu, J., Xuan, W., Jin, Z., Diab, M. T. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 5430-5444, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification He, P., Huang, Y., Sachan, M., Jin, Z. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 1231-1250, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026 (Published) DOI URL BibTeX

Empirical Inference Conference Paper When Do Language Models Endorse Limitations on Human Rights Principles? Samway, K., Takagi, M. N., Mihalcea, R., Schölkopf, B., Chalkidis, I., Hershcovich, D., Jin, Z. Findings of the Association for Computational Linguistics: EACL, 6597-6623, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026 (Published) DOI URL BibTeX

Empirical Inference Conference Paper CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures Pandey, P. S., Yang, Y., Liu, J., Jin, Z. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 1251-1266, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026 (Published) DOI URL BibTeX

Empirical Inference Conference Paper NLP for Social Good: A Survey and Outlook of Challenges, Opportunities and Responsible Deployment Karamolegkou, A., Borah, A., Cho, E., Choudhury, S. R., Galletti, M., Gupta, P., Ignat, O., Kargupta, P., Kotonya, N., Lamba, H., Lee, S., Mangla, A., Mondal, I., Moudakir, F. Z., Nazar, D., Nemkova, P., Pisarevskaya, D., Rizwan, N., Sabri, N., Samway, K., et al. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers), 5110-5170, (Editors: Demberg, Vera and Inui, Kentaro and Marquez, Lluís), Association for Computational Linguistics, 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL), March 2026 (Published) DOI URL BibTeX

Empirical Inference Conference Paper A data and task-constrained mechanistic model of the mouse outer retina shows robustness to contrast variations Kadhim, K. L., Beck, J., Huang, Z., Macke, J. H., Rieke, F., Euler, T., Deistler, M., Berens, P. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) bioRxiv BibTeX

Empirical Inference Conference Paper Are Language Models Efficient Reasoners? A Perspective from Logic Programming Opedal, A., Zengaffinen, Y., Shirakami, H., Pasti, C., Sachan, M., Saparov, A., Cotterell, R., Schölkopf, B. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Autoformalizing Natural Language to First-Order Logic: A Case Study in Logical Fallacy Detection Lalwani*, A., Kim*, T., Chopra, L., Hahn, C., Jin, Z., Sachan, M. Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 132-147, (Editors: Kentaro Inui, Sakriani Sakti, Haofen Wang, Derek F. Wong, Pushpak Bhattacharyya, Biplab Banerjee, Asif Ekbal, Tanmoy Chakraborty, Dhirendra Pratap Singh), The Asian Federation of Natural Language Processing and The Association for Computational Linguistics, IJCNLP & AACL, December 2025, *equal contribution (Published)

Abstract ›

Translating natural language into formal language such as First-Order Logic (FOL) is a foundational challenge in NLP with wide-ranging applications in automated reasoning, misinformation tracking, and knowledge validation. In this paper, we introduce Natural Language to First-Order Logic (NL2FOL), a framework to autoformalize natural language to FOL step-by-step using Large Language Models (LLMs). Our approach addresses key challenges in this translation process, including the integration of implicit background knowledge. By leveraging structured representations generated by NL2FOL, we use Satisfiability Modulo Theory (SMT) solvers to reason about the logical validity of natural language statements. We present logical fallacy detection as a case study to evaluate the efficacy of NL2FOL. Being neurosymbolic, our approach also provides interpretable insights into the reasoning process and demonstrates robustness without requiring model fine-tuning or labeled training data. Our framework achieves good performance on multiple datasets{--}on the Logic dataset, NL2FOL achieves an F1-score of 78{\%}, while generalizing effectively to the LogicClimate dataset with an F1-score of 80{\%}.

DOI URL BibTeX

Empirical Inference Conference Paper CauSciBench: Assessing LLM Causal Reasoning for Scientific Research Acharya, S., Zhang, T. J., Kim, A., Haghighat, A., Sun, X., Shrestha, R. B., Mordig, M., Danisman, F., Jose, C., Qi, Y., Cobben, P., Schölkopf, B., Sachan, M., Jin, Z. NeurIPS 2025: 5th Workshop on Mathematical Reasoning and AI (Math-AI) and CauScien Workshop, December 2025 (Published) URL BibTeX

Empirical Inference Conference Paper Counterfactual reasoning: an analysis of in-context emergence Miller, M., Schölkopf, B., Guo, S. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Cultural Alien Sampler: Open-ended art generation balancing originality and coherence Hernandez, A., Yakura, H., Brinkmann, L., Sola, M. C., Alhaija, H. A., Serna, I., Rahaman, N., Schölkopf, B., Rahwan, I. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, Creative AI Track, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Do-PFN: In-Context Learning for Causal Effect Estimation Robertson*, J., Reuter*, A., Guo, S., Hollmann, N., Hutter, F., Schölkopf, B. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025, *equal contribution (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Effortless, Simulation-Efficient Bayesian Inference using Tabular Foundation Models Vetter, J., Gloeckler, M., Gedon, D., Macke, J. H. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper FNOPE: Simulation-based inference on function spaces with Fourier Neural Operators Moss, G., Muhle, L. S., Drews, R., Macke, J. H., Schröder, C. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Identifying multi-compartment Hodgkin-Huxley models with high-density extracellular voltage recordings Tanoh, I. C., Deistler, M., Macke, J. H., Linderman, S. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Quriosity: Analyzing Human Questioning Behavior and Causal Inquiry through Curiosity-Driven Queries Ceraolo*, R., Kharlapenko*, D., Khan*, A., Reymond, A., Mihalcea, R., Schölkopf, B., Sachan, M., Jin, Z. Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics, 534-563, (Editors: Kentaro Inui, Sakriani Sakti, Haofen Wang, Derek F. Wong, Pushpak Bhattacharyya, Biplab Banerjee, Asif Ekbal, Tanmoy Chakraborty, Dhirendra Pratap Singh), The Asian Federation of Natural Language Processing and The Association for Computational Linguistics, IJCNLP & AACL, December 2025, *equal contribution (Published)

Abstract ›

Recent progress in Large Language Model (LLM) technology has changed our role in interacting with these models. Instead of primarily testing these models with questions we already know answers to, we are now using them for queries where the answers are unknown to us, driven by human curiosity. This shift highlights the growing need to understand curiosity-driven human questions {--} those that are more complex, open-ended, and reflective of real-world needs. To this end, we present Quriosity, a collection of 13K naturally occurring questions from three diverse sources: human-to-search-engine queries, human-to-human interactions, and human-to-LLM conversations. Our comprehensive collection enables a rich understanding of human curiosity across various domains and contexts. Our analysis reveals a significant presence of causal questions (up to 42{\%}) in the dataset, for which we develop an iterative prompt improvement framework to identify all causal queries and examine their unique linguistic properties, cognitive complexity and source distribution. We also lay the groundwork for exploring efficient identifiers of causal questions, providing six efficient classification models.

DOI URL BibTeX

Empirical Inference Conference Paper Reparameterized LLM Training via Orthogonal Equivalence Transformation Qiu, Z., Buchholz, S., Xiao, T., Dax, M., Schölkopf, B., Liu, W. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Root Cause Analysis of Outliers with Missing Structural Knowledge Orchard, W. R., Okati, N., Garrido Mejia, S., Blöbaum, P., Janzing, D. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper SPARTAN: A Sparse Transformer World Model Attending to What Matters Lei, A., Schölkopf, B., Posner, I. Advances in Neural Information Processing Systems 38 (NeurIPS 2025), 39th Annual Conference on Neural Information Processing Systems, December 2025 (Accepted) arXiv BibTeX

Empirical Inference Conference Paper Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models Choi*, Y., Li*, C., Yang, Y., Jin, Z. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 28895-28928, (Editors: Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet), Association for Computational Linguistics, EMNLP, November 2025, *equal contribution (Published)

Abstract ›

As large language models (LLMs) are increasingly integrated into multi-agent and human-AI systems, understanding their awareness of both self-context and conversational partners is essential for ensuring reliable performance and robust safety. While prior work has extensively studied situational awareness which refers to an LLM’s ability to recognize its operating phase and constraints, it has largely overlooked the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. In this paper, we formalize this latter capability as interlocutor awareness and present the first systematic evaluation of its emergence in contemporary LLMs. We examine interlocutor inference across three dimensions—reasoning patterns, linguistic style, and alignment preferences—and show that LLMs reliably identify same-family peers and certain prominent model families, such as GPT and Claude. To demonstrate its practical significance, we develop three case studies in which interlocutor awareness both enhances multi-LLM collaboration through prompt adaptation and introduces new alignment and safety vulnerabilities, including reward-hacking behaviors and increased jailbreak susceptibility. Our findings highlight the dual promise and peril of identity—sensitive behavior in LLMs, underscoring the need for further understanding of interlocutor awareness and new safeguards in multi-agent deployments.

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Empirical Inference Conference Paper Are Language Models Consequentialist or Deontological Moral Reasoners? Samway, K., Kleiman-Weiner, M., Guzman Piedrahita, D., Mihalcea, R., Schölkopf, B., Jin, Z. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 30699-30726, (Editors: Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet), Association for Computational Linguistics, EMNLP, November 2025 (Published)

Abstract ›

As AI systems increasingly navigate applications in healthcare, law, and governance, understanding how they handle ethically complex scenarios becomes critical. Previous work has mainly examined the moral judgments in large language models (LLMs), rather than their underlying moral reasoning process. In contrast, we focus on a large-scale analysis of the moral reasoning traces provided by LLMs. Furthermore, unlike prior work that attempted to draw inferences from only a handful of moral dilemmas, our study leverages over 600 distinct trolley problems as probes for revealing the reasoning patterns that emerge within different LLMs. We introduce and test a taxonomy of moral rationales to systematically classify reasoning traces according to two main normative ethical theories: consequentialism and deontology. Our analysis reveals that LLM chains-of-thought favor deontological principles based on moral obligations, while post-hoc explanations shift notably toward consequentialist rationales that emphasize utility. Our framework provides a foundation for understanding how LLMs process and articulate ethical considerations, an important step toward safe and interpretable deployment of LLMs in high-stakes decision-making environments."

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Empirical Inference Conference Paper Improving Large Language Model Safety with Contrastive Representation Learning Simko, S., Sachan, M., Schölkopf, B., Jin, Z. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 28166-28194, (Editors: Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet), Association for Computational Linguistics, November 2025 (Published) arXiv DOI URL BibTeX

Empirical Inference Conference Paper Orthogonal Finetuning Made Scalable Qiu*, Z., Liu*, W., Weller, A., Schölkopf, B. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP), 31946-31963, (Editors: Christodoulopoulos, Christos and Chakraborty, Tanmoy and Rose, Carolyn and Peng, Violet), Association for Computational Linguistics, EMNLP, November 2025, *equal contribution (Published)

Abstract ›

Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deployment. We identify the core computational bottleneck in OFT as its weight-centric implementation, which relies on costly matrix-matrix multiplications with cubic complexity. To overcome this, we propose OFTv2, an input-centric reformulation that instead uses matrix-vector multiplications (i.e., matrix-free computation), reducing the computational cost to quadratic. We further introduce the Cayley{--}Neumann parameterization, an efficient orthogonal parameterization that approximates the matrix inversion in the Cayley transform via a truncated Neumann series. These modifications allow OFTv2 to achieve up to 10x faster training and 3x lower GPU memory usage without compromising performance. In addition, we extend OFTv2 to support finetuning quantized foundation models and show that it outperforms the popular QLoRA in training stability, efficiency, and memory usage.

DOI URL BibTeX

Empirical Inference Conference Paper Corrupted by reasoning: Reasoning language models become free-riders in public goods games Guzman Piedrahita, D., Yang, Y., Sachan, M., Ramponi, G., Schölkopf, B., Jin, Z. Second Conference on Language Modeling (COLM 2025), October 2025 (Published) arXiv URL BibTeX

Empirical Inference Deep Models and Optimization Conference Paper Generalized Interpolating Discrete Diffusion von Rütte, D., Fluri, J., Ding, Y., Orvieto, A., Schölkopf, B., Hofmann, T. Proceedings of the 42nd International Conference on Machine Learning (ICML), 267:61810-61843, Proceedings of Machine Learning Research, (Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry), PMLR, International Conference on Machine Learning, July 2025 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Generative Intervention Models for Causal Perturbation Modeling Schneider, N., Lorch, L., Kilbertus, N., Schölkopf, B., Krause, A. Proceedings of the 42nd International Conference on Machine Learning (ICML), 267:53388-53412, Proceedings of Machine Learning Research, (Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry), PMLR, International Conference on Machine Learning, July 2025 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Learning Joint Interventional Effects from Single-Variable Interventions in Additive Models Kekić, A., Garrido Mejia, S., Schölkopf, B. Proceedings of the 42nd International Conference on Machine Learning (ICML), 267:29651-29669, Proceedings of Machine Learning Research, (Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry), PMLR, International Conference on Machine Learning, July 2025 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Position: Probabilistic Modelling is Sufficient for Causal Inference Mlodozeniec, B. K., Krueger, D., Turner, R. E. Proceedings of the 42nd International Conference on Machine Learning (ICML), 267:81810-81840, Proceedings of Machine Learning Research, (Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry), PMLR, International Conference on Machine Learning, July 2025 (Published) URL BibTeX

Empirical Inference Conference Paper Progressive Tempering Sampler with Diffusion Rissanen*, S., OuYang*, R., He*, J., Chen, W., Heinonen, M., Solin, A., Hernández-Lobato, J. M. Proceedings of the 42nd International Conference on Machine Learning (ICML), 267:51724-51746, Proceedings of Machine Learning Research, (Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry), PMLR, International Conference on Machine Learning, July 2025, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Scalable Gaussian Processes with Latent Kronecker Structure Lin, J. A., Ament, A., Balandat, M., Eriksson, D., Hernández-Lobato, J. M., Bakshy, E. Proceedings of the 42nd International Conference on Machine Learning (ICML), 267:37730-37744, Proceedings of Machine Learning Research, (Editors: Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry), PMLR, International Conference on Machine Learning, July 2025 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Accuracy on the wrong line: On the pitfalls of noisy data for out-of-distribution generalisation Sanyal, A., Hu, Y., Yu, Y., Ma, Y., Wang, Y., Schölkopf, B. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 258:2170-2178, Proceedings of Machine Learning Research, (Editors: Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz), PMLR, May 2025 (Published) URL BibTeX

Empirical Inference Conference Paper Training Neural Samplers with Reverse Diffusive KL Divergence He*, J., Chen*, W., Zhang*, M., Barber, D., Hernández-Lobato, J. M. Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), 258:5167-5175, Proceedings of Machine Learning Research, (Editors: Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz), PMLR, May 2025, *equal contribution (Published) URL BibTeX

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