Publications

Empirical Inference Article GRASP: Scalable Graph Alignment by Spectral Corresponding Functions Hermanns, J., Skitsas, K., Tsitsulin, A., Munkhoeva, M., Kyster, A., Nielsen, S., Bronstein, A. M., Mottin, D., Karras, P. ACM Transactions on Knowledge Discovery from Data, 17(4), February 2023 (Published) DOI BibTeX

Empirical Inference Article SphereFace Revived: Unifying Hyperspherical Face Recognition Liu, W., Wen, Y., Raj, B., Singh, R., Weller, A. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2):2458-2474, February 2023 (Published) DOI BibTeX

Empirical Inference Conference Paper Towards Empirical Process Theory for Vector-Valued Functions: Metric Entropy of Smooth Function Classes Park, J., Muandet, K. Proceedings of the 34th International Conference on Algorithmic Learning Theory (ALT), 201:1216-1260, Proceedings of Machine Learning Research, (Editors: Agrawal, Shipra and Orabona, Francesco), PMLR, February 2023 (Published) URL BibTeX

Empirical Inference Master Thesis Towards Generative Machine Teaching Qui, Z. Technical University of Munich, Germany, February 2023 (Published) BibTeX

Empirical Inference Article ViViT: Curvature Access Through The Generalized Gauss-Newton’s Low-Rank Structure Dangel*, F., Tatzel*, L., Hennig, P. Transactions on Machine Learning Research, February 2023, *equal contribution (Published) URL BibTeX

Empirical Inference Article A machine learning route between band mapping and band structure Xian*, R. P., Stimper*, V., Zacharias, M., Dendzik, M., Dong, S., Beaulieu, S., Schölkopf, B., Wolf, M., Rettig, L., Carbogno, C., Bauer, S., Ernstorfer, R. Nature Computational Science, 3(1):101-114, January 2023, *equal contribution (Published) arXiv DOI BibTeX

Empirical Inference Master Thesis ArchiSound: Audio Generation with Diffusion Schneider, F. ETH Zurich, Switzerland, January 2023, external supervision (Published) BibTeX

Empirical Inference Article Audio Retrieval With Natural Language Queries: A Benchmark Study Koepke, A. S., Oncescu, A., Henriques, J. F., Akata, Z., Albanie, S. IEEE Transactions on Multimedia, 25:2675-2685, January 2023 (Published) DOI BibTeX

Empirical Inference Master Thesis Generation and Quantification of Spin in Robot Table Tennis Dittrich, A. University of Stuttgart, Germany, January 2023 (Published) BibTeX

Empirical Inference Article Learning Dynamical Systems using Local Stability Priors Mehrjou, A., Iannelli, A., Schölkopf, B. Journal of Computational Dynamics, 10(1):175-198, January 2023, Special issue "Computation of Lyapunov functions and contraction metrics" (Published) DOI BibTeX

Empirical Inference Article Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots Büchler, D., Calandra, R., Peters, J. Robotics and Autonomous Systems, 159, Elsevier, Amsterdam, January 2023 (Published)

Abstract ›

High-speed and high-acceleration movements are inherently hard to control. Applying learning to the control of such motions on anthropomorphic robot arms can improve the accuracy of the control but might damage the system. The inherent exploration of learning approaches can lead to instabilities and the robot reaching joint limits at high speeds. Having hardware that enables safe exploration of high-speed and high-acceleration movements is therefore desirable. To address this issue, we propose to use robots actuated by Pneumatic Artificial Muscles (PAMs). In this paper, we present a four degrees of freedom (DoFs) robot arm that reaches high joint angle accelerations of up to 28000 °/s^2 while avoiding dangerous joint limits thanks to the antagonistic actuation and limits on the air pressure ranges. With this robot arm, we are able to tune control parameters using Bayesian optimization directly on the hardware without additional safety considerations. The achieved tracking performance on a fast trajectory exceeds previous results on comparable PAM-driven robots. We also show that our system can be controlled well on slow trajectories with PID controllers due to careful construction considerations such as minimal bending of cables, lightweight kinematics and minimal contact between PAMs and PAMs with the links. Finally, we propose a novel technique to control the the co-contraction of antagonistic muscle pairs. Experimental results illustrate that choosing the optimal co-contraction level is vital to reach better tracking performance. Through the use of PAM-driven robots and learning, we do a small step towards the future development of robots capable of more human-like motions.

Arxiv Video DOI URL BibTeX

Autonomous Learning Haptic Intelligence Empirical Inference Article Predicting the Force Map of an ERT-Based Tactile Sensor Using Simulation and Deep Networks Lee, H., Sun, H., Park, H., Serhat, G., Javot, B., Martius, G., Kuchenbecker, K. J. IEEE Transactions on Automation Science and Engineering, 20(1):425-439, January 2023 (Published)

Abstract ›

Electrical resistance tomography (ERT) can be used to create large-scale soft tactile sensors that are flexible and robust. Good performance requires a fast and accurate mapping from the sensor's sequential voltage measurements to the distribution of force across its surface. However, particularly with multiple contacts, this task is challenging for both previously developed approaches: physics-based modeling and end-to-end data-driven learning. Some promising results were recently achieved using sim-to-real transfer learning, but estimating multiple contact locations and accurate contact forces remains difficult because simulations tend to be less accurate with a high number of contact locations and/or high force. This paper introduces a modular hybrid method that combines simulation data synthesized from an electromechanical finite element model with real measurements collected from a new ERT-based tactile sensor. We use about 290,000 simulated and 90,000 real measurements to train two deep neural networks: the first (Transfer-Net) captures the inevitable gap between simulation and reality, and the second (Recon-Net) reconstructs contact forces from voltage measurements. The number of contacts, contact locations, force magnitudes, and contact diameters are evaluated for a manually collected multi-contact dataset of 150 measurements. Our modular pipeline's results outperform predictions by both a physics-based model and end-to-end learning.

DOI BibTeX

Empirical Inference Article Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases Mehrjou*, A., Soleymani*, A., Abyaneh, A., Bhatt, S., Schölkopf, B., Bauer, S. PLOS Computational Biology, 19(1):1-41, January 2023, *equal contribution (Published) DOI BibTeX

Empirical Inference Article Quantum machine learning beyond kernel methods Jerbi, S., Fiderer, L. J., Poulsen Nautrup, H., Kübler, J. M., Briegel, H. J., Dunjko, V. Nature Communications, 14(1), January 2023 (Published) DOI BibTeX

Empirical Inference Article A Deterministic Approximation to Neural SDEs Look, A., Kandemir, M., Rakitsch, B., Peters, J. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(4):4023-4037, 2023 (Published) DOI BibTeX

Empirical Inference Article Altered brain dynamics index levels of arousal in complete locked-in syndrome Zilio, F., Gomez-Pilar, J., Chaudhary, U., Fogel, S., Fomina, T., Synofzik, M., Schöls, L., Cao, S., Zhang, J., Huang, Z., Birbaumer, N., Northoff, G. Communications Biology, 6(1), 2023 (Published) DOI BibTeX

Empirical Inference Article Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing Fay, L., Cobos, E., Yang, B., Gatidis, S., Küstner, T. IEEE Access, 11:64070-64086, 2023 (Published) DOI BibTeX

Movement Generation and Control Empirical Inference Autonomous Motion Article BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning Meduri, A., Shah, P., Viereck, J., Khadiv, M., Havoutis, I., Righetti, L. IEEE Transactions on Robotics, 39(2):905-922, IEEE, 2023 (Published) DOI URL BibTeX

Empirical Inference Miscellaneous Borges und die Künstliche Intelligenz Bottou, L., Schölkopf, B. 2023, published in Frankfurter Allgemeine Zeitung, 18 December 2023, Nr. 294 (Published) PDF BibTeX

Empirical Inference Article Information theoretic measures of causal influences during transient neural events Shao, K., Logothetis, N. K., Besserve, M. Frontiers in Network Physiology, 3, 2023 (Published) DOI URL BibTeX

Empirical Inference Article Metrizing Weak Convergence with Maximum Mean Discrepancies Simon-Gabriel, C., Barp, A., Schölkopf, B., Mackey, L. Journal of Machine Learning Research, 24(184), 2023 (Published)

Abstract ›

This paper characterizes the maximum mean discrepancies (MMD) that metrize the weak convergence of probability measures for a wide class of kernels. More precisely, we prove that, on a locally compact, non-compact, Hausdorff space, the MMD of a bounded continuous Borel measurable kernel k, whose RKHS-functions vanish at infinity (i.e., Hk ⊂ C0), metrizes the weak convergence of probability measures if and only if k is continuous and integrally strictly positive definite (∫ s.p.d.) over all signed, finite, regular Borel measures. We also correct a prior result of Simon-Gabriel and Schölkopf (JMLR 2018, Thm. 12) by showing that there exist both bounded continuous ∫ s.p.d. kernels that do not metrize weak convergence and bounded continuous non-∫ s.p.d. kernels that do metrize it

arXiv URL BibTeX

Empirical Inference Article Mimicking Tumor Cell Heterogeneity of Colorectal Cancer in a Patient-derived Organoid-Fibroblast Model Atanasova, V. S., de Jesus Cardona, C., Hejret, V., Tiefenbacher, A., Mair, T., Tran, L., Pfneissl, J., Draganić, K., Binder, C., Kabiljo, J., Clement, J., Woeran, K., Neudert, B., Wohlhaupter, S., Haase, A., Domazet, S., Hengstschläger, M., Mitterhauser, M., Müllauer, L., Tichý, B., et al. Cellular and molecular gastroenterology and hepatology, 15(6):1391-1419, 2023 (Published) DOI BibTeX

Empirical Inference Book Chapter Natural Language Processing for Policymaking Jin, Z., Mihalcea, R. In Handbook of Computational Social Science for Policy, 141-162, 7, (Editors: Bertoni, E. and Fontana, M. and Gabrielli, L. and Signorelli, S. and Vespe, M.), Springer International Publishing, 2023 (Published) DOI BibTeX

Empirical Inference Technical Report Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80 Berenz, V., Widmaier, F., Guist, S., Schölkopf, B., Büchler, D. Robot Software Architectures Workshop (RSA) 2023, ICRA, 2023 (Published)

Abstract ›

Robotic applications require the integration of various modalities, encompassing perception, control of real robots and possibly the control of simulated environments. While the state-of-the-art robotic software solutions such as ROS 2 provide most of the required features, flexible synchronization between algorithms, data streams and control loops can be tedious. o80 is a versatile C++ framework for robotics which provides a shared memory model and a command framework for real-time critical systems. It enables expert users to set up complex robotic systems and generate Python bindings for scientists. o80's unique feature is its flexible synchronization between processes, including the traditional blocking commands and the novel ``bursting mode'', which allows user code to control the execution of the lower process control loop. This makes it particularly useful for setups that mix real and simulated environments.

arxiv poster URL BibTeX

Empirical Inference Article normflows: A PyTorch Package for Normalizing Flows Stimper, V., Liu, D., Campbell, A., Berenz, V., Ryll, L., Schölkopf, B., Hernández-Lobato, J. M. Journal of Open Source Software, 8(86):5361, The Journal of Open Source Software, 2023 (Published)

Abstract ›

Normalizing flows model probability distributions through an expressive tractable density (D. Rezende & Mohamed, 2015; Esteban G. Tabak & Turner, 2013; Esteban G. Tabak & Vanden-Eijnden, 2010). They transform a simple base distribution, such as a Gaussian, through a sequence of invertible functions, which are referred to as layers. These layers typically use neural networks to become very expressive. Flows are ubiquitous in machine learning and have been applied to image generation (Grcić et al., 2021; Kingma & Dhariwal, 2018), text modeling (Wang & Wang, 2019), variational inference (D. Rezende & Mohamed, 2015), approximating Boltzmann distributions (Noé et al., 2019), and many other problems (Kobyzev et al., 2021; Papamakarios et al., 2021). Here, we present normflows, a Python package for normalizing flows. It allows to build normalizing flow models from a suite of base distributions, flow layers, and neural networks. The package is implemented in the popular deep learning framework PyTorch (Paszke et al., 2019), which simplifies the integration of flows in larger machine learning models or pipelines. It supports most of the common normalizing flow architectures, such as Real NVP (Dinh et al., 2017), Glow (Kingma & Dhariwal, 2018), Masked Autoregressive Flows (Papamakarios et al., 2017), Neural Spline Flows (Durkan et al., 2019; Müller et al., 2019), Residual Flows (Chen et al., 2019), and many more. The package can be easily installed via pip and the code is publicly available on GitHub.

JOSS GitHub DOI URL BibTeX

Empirical Inference Conference Paper Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Immer, A. P. E. M. A. V. J. E. In Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 1-24, Curran Associates Inc., NeurIPS, 2023 (Published) DOI URL BibTeX

Empirical Inference Article Quantitative three-dimensional imaging of chemical short-range order via machine learning enhanced atom probe tomography Li, Y. W. Y. W. Z. L. X. C. T. H. L. R. Z. Z. X. H. L. D. R. G. Y. N. J. M. A. R. M. B. S. L. H. B. I. S. L. T. G. B. Nature Communications, 14:7410, 2023 (Published)

Abstract ›

Chemical short-range order (CSRO) refers to atoms of specific elements self-organising within a disordered crystalline matrix to form particular atomic neighbourhoods. CSRO is typically characterized indirectly, using volume-averaged or through projection microscopy techniques that fail to capture the three-dimensional atomistic architectures. Here, we present a machine-learning enhanced approach to break the inherent resolution limits of atom probe tomography enabling three-dimensional imaging of multiple CSROs. We showcase our approach by addressing a long-standing question encountered in body-centred-cubic Fe-Al alloys that see anomalous property changes upon heat treatment. We use it to evidence non-statistical B2-CSRO instead of the generally-expected D03-CSRO. We introduce quantitative correlations among annealing temperature, CSRO, and nano-hardness and electrical resistivity. Our approach is further validated on modified D03-CSRO detected in Fe-Ga. The proposed strategy can be generally employed to investigate short/medium/long-range ordering phenomena in different materials and help design future high-performance materials.

DOI URL BibTeX

Human Aspects of Machine Learning Empirical Inference Conference Paper Robustness implies fairness in causal algorithmic recourse. Ehyaei, A., Karimi, A., Schölkopf, B., Maghsudi, S. In 2023 (Published) BibTeX

Haptic Intelligence Autonomous Learning Empirical Inference Miscellaneous A Sequential Group VAE for Robot Learning of Haptic Representations Richardson, B. A., Kuchenbecker, K. J., Martius, G. 1-11, Workshop paper (8 pages) presented at the CoRL Workshop on Aligning Robot Representations with Humans, Auckland, New Zealand, December 2022 (Published)

Abstract ›

Haptic representation learning is a difficult task in robotics because information can be gathered only by actively exploring the environment over time, and because different actions elicit different object properties. We propose a Sequential Group VAE that leverages object persistence to learn and update latent general representations of multimodal haptic data. As a robot performs sequences of exploratory procedures on an object, the model accumulates data and learns to distinguish between general object properties, such as size and mass, and trial-to-trial variations, such as initial object position. We demonstrate that after very few observations, the general latent representations are sufficiently refined to accurately encode many haptic object properties.

URL BibTeX

Empirical Inference Article A survey of algorithmic recourse: contrastive explanations and consequential recommendations Karimi, A., Barthe, G., Schölkopf, B., Valera, I. ACM Computing Surveys, 55(5), Association for Computing Machinery (ACM), December 2022 (Published) arXiv DOI URL BibTeX

Empirical Inference Conference Paper Active Bayesian Causal Inference Toth, C., Lorch, L., Knoll, C., Krause, A., Pernkopf, F., Peharz*, R., von Kügelgen*, J. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:16261-16275, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), December 2022, *shared last author (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Amortized Inference for Causal Structure Learning Lorch, L., Sussex, S., Rothfuss, J., Krause, A., Schölkopf, B. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:13104-13118, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper AutoML Two-Sample Test Kübler, J. M., Stimper, V., Buchholz, S., Muandet, K., Schölkopf, B. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:15929-15941, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis Perry, R., von Kügelgen*, J., Schölkopf*, B. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 10904-10917, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), December 2022, *shared last author (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Decoding Attention from Gaze: A Benchmark Dataset and End-to-End Models Uppal, K., Kim, J., Singh, S. Proceedings of The 1st Gaze Meets ML workshop in conjunction with NeurIPS 2022, 210:219-240, Proceedings of Machine Learning Research, (Editors: Lourentzou, Ismini and Wu, Joy and Kashyap, Satyananda and Karargyris, Alexandros and Celi, Leo Anthony and Kawas, Ban and Talathi, Sachin), PMLR, December 2022 (Published) PDF URL BibTeX

Empirical Inference Conference Paper Differentially Private Language Models for Secure Data Sharing Mattern, J., Jin, Z., Weggenmann, B., Schölkopf, B., Sachan, M. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), 4860-4873, (Editors: Yoav Goldberg and Zornitsa Kozareva and Yue Zhang), Association for Computational Linguistics, The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) , December 2022 (Published) arXiv DOI URL BibTeX

Empirical Inference Conference Paper Direct Advantage Estimation Pan, H., Gürtler, N., Neitz, A., Schölkopf, B. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:11869-11880, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022, *also at 15th European Workshop on Reinforcement Learning (EWRL 2022) (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Efficient identification of informative features in simulation-based inference Beck, J., Deistler, M., Bernaerts, Y., Macke, J. H., Berens, P. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:19260-19273, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) URL BibTeX

Empirical Inference Autonomous Learning Robust Machine Learning Conference Paper Embrace the Gap: VAEs Perform Independent Mechanism Analysis Reizinger*, P., Gresele*, L., Brady*, J., von Kügelgen, J., Zietlow, D., Schölkopf, B., Martius, G., Brendel, W., Besserve, M. Advances in Neural Information Processing Systems (NeurIPS 2022), 35:12040-12057, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022, *equal first authorship (Published) Arxiv PDF URL BibTeX

Empirical Inference Conference Paper Exploring the Latent Space of Autoencoders with Interventional Assays Leeb, F., Bauer, S., Besserve, M., Schölkopf, B. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:21562-21574, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Function Classes for Identifiable Nonlinear Independent Component Analysis Buchholz, S., Besserve, M., Schölkopf, B. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:16946-16961, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) arXiv URL BibTeX

Empirical Inference Article Generalized Few-Shot Video Classification With Video Retrieval and Feature Generation Xian, Y., Korbar, B., Douze, M., Torresani, L., Schiele, B., Akata, Z. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(12):8949-8961, December 2022 (Published) DOI URL BibTeX

Empirical Inference Conference Paper Interventions, Where and How? Experimental Design for Causal Models at Scale Tigas, P., Annadani, Y., Jesson, A., Schölkopf, B., Gal, Y., Bauer, S. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:24130-24143, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations Immer, A., van der Ouderaa, T. F. A., Rätsch, G., Fortuin, V., van der Wilk, M. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:12449-12463, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022 (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Learning Random Feature Dynamics for Uncertainty Quantification Agudelo-España, D., Nemmour, Y., Schölkopf, B., Zhu, J. 2022 IEEE 61st IEEE Conference on Decision and Control (CDC), 4937-4944, IEEE, 61st IEEE Conference on Decision and Control (CDC 2022), December 2022 (Published) PDF DOI URL BibTeX

Empirical Inference Autonomous Learning Conference Paper Learning with Muscles: Benefits for Data-Efficiency and Robustness in Anthropomorphic Tasks Wochner, I., Schumacher, P., Martius, G., Büchler, D., Schmitt, S., Haeufle, D. Proceedings of the 6th Conference on Robot Learning (CoRL), 205:1178-1188, Proceedings of Machine Learning Research, (Editors: Liu, Karen and Kulic, Dana and Ichnowski, Jeff), PMLR, December 2022 (Published) URL BibTeX

Empirical Inference Conference Paper Logical Fallacy Detection Jin, Z., Lalwani, A., Vaidhya, T., Shen, X., Ding, Y., Lyu, Z., Sachan, M., Mihalcea, R., Schölkopf, B. Findings of the Association for Computational Linguistics: EMNLP 2022, 7180-7198, (Editors: Goldberg, Yoav and Kozareva, Zornitsa and Zhang, Yue), Association for Computational Linguistics, The 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP 2022) , December 2022 (Published) arXiv DOI URL BibTeX

Empirical Inference Conference Paper Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee Nemmour, Y., Kremer, H., Schölkopf, B., Zhu, J. IEEE 61st Conference on Decision and Control (CDC), 5660-5667, December 2022 (Published) DOI BibTeX

Empirical Inference Conference Paper Neural Attentive Circuits Weiss*, M., Rahaman*, N., Locatello, F., Pal, C., Bengio, Y., Schölkopf, B., Li, E. L., Ballas, N. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 35:7741-7754, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems, December 2022, *equal contribution (Published) arXiv URL BibTeX

Empirical Inference Conference Paper Optimal Binary Classification Beyond Accuracy Singh, S., Khim, J. Advances in Neural Information Processing Systems 35 (NeurIPS 2022), 18226-18240, (Editors: S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh), Curran Associates, Inc., 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), December 2022 (Published) arXiv URL BibTeX

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