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2024


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Language Models Can Reduce Asymmetry in Information Markets

Rahaman, N., Weiss, M., Wüthrich, M., Bengio, Y., Li, E., Pal, C., Schölkopf, B.

arXiv:2403.14443, March 2024, Published as: Redesigning Information Markets in the Era of Language Models, Conference on Language Modeling (COLM) (techreport)

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.

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link (url) [BibTex]

2024


link (url) [BibTex]


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Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation

Achterhold, J., Guttikonda, S., Kreber, J. U., Li, H., Stueckler, J.

CoRR abs/2409.11452, 2024, Preprint submitted to Robotics and Autonomous Systems Journal. https://arxiv.org/abs/2409.11452 (techreport) Submitted

Abstract
Mobile robots should be capable of planning cost-efficient paths for autonomous navigation. Typically, the terrain and robot properties are subject to variations. For instance, properties of the terrain such as friction may vary across different locations. Also, properties of the robot may change such as payloads or wear and tear, e.g., causing changing actuator gains or joint friction. Autonomous navigation approaches should thus be able to adapt to such variations. In this article, we propose a novel approach for learning a probabilistic, terrain- and robot-aware forward dynamics model (TRADYN) which can adapt to such variations and demonstrate its use for navigation. Our learning approach extends recent advances in meta-learning forward dynamics models based on Neural Processes for mobile robot navigation. We evaluate our method in simulation for 2D navigation of a robot with uni-cycle dynamics with varying properties on terrain with spatially varying friction coefficients. In our experiments, we demonstrate that TRADYN has lower prediction error over long time horizons than model ablations which do not adapt to robot or terrain variations. We also evaluate our model for navigation planning in a model-predictive control framework and under various sources of noise. We demonstrate that our approach yields improved performance in planning control-efficient paths by taking robot and terrain properties into account.

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preprint [BibTex]

preprint [BibTex]


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A Pontryagin Perspective on Reinforcement Learning

Eberhard, O., Vernade, C., Muehlebach, M.

Max Planck Institute for Intelligent Systems, 2024 (techreport)

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link (url) [BibTex]

link (url) [BibTex]


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Distributed Event-Based Learning via ADMM

Er, D., Trimpe, S., Muehlebach, M.

Max Planck Institute for Intelligent Systems, 2024 (techreport)

lds

link (url) [BibTex]

link (url) [BibTex]


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Incremental Few-Shot Adaptation for Non-Prehensile Object Manipulation using Parallelizable Physics Simulators

Baumeister, F., Mack, L., Stueckler, J.

CoRR abs/2409.13228, CoRR, 2024, Submitted to IEEE International Conference on Robotics and Automation (ICRA) 2025 (techreport) Submitted

Abstract
Few-shot adaptation is an important capability for intelligent robots that perform tasks in open-world settings such as everyday environments or flexible production. In this paper, we propose a novel approach for non-prehensile manipulation which iteratively adapts a physics-based dynamics model for model-predictive control. We adapt the parameters of the model incrementally with a few examples of robot-object interactions. This is achieved by sampling-based optimization of the parameters using a parallelizable rigid-body physics simulation as dynamic world model. In turn, the optimized dynamics model can be used for model-predictive control using efficient sampling-based optimization. We evaluate our few-shot adaptation approach in several object pushing experiments in simulation and with a real robot.

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preprint supplemental video link (url) [BibTex]

preprint supplemental video link (url) [BibTex]

2023


Synchronizing Machine Learning Algorithms, Realtime Robotic Control and Simulated Environment with o80
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 (techreport)

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.

ei

arxiv poster link (url) [BibTex]

2022


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Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)

Biester, L., Demszky, D., Jin, Z., Sachan, M., Tetreault, J., Wilson, S., Xiao, L., Zhao, J.

Association for Computational Linguistics, December 2022 (proceedings)

ei

link (url) [BibTex]

2022


link (url) [BibTex]


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Causality, causal digital twins, and their applications

Schölkopf, B.

Machine Learning for Science: Bridging Data-Driven and Mechanistic Modelling (Dagstuhl Seminar 22382), (Editors: Berens, Philipp and Cranmer, Kyle and Lawrence, Neil D. and von Luxburg, Ulrike and Montgomery, Jessica), September 2022 (talk)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Proceedings of the First Conference on Causal Learning and Reasoning (CLeaR 2022)

Schölkopf, B., Uhler, C., Zhang, K.

177, Proceedings of Machine Learning Research, PMLR, April 2022 (proceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


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Observability Analysis of Visual-Inertial Odometry with Online Calibration of Velocity-Control Based Kinematic Motion Models

Li, H., Stueckler, J.

abs/2204.06651, CoRR/arxiv, 2022 (techreport)

Abstract
In this paper, we analyze the observability of the visual-inertial odometry (VIO) using stereo cameras with a velocity-control based kinematic motion model. Previous work shows that in general case the global position and yaw are unobservable in VIO system, additionally the roll and pitch become also unobservable if there is no rotation. We prove that by integrating a planar motion constraint roll and pitch become observable. We also show that the parameters of the motion model are observable.

ev

link (url) [BibTex]

2021


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Physically Plausible Tracking & Reconstruction of Dynamic Objects

Strecke, M., Stückler, J.

KIT Science Week Scientific Conference & DGR-Days 2021, October 2021 (talk)

ev

[BibTex]

2021


[BibTex]


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Proceedings of the 1st Workshop on NLP for Positive Impact

Field, A., Prabhumoye, S., Sap, M., Jin, Z., Zhao, J., Brockett, C.

Association for Computational Linguistics, August 2021 (proceedings)

ei

link (url) [BibTex]

link (url) [BibTex]


Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning
Improving Human Decision-Making by Discovering Efficient Strategies for Hierarchical Planning

Heindrich, L., Consul, S., Stojcheski, J., Lieder, F.

Tübingen, Germany, The first edition of Life Improvement Science Conference, June 2021 (talk) Accepted

Abstract
The discovery of decision strategies is an essential part of creating effective cognitive tutors that teach planning and decision-making skills to humans. In the context of bounded rationality, this requires weighing the benefits of different planning operations compared to their computational costs. For small decision problems, it has already been shown that near-optimal decision strategies can be discovered automatically and that the discovered strategies can be taught to humans to increase their performance. Unfortunately, these near-optimal strategy discovery algorithms have not been able to scale well to larger problems due to their computational complexity. In this talk, we will present recent work at the Rationality Enhancement Group to overcome the computational bottleneck of existing strategy discovery algorithms. Our approach makes use of the hierarchical structure of human behavior by decomposing sequential decision problems into two sub-problems: setting a goal and planning how to achieve it. An additional metacontroller component is introduced to switch the current goal when it becomes beneficial. The hierarchical decomposition enables us to discover near-optimal strategies for human planning in larger and more complex tasks than previously possible. We then show in online experiments that teaching the discovered strategies to humans improves their performance in complex sequential decision-making tasks.

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Project Page [BibTex]

Project Page [BibTex]


Toward a Science of Effective Well-Doing
Toward a Science of Effective Well-Doing

Lieder, F., Prentice, M., Corwin-Renner, E.

May 2021 (techreport)

Abstract
Well-doing, broadly construed, encompasses acting and thinking in ways that contribute to humanity’s flourishing in the long run. This often takes the form of setting a prosocial goal and pursuing it over an extended period of time. To set and pursue goals in a way that is extremely beneficial for humanity (effective well-doing), people often have to employ critical thinking and far-sighted, rational decision-making in the service of the greater good. To promote effective well-doing, we need to better understand its determinants and psychological mechanisms, as well as the barriers to effective well-doing and how they can be overcome. In this article, we introduce a taxonomy of different forms of well-doing and introduce a conceptual model of the cognitive mechanisms of effective well-doing. We view effective well-doing as the upper end of a moral continuum whose lower half comprises behaviors that are harmful to humanity (ill-doing), and we argue that the capacity for effective well-doing has to be developed through personal growth (e.g., learning how to pursue goals effectively). Research on these phenomena has so far been scattered across numerous disconnected literatures from multiple disciplines. To bring these communities together, we call for the establishment of a transdisciplinary research field focussed on understanding and promoting effective well-doing and personal growth as well as understanding and reducing ill-doing. We define this research field in terms of its goals and questions. We review what is already known about these questions in different disciplines and argue that laying the scientific foundation for promoting effective well-doing is one of the most valuable contributions that the behavioral sciences can make in the 21st century.

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Preprint Project Page [BibTex]

2020


Optimal To-Do List Gamification
Optimal To-Do List Gamification

Stojcheski, J., Felso, V., Lieder, F.

ArXiv Preprint, 2020 (techreport)

Abstract
What should I work on first? What can wait until later? Which projects should I prioritize and which tasks are not worth my time? These are challenging questions that many people face every day. People’s intuitive strategy is to prioritize their immediate experience over the long-term consequences. This leads to procrastination and the neglect of important long-term projects in favor of seemingly urgent tasks that are less important. Optimal gamification strives to help people overcome these problems by incentivizing each task by a number of points that communicates how valuable it is in the long-run. Unfortunately, computing the optimal number of points with standard dynamic programming methods quickly becomes intractable as the number of a person’s projects and the number of tasks required by each project increase. Here, we introduce and evaluate a scalable method for identifying which tasks are most important in the long run and incentivizing each task according to its long-term value. Our method makes it possible to create to-do list gamification apps that can handle the size and complexity of people’s to-do lists in the real world.

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link (url) DOI Project Page [BibTex]


Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures
Excursion Search for Constrained Bayesian Optimization under a Limited Budget of Failures

Marco, A., Rohr, A. V., Baumann, D., Hernández-Lobato, J. M., Trimpe, S.

2020 (proceedings) In revision

Abstract
When learning to ride a bike, a child falls down a number of times before achieving the first success. As falling down usually has only mild consequences, it can be seen as a tolerable failure in exchange for a faster learning process, as it provides rich information about an undesired behavior. In the context of Bayesian optimization under unknown constraints (BOC), typical strategies for safe learning explore conservatively and avoid failures by all means. On the other side of the spectrum, non conservative BOC algorithms that allow failing may fail an unbounded number of times before reaching the optimum. In this work, we propose a novel decision maker grounded in control theory that controls the amount of risk we allow in the search as a function of a given budget of failures. Empirical validation shows that our algorithm uses the failures budget more efficiently in a variety of optimization experiments, and generally achieves lower regret, than state-of-the-art methods. In addition, we propose an original algorithm for unconstrained Bayesian optimization inspired by the notion of excursion sets in stochastic processes, upon which the failures-aware algorithm is built.

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arXiv code (python) PDF [BibTex]

2019


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Multivariate coupling estimation between continuous signals and point processes

Safavi, S., Logothetis, N., Besserve, M.

Neural Information Processing Systems 2019 - Workshop on Learning with Temporal Point Processes, December 2019 (talk)

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Talk video link (url) [BibTex]

2019


Talk video link (url) [BibTex]

2018


Impact of Trunk Orientation  for Dynamic Bipedal Locomotion
Impact of Trunk Orientation for Dynamic Bipedal Locomotion

Drama, Ö.

Dynamic Walking Conference, May 2018 (talk)

Abstract
Impact of trunk orientation for dynamic bipedal locomotion My research revolves around investigating the functional demands of bipedal running, with focus on stabilizing trunk orientation. When we think about postural stability, there are two critical questions we need to answer: What are the necessary and sufficient conditions to achieve and maintain trunk stability? I am concentrating on how morphology affects control strategies in achieving trunk stability. In particular, I denote the trunk pitch as the predominant morphology parameter and explore the requirements it imposes on a chosen control strategy. To analyze this, I use a spring loaded inverted pendulum model extended with a rigid trunk, which is actuated by a hip motor. The challenge for the controller design here is to have a single hip actuator to achieve two coupled tasks of moving the legs to generate motion and stabilizing the trunk. I enforce orthograde and pronograde postures and aim to identify the effect of these trunk orientations on the hip torque and ground reaction profiles for different control strategies.

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Impact of trunk orientation for dynamic bipedal locomotion [DW 2018] link (url) [BibTex]


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Detailed Dense Inference with Convolutional Neural Networks via Discrete Wavelet Transform

Ma, L., Stueckler, J., Wu, T., Cremers, D.

arxiv, 2018, arXiv:1808.01834 (techreport)

ev

[BibTex]

[BibTex]

2016


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Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI)

Ihler, A. T., Janzing, D.

pages: 869 pages, AUAI Press, June 2016 (proceedings)

ei

link (url) [BibTex]

2016


link (url) [BibTex]


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Supplemental material for ’Communication Rate Analysis for Event-based State Estimation’

Ebner, S., Trimpe, S.

Max Planck Institute for Intelligent Systems, January 2016 (techreport)

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PDF [BibTex]

PDF [BibTex]

2015


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Distributed Event-based State Estimation

Trimpe, S.

Max Planck Institute for Intelligent Systems, November 2015 (techreport)

Abstract
An event-based state estimation approach for reducing communication in a networked control system is proposed. Multiple distributed sensor-actuator-agents observe a dynamic process and sporadically exchange their measurements and inputs over a bus network. Based on these data, each agent estimates the full state of the dynamic system, which may exhibit arbitrary inter-agent couplings. Local event-based protocols ensure that data is transmitted only when necessary to meet a desired estimation accuracy. This event-based scheme is shown to mimic a centralized Luenberger observer design up to guaranteed bounds, and stability is proven in the sense of bounded estimation errors for bounded disturbances. The stability result extends to the distributed control system that results when the local state estimates are used for distributed feedback control. Simulation results highlight the benefit of the event-based approach over classical periodic ones in reducing communication requirements.

am ics

arXiv [BibTex]

2015


arXiv [BibTex]


Proceedings of the 37th German Conference on Pattern Recognition
Proceedings of the 37th German Conference on Pattern Recognition

Gall, J., Gehler, P., Leibe, B.

Springer, German Conference on Pattern Recognition, October 2015 (proceedings)

ps

GCPR conference website [BibTex]

GCPR conference website [BibTex]


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Causal Inference for Empirical Time Series Based on the Postulate of Independence of Cause and Mechanism

Besserve, M.

53rd Annual Allerton Conference on Communication, Control, and Computing, September 2015 (talk)

ei

[BibTex]

[BibTex]


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Independence of cause and mechanism in brain networks

Besserve, M.

DALI workshop on Networks: Processes and Causality, April 2015 (talk)

ei

[BibTex]

[BibTex]


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Information-Theoretic Implications of Classical and Quantum Causal Structures

Chaves, R., Majenz, C., Luft, L., Maciel, T., Janzing, D., Schölkopf, B., Gross, D.

18th Conference on Quantum Information Processing (QIP), 2015 (talk)

ei

Web link (url) [BibTex]

Web link (url) [BibTex]


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Cosmology from Cosmic Shear with DES Science Verification Data

Abbott, T., Abdalla, F. B., Allam, S., Amara, A., Annis, J., Armstrong, R., Bacon, D., Banerji, M., Bauer, A. H., Baxter, E., others,

arXiv preprint arXiv:1507.05552, 2015 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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The DES Science Verification Weak Lensing Shear Catalogs

Jarvis, M., Sheldon, E., Zuntz, J., Kacprzak, T., Bridle, S. L., Amara, A., Armstrong, R., Becker, M. R., Bernstein, G. M., Bonnett, C., others,

arXiv preprint arXiv:1507.05603, 2015 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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The search for single exoplanet transits in the Kepler light curves

Foreman-Mackey, D., Hogg, D. W., Schölkopf, B.

IAU General Assembly, 22, pages: 2258352, 2015 (talk)

ei

link (url) [BibTex]

link (url) [BibTex]

2014


Model transport: towards scalable transfer learning on manifolds - supplemental material
Model transport: towards scalable transfer learning on manifolds - supplemental material

Freifeld, O., Hauberg, S., Black, M. J.

(9), April 2014 (techreport)

Abstract
This technical report is complementary to "Model Transport: Towards Scalable Transfer Learning on Manifolds" and contains proofs, explanation of the attached video (visualization of bases from the body shape experiments), and high-resolution images of select results of individual reconstructions from the shape experiments. It is identical to the supplemental mate- rial submitted to the Conference on Computer Vision and Pattern Recognition (CVPR 2014) on November 2013.

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PDF [BibTex]


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Unsupervised identification of neural events in local field potentials

Besserve, M., Schölkopf, B., Logothetis, N. K.

44th Annual Meeting of the Society for Neuroscience (Neuroscience), 2014 (talk)

ei

[BibTex]

[BibTex]


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Quantifying statistical dependency

Besserve, M.

Research Network on Learning Systems Summer School, 2014 (talk)

ei

[BibTex]

[BibTex]

2013


Puppet Flow
Puppet Flow

Zuffi, S., Black, M. J.

(7), Max Planck Institute for Intelligent Systems, October 2013 (techreport)

Abstract
We introduce Puppet Flow (PF), a layered model describing the optical flow of a person in a video sequence. We consider video frames composed by two layers: a foreground layer corresponding to a person, and background. We model the background as an affine flow field. The foreground layer, being a moving person, requires reasoning about the articulated nature of the human body. We thus represent the foreground layer with the Deformable Structures model (DS), a parametrized 2D part-based human body representation. We call the motion field defined through articulated motion and deformation of the DS model, a Puppet Flow. By exploiting the DS representation, Puppet Flow is a parametrized optical flow field, where parameters are the person's pose, gender and body shape.

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pdf Project Page Project Page [BibTex]

2013


pdf Project Page Project Page [BibTex]


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Studying large-scale brain networks: electrical stimulation and neural-event-triggered fMRI

Logothetis, N., Eschenko, O., Murayama, Y., Augath, M., Steudel, T., Evrard, H., Besserve, M., Oeltermann, A.

Twenty-Second Annual Computational Neuroscience Meeting (CNS*2013), July 2013, journal = {BMC Neuroscience}, year = {2013}, month = {7}, volume = {14}, number = {Supplement 1}, pages = {A1}, (talk)

ei

Web [BibTex]

Web [BibTex]


Learning and Optimization with Submodular Functions
Learning and Optimization with Submodular Functions

Sankaran, B., Ghazvininejad, M., He, X., Kale, D., Cohen, L.

ArXiv, May 2013 (techreport)

Abstract
In many naturally occurring optimization problems one needs to ensure that the definition of the optimization problem lends itself to solutions that are tractable to compute. In cases where exact solutions cannot be computed tractably, it is beneficial to have strong guarantees on the tractable approximate solutions. In order operate under these criterion most optimization problems are cast under the umbrella of convexity or submodularity. In this report we will study design and optimization over a common class of functions called submodular functions. Set functions, and specifically submodular set functions, characterize a wide variety of naturally occurring optimization problems, and the property of submodularity of set functions has deep theoretical consequences with wide ranging applications. Informally, the property of submodularity of set functions concerns the intuitive principle of diminishing returns. This property states that adding an element to a smaller set has more value than adding it to a larger set. Common examples of submodular monotone functions are entropies, concave functions of cardinality, and matroid rank functions; non-monotone examples include graph cuts, network flows, and mutual information. In this paper we will review the formal definition of submodularity; the optimization of submodular functions, both maximization and minimization; and finally discuss some applications in relation to learning and reasoning using submodular functions.

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arxiv link (url) [BibTex]

arxiv link (url) [BibTex]


A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them

Sun, D., Roth, S., Black, M. J.

(CS-10-03), Brown University, Department of Computer Science, January 2013 (techreport)

ps

pdf [BibTex]

pdf [BibTex]


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Animating Samples from Gaussian Distributions

Hennig, P.

(8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 (techreport)

ei pn

PDF [BibTex]

PDF [BibTex]


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Proceedings of the 10th European Workshop on Reinforcement Learning, Volume 24

Deisenroth, M., Szepesvári, C., Peters, J.

pages: 173, JMLR, European Workshop On Reinforcement Learning, EWRL, 2013 (proceedings)

ei

Web [BibTex]

Web [BibTex]


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Domain Generalization via Invariant Feature Representation

Muandet, K.

30th International Conference on Machine Learning (ICML2013), 2013 (talk)

ei

PDF [BibTex]

PDF [BibTex]


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Maximizing Kepler science return per telemetered pixel: Detailed models of the focal plane in the two-wheel era

Hogg, D. W., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Lang, D., Montet, B. T., Schiminovich, D., Schölkopf, B.

arXiv:1309.0653, 2013 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]


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Maximizing Kepler science return per telemetered pixel: Searching the habitable zones of the brightest stars

Montet, B. T., Angus, R., Barclay, T., Dawson, R., Fergus, R., Foreman-Mackey, D., Harmeling, S., Hirsch, M., Hogg, D. W., Lang, D., Schiminovich, D., Schölkopf, B.

arXiv:1309.0654, 2013 (techreport)

ei

link (url) [BibTex]

link (url) [BibTex]