Publications | Empirical Inference - Max Planck Institute for Intelligent Systems

Controlling musculoskeletal systems, especially robots actuated by pneumatic artificial muscles, is a challenging task due to nonlinearities, hysteresis effects, massive actuator de- lay and unobservable dependencies such as temperature. Despite such difficulties, muscular systems offer many beneficial prop- erties to achieve human-comparable performance in uncertain and fast-changing tasks. For example, muscles are backdrivable and provide variable stiffness while offering high forces to reach high accelerations. In addition, the embodied intelligence deriving from the compliance might reduce the control demands for specific tasks. In this paper, we address the problem of how to accurately control musculoskeletal robots. To address this issue, we propose to learn probabilistic forward dynamics models using Gaussian processes and, subsequently, to employ these models for control. However, Gaussian processes dynamics models cannot be set-up for our musculoskeletal robot as for traditional motor- driven robots because of unclear state composition etc. We hence empirically study and discuss in detail how to tune these approaches to complex musculoskeletal robots and their specific challenges. Moreover, we show that our model can be used to accurately control an antagonistic pair of pneumatic artificial muscles for a trajectory tracking task while considering only one- step-ahead predictions of the forward model and incorporating model uncertainty.

RAL18final link (url) DOI Project Page [BibTex]

Büchler, D., Calandra, R., Schölkopf, B., Peters, J. Control of Musculoskeletal Systems using Learned Dynamics Models IEEE Robotics and Automation Letters, Robotics and Automation Letters, 3(4):3161-3168, IEEE, 2018 (article)

RAL18final link (url) DOI Project Page [BibTex]

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

Ruiz, F. J. R., Valera, I., Svensson, L., Perez-Cruz, F.

IEEE Transactions on Cognitive Communications and Networking, 4(2):177-191, June 2018 (article)

DOI Project Page [BibTex]

Ruiz, F. J. R., Valera, I., Svensson, L., Perez-Cruz, F. Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation IEEE Transactions on Cognitive Communications and Networking, 4(2):177-191, June 2018 (article)

DOI Project Page [BibTex]

Assisting Movement Training and Execution With Visual and Haptic Feedback

Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G.

Frontiers in Neurorobotics, 12, May 2018 (article)

DOI Project Page [BibTex]

Ewerton, M., Rother, D., Weimar, J., Kollegger, G., Wiemeyer, J., Peters, J., Maeda, G. Assisting Movement Training and Execution With Visual and Haptic Feedback Frontiers in Neurorobotics, 12, May 2018 (article)

DOI Project Page [BibTex]

Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations

Manschitz, S., Gienger, M., Kober, J., Peters, J.

IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

DOI Project Page [BibTex]

Manschitz, S., Gienger, M., Kober, J., Peters, J. Mixture of Attractors: A Novel Movement Primitive Representation for Learning Motor Skills From Demonstrations IEEE Robotics and Automation Letters, 3(2):926-933, April 2018 (article)

DOI Project Page [BibTex]

Probabilistic movement primitives under unknown system dynamics

Paraschos, A., Rueckert, E., Peters, J., Neumann, G.

Advanced Robotics, 32(6):297-310, April 2018 (article)

DOI Project Page [BibTex]

Paraschos, A., Rueckert, E., Peters, J., Neumann, G. Probabilistic movement primitives under unknown system dynamics Advanced Robotics, 32(6):297-310, April 2018 (article)

DOI Project Page [BibTex]

An Algorithmic Perspective on Imitation Learning

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J.

Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

DOI Project Page [BibTex]

Osa, T., Pajarinen, J., Neumann, G., Bagnell, J., Abbeel, P., Peters, J. An Algorithmic Perspective on Imitation Learning Foundations and Trends in Robotics, 7(1-2):1-179, March 2018 (article)

DOI Project Page [BibTex]

Using Probabilistic Movement Primitives in Robotics

Paraschos, A., Daniel, C., Peters, J., Neumann, G.

Autonomous Robots, 42(3):529-551, March 2018 (article)

DOI Project Page [BibTex]

Paraschos, A., Daniel, C., Peters, J., Neumann, G. Using Probabilistic Movement Primitives in Robotics Autonomous Robots, 42(3):529-551, March 2018 (article)

DOI Project Page [BibTex]

A kernel-based approach to learning contact distributions for robot manipulation tasks

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J.

Autonomous Robots, 42(3):581-600, March 2018 (article)

DOI Project Page [BibTex]

Kroemer, O., Leischnig, S., Luettgen, S., Peters, J. A kernel-based approach to learning contact distributions for robot manipulation tasks Autonomous Robots, 42(3):581-600, March 2018 (article)

DOI Project Page [BibTex]

Approximate Value Iteration Based on Numerical Quadrature

Vinogradska, J., Bischoff, B., Peters, J.

IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

DOI Project Page [BibTex]

Vinogradska, J., Bischoff, B., Peters, J. Approximate Value Iteration Based on Numerical Quadrature IEEE Robotics and Automation Letters, 3(2):1330-1337, January 2018 (article)

DOI Project Page [BibTex]

Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review

Yi, Z., Zhang, Y., Peters, J.

Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

DOI Project Page [BibTex]

Yi, Z., Zhang, Y., Peters, J. Biomimetic Tactile Sensors and Signal Processing with Spike Trains: A Review Sensors and Actuators A: Physical, 269, pages: 41-52, January 2018 (article)

DOI Project Page [BibTex]

Design and Analysis of the NIPS 2016 Review Process

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U.

Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

arXiv link (url) Project Page Project Page [BibTex]

Shah*, N., Tabibian*, B., Muandet, K., Guyon, I., von Luxburg, U. Design and Analysis of the NIPS 2016 Review Process Journal of Machine Learning Research, 19(49):1-34, 2018, *equal contribution (article)

arXiv link (url) Project Page Project Page [BibTex]

Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks

Tanneberg, D., Peters, J., Rueckert, E.

Neural Networks, 109, pages: 67-80, 2018 (article)

link (url) DOI Project Page [BibTex]

Tanneberg, D., Peters, J., Rueckert, E. Intrinsic Motivation and Mental Replay enable Efficient Online Adaptation in Stochastic Recurrent Networks Neural Networks, 109, pages: 67-80, 2018 (article)

link (url) DOI Project Page [BibTex]

A Flexible Approach for Fair Classification

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K.

Journal of Machine Learning, 2018 (article) Accepted

Project Page [BibTex]

Zafar, M. B., Valera, I., Gomez Rodriguez, M., Gummadi, K. A Flexible Approach for Fair Classification Journal of Machine Learning, 2018 (article) Accepted

Project Page [BibTex]

Adaptation and Robust Learning of Probabilistic Movement Primitives

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J.

IEEE Transactions on Robotics, 2018 (article) In revision

arXiv Project Page [BibTex]

Gomez-Gonzalez, S., Neumann, G., Schölkopf, B., Peters, J. Adaptation and Robust Learning of Probabilistic Movement Primitives IEEE Transactions on Robotics, 2018 (article) In revision

arXiv Project Page [BibTex]

Does universal controllability of physical systems prohibit thermodynamic cycles?

Janzing, D., Wocjan, P.

Open Systems and Information Dynamics, 25(3):1850016, 2018 (article)

PDF DOI [BibTex]

Janzing, D., Wocjan, P. Does universal controllability of physical systems prohibit thermodynamic cycles? Open Systems and Information Dynamics, 25(3):1850016, 2018 (article)

PDF DOI [BibTex]

Learning Causality and Causality-Related Learning: Some Recent Progress

Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C.

National Science Review, 5(1):26-29, 2018 (article)

DOI [BibTex]

Zhang, K., Schölkopf, B., Spirtes, P., Glymour, C. Learning Causality and Causality-Related Learning: Some Recent Progress National Science Review, 5(1):26-29, 2018 (article)

DOI [BibTex]

Online optimal trajectory generation for robot table tennis

Koc, O., Maeda, G., Peters, J.

Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

PDF link (url) DOI Project Page [BibTex]

Koc, O., Maeda, G., Peters, J. Online optimal trajectory generation for robot table tennis Robotics and Autonomous Systems, 105, pages: 121-137, 2018 (article)

PDF link (url) DOI Project Page [BibTex]

Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise

Pfister*, N., Weichwald*, S., Bülmann, P., Schölkopf, B.

2018, *equal contribution (article) Submitted

ArXiv Code Project page PDF Project Page Project Page [BibTex]

Pfister*, N., Weichwald*, S., Bülmann, P., Schölkopf, B. Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise 2018, *equal contribution (article) Submitted

ArXiv Code Project page PDF Project Page Project Page [BibTex]

Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S.

Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

Abstract

This paper introduces a novel Hilbert space representation of a counterfactual distribution---called counterfactual mean embedding (CME)---with applications in nonparametric causal inference. Counterfactual prediction has become an ubiquitous tool in machine learning applications, such as online advertisement, recommendation systems, and medical diagnosis, whose performance relies on certain interventions. To infer the outcomes of such interventions, we propose to embed the associated counterfactual distribution into a reproducing kernel Hilbert space (RKHS) endowed with a positive definite kernel. Under appropriate assumptions, the CME allows us to perform causal inference over the entire landscape of the counterfactual distribution. The CME can be estimated consistently from observational data without requiring any parametric assumption about the underlying distributions. We also derive a rate of convergence which depends on the smoothness of the conditional mean and the Radon-Nikodym derivative of the underlying marginal distributions. Our framework can deal with not only real-valued outcome, but potentially also more complex and structured outcomes such as images, sequences, and graphs. Lastly, our experimental results on off-policy evaluation tasks demonstrate the advantages of the proposed estimator.

arXiv [BibTex]

Muandet, K., Kanagawa, M., Saengkyongam, S., Marukata, S. Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference Arxiv e-prints, arXiv:1805.08845v1 [stat.ML], 2018 (article)

arXiv [BibTex]

Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions

Osa, T., Peters, J., Neumann, G.

Advanced Robotics, 32(18):955-968, 2018 (article)

DOI Project Page [BibTex]

Osa, T., Peters, J., Neumann, G. Hierarchical Reinforcement Learning of Multiple Grasping Strategies with Human Instructions Advanced Robotics, 32(18):955-968, 2018 (article)

DOI Project Page [BibTex]

Autofocusing-based phase correction

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K.

Magnetic Resonance in Medicine, 80(3):958-968, 2018 (article)

DOI [BibTex]

Loktyushin, A., Ehses, P., Schölkopf, B., Scheffler, K. Autofocusing-based phase correction Magnetic Resonance in Medicine, 80(3):958-968, 2018 (article)

DOI [BibTex]

Case series: Slowing alpha rhythm in late-stage ALS patients

Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M.

Clinical Neurophysiology, 129(2):406-408, 2018 (article)

DOI Project Page [BibTex]

Hohmann, M. R., Fomina, T., Jayaram, V., Emde, T., Just, J., Synofzik, M., Schölkopf, B., Schöls, L., Grosse-Wentrup, M. Case series: Slowing alpha rhythm in late-stage ALS patients Clinical Neurophysiology, 129(2):406-408, 2018 (article)

DOI Project Page [BibTex]

Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H.

Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

link (url) Project Page [BibTex]

Šošić, A., Rueckert, E., Peters, J., Zoubir, A., Koeppl, H. Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling Journal of Machine Learning Research, 19(69):1-45, 2018 (article)

link (url) Project Page [BibTex]

Grip Stabilization of Novel Objects using Slip Prediction

Veiga, F., Peters, J., Hermans, T.

IEEE Transactions on Haptics, 2018 (article) In press

DOI Project Page [BibTex]

Veiga, F., Peters, J., Hermans, T. Grip Stabilization of Novel Objects using Slip Prediction IEEE Transactions on Haptics, 2018 (article) In press

DOI Project Page [BibTex]

Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T.

Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

link (url) [BibTex]

Kellmeyer, P., Grosse-Wentrup, M., Schulze-Bonhage, A., Ziemann, U., Ball, T. Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis—implications for brain–computer interfacing Journal of Neural Engineering, 15(4):041003, IOP Publishing, 2018 (article)

link (url) [BibTex]

Quantum machine learning: a classical perspective

Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., Wossnig, L.

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2209):20170551, 2018 (article)

link (url) DOI [BibTex]

Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., Wossnig, L. Quantum machine learning: a classical perspective Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 474(2209):20170551, 2018 (article)

link (url) DOI [BibTex]

Maschinelles Lernen: Entwicklung ohne Grenzen?

Schökopf, B.

In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

Schökopf, B. Maschinelles Lernen: Entwicklung ohne Grenzen? In Mit Optimismus in die Zukunft schauen. Künstliche Intelligenz - Chancen und Rahmenbedingungen, pages: 26-34, (Editors: Bender, G. and Herbrich, R. and Siebenhaar, K.), B&S Siebenhaar Verlag, 2018 (incollection)

[BibTex]

Kernel-based tests for joint independence

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J.

Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

DOI [BibTex]

Pfister, N., Bühlmann, P., Schölkopf, B., Peters, J. Kernel-based tests for joint independence Journal of the Royal Statistical Society: Series B (Statistical Methodology), 80(1):5-31, 2018 (article)

DOI [BibTex]

Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R.

Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

DOI Project Page [BibTex]

Babbar, R., Heni, M., Peter, A., Hrabě de Angelis, M., Häring, H., Fritsche, A., Preissl, H., Schölkopf, B., Wagner, R. Prediction of Glucose Tolerance without an Oral Glucose Tolerance Test Frontiers in Endocrinology, 9, pages: 82, 2018 (article)

DOI Project Page [BibTex]

Invariant Models for Causal Transfer Learning

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J.

Journal of Machine Learning Research, 19(36):1-34, 2018 (article)

link (url) [BibTex]

Rojas-Carulla, M., Schölkopf, B., Turner, R., Peters, J. Invariant Models for Causal Transfer Learning Journal of Machine Learning Research, 19(36):1-34, 2018 (article)

link (url) [BibTex]

MOABB: Trustworthy algorithm benchmarking for BCIs

Jayaram, V., Barachant, A.

Journal of Neural Engineering, 15(6):066011, 2018 (article)

link (url) DOI Project Page [BibTex]

Jayaram, V., Barachant, A. MOABB: Trustworthy algorithm benchmarking for BCIs Journal of Neural Engineering, 15(6):066011, 2018 (article)

link (url) DOI Project Page [BibTex]

f-Divergence constrained policy improvement

Belousov, B., Peters, J.

Journal of Machine Learning Research, 2018 (article) Submitted

Project Page [BibTex]

Belousov, B., Peters, J. f-Divergence constrained policy improvement Journal of Machine Learning Research, 2018 (article) Submitted

Project Page [BibTex]

Phylogenetic convolutional neural networks in metagenomics

Fioravanti*, D., Giarratano*, Y., Maggio*, V., Agostinelli, C., Chierici, M., Jurman, G., Furlanello, C.

BMC Bioinformatics, 19(2):49 pages, 2018, *equal contribution (article)

DOI [BibTex]

Fioravanti*, D., Giarratano*, Y., Maggio*, V., Agostinelli, C., Chierici, M., Jurman, G., Furlanello, C. Phylogenetic convolutional neural networks in metagenomics BMC Bioinformatics, 19(2):49 pages, 2018, *equal contribution (article)

DOI [BibTex]

Food specific inhibitory control under negative mood in binge-eating disorder: Evidence from a multimethod approach

Leehr, E. J., Schag, K., Dresler, T., Grosse-Wentrup, M., Hautzinger, M., Fallgatter, A. J., Zipfel, S., Giel, K. E., Ehlis, A.

International Journal of Eating Disorders, 51(2):112-123, Wiley Online Library, 2018 (article)

DOI [BibTex]

Leehr, E. J., Schag, K., Dresler, T., Grosse-Wentrup, M., Hautzinger, M., Fallgatter, A. J., Zipfel, S., Giel, K. E., Ehlis, A. Food specific inhibitory control under negative mood in binge-eating disorder: Evidence from a multimethod approach International Journal of Eating Disorders, 51(2):112-123, Wiley Online Library, 2018 (article)

DOI [BibTex]

Linking imaging to omics utilizing image-guided tissue extraction

Disselhorst, J. A., Krueger, M. A., Ud-Dean, S. M. M., Bezrukov, I., Jarboui, M. A., Trautwein, C., Traube, A., Spindler, C., Cotton, J. M., Leibfritz, D., Pichler, B. J.

Proceedings of the National Academy of Sciences, 115(13):E2980-E2987, 2018 (article)

DOI [BibTex]

Disselhorst, J. A., Krueger, M. A., Ud-Dean, S. M. M., Bezrukov, I., Jarboui, M. A., Trautwein, C., Traube, A., Spindler, C., Cotton, J. M., Leibfritz, D., Pichler, B. J. Linking imaging to omics utilizing image-guided tissue extraction Proceedings of the National Academy of Sciences, 115(13):E2980-E2987, 2018 (article)

DOI [BibTex]

Methods in Psychophysics

Wichmann, F. A., Jäkel, F.

In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

[BibTex]

Wichmann, F. A., Jäkel, F. Methods in Psychophysics In Stevens’ Handbook of Experimental Psychology and Cognitive Neuroscience, 5 (Methodology), 7, 4th, John Wiley & Sons, Inc., 2018 (inbook)

[BibTex]

Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions

Simon-Gabriel, C. J., Schölkopf, B.

Journal of Machine Learning Research, 19(44):1-29, 2018 (article)

URL arXiv_long version Project Page [BibTex]

Simon-Gabriel, C. J., Schölkopf, B. Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions Journal of Machine Learning Research, 19(44):1-29, 2018 (article)

URL arXiv_long version Project Page [BibTex]

Discriminative Transfer Learning for General Image Restoration

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M.

IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

DOI [BibTex]

Xiao, L., Heide, F., Heidrich, W., Schölkopf, B., Hirsch, M. Discriminative Transfer Learning for General Image Restoration IEEE Transactions on Image Processing, 27(8):4091-4104, 2018 (article)

DOI [BibTex]

Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples

Ramirez-Villegas, J. F., Willeke, K. F., Logothetis, N. K., Besserve, M.

Neuron, 100(5):1224-1240, 2018 (article)

link (url) DOI Project Page [BibTex]

Ramirez-Villegas, J. F., Willeke, K. F., Logothetis, N. K., Besserve, M. Dissecting the synapse- and frequency-dependent network mechanisms of in vivo hippocampal sharp wave-ripples Neuron, 100(5):1224-1240, 2018 (article)

link (url) DOI Project Page [BibTex]

In-Hand Object Stabilization by Independent Finger Control

Veiga, F. F., Edin, B. B., Peters, J.

IEEE Transactions on Robotics, 2018 (article) Submitted

Project Page [BibTex]

Veiga, F. F., Edin, B. B., Peters, J. In-Hand Object Stabilization by Independent Finger Control IEEE Transactions on Robotics, 2018 (article) Submitted

Project Page [BibTex]

Visualizing and understanding Sum-Product Networks

Vergari, A., Di Mauro, N., Esposito, F.

Machine Learning, 2018 (article)

DOI [BibTex]

Vergari, A., Di Mauro, N., Esposito, F. Visualizing and understanding Sum-Product Networks Machine Learning, 2018 (article)

DOI [BibTex]

Transfer Learning for BCIs

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M.

In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

Project Page [BibTex]

Jayaram, V., Fiebig, K., Peters, J., Grosse-Wentrup, M. Transfer Learning for BCIs In Brain–Computer Interfaces Handbook, pages: 425-442, 22, (Editors: Chang S. Nam, Anton Nijholt and Fabien Lotte), CRC Press, 2018 (incollection)

Project Page [BibTex]

Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments

Grau-Moya, J, Krüger, M, Braun, DA

Entropy, 20(1:1):1-28, January 2018 (article)

Abstract

Living organisms from single cells to humans need to adapt continuously to respond to changes in their environment. The process of behavioural adaptation can be thought of as improving decision-making performance according to some utility function. Here, we consider an abstract model of organisms as decision-makers with limited information-processing resources that trade off between maximization of utility and computational costs measured by a relative entropy, in a similar fashion to thermodynamic systems undergoing isothermal transformations. Such systems minimize the free energy to reach equilibrium states that balance internal energy and entropic cost. When there is a fast change in the environment, these systems evolve in a non-equilibrium fashion because they are unable to follow the path of equilibrium distributions. Here, we apply concepts from non-equilibrium thermodynamics to characterize decision-makers that adapt to changing environments under the assumption that the temporal evolution of the utility function is externally driven and does not depend on the decision-maker’s action. This allows one to quantify performance loss due to imperfect adaptation in a general manner and, additionally, to find relations for decision-making similar to Crooks’ fluctuation theorem and Jarzynski’s equality. We provide simulations of several exemplary decision and inference problems in the discrete and continuous domains to illustrate the new relations.

DOI [BibTex]

Grau-Moya, J, Krüger, M, Braun, DA Non-Equilibrium Relations for Bounded Rational Decision-Making in Changing Environments Entropy, 20(1:1):1-28, January 2018 (article)

DOI [BibTex]

2002

Constructing Boosting algorithms from SVMs: an application to one-class classification.

Rätsch, G., Mika, S., Schölkopf, B., Müller, K.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, September 2002 (article)

Abstract

We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation procedure for a new algorithmone-class leveragingstarting from the one-class support vector machine (1-SVM). This is a first step toward unsupervised learning in a boosting framework. Building on so-called barrier methods known from the theory of constrained optimization, it returns a function, written as a convex combination of base hypotheses, that characterizes whether a given test point is likely to have been generated from the distribution underlying the training data. Simulations on one-class classification problems demonstrate the usefulness of our approach.

DOI [BibTex]

2002

Rätsch, G., Mika, S., Schölkopf, B., Müller, K. Constructing Boosting algorithms from SVMs: an application to one-class classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(9):1184-1199, September 2002 (article)

DOI [BibTex]

The contributions of color to recognition memory for natural scenes

Wichmann, F., Sharpe, L., Gegenfurtner, K.

Journal of Experimental Psychology: Learning, Memory and Cognition, 28(3):509-520, May 2002 (article)

Abstract

The authors used a recognition memory paradigm to assess the influence of color information on visual memory for images of natural scenes. Subjects performed 5-10% better for colored than for black-and-white images independent of exposure duration. Experiment 2 indicated little influence of contrast once the images were suprathreshold, and Experiment 3 revealed that performance worsened when images were presented in color and tested in black and white, or vice versa, leading to the conclusion that the surface property color is part of the memory representation. Experiments 4 and 5 exclude the possibility that the superior recognition memory for colored images results solely from attentional factors or saliency. Finally, the recognition memory advantage disappears for falsely colored images of natural scenes: The improvement in recognition memory depends on the color congruence of presented images with learned knowledge about the color gamut found within natural scenes. The results can be accounted for within a multiple memory systems framework.

PDF Web DOI [BibTex]

Wichmann, F., Sharpe, L., Gegenfurtner, K. The contributions of color to recognition memory for natural scenes Journal of Experimental Psychology: Learning, Memory and Cognition, 28(3):509-520, May 2002 (article)

PDF Web DOI [BibTex]

Training invariant support vector machines

DeCoste, D., Schölkopf, B.

Machine Learning, 46(1-3):161-190, January 2002 (article)

Abstract

Practical experience has shown that in order to obtain the best possible performance, prior knowledge about invariances of a classification problem at hand ought to be incorporated into the training procedure. We describe and review all known methods for doing so in support vector machines, provide experimental results, and discuss their respective merits. One of the significant new results reported in this work is our recent achievement of the lowest reported test error on the well-known MNIST digit recognition benchmark task, with SVM training times that are also significantly faster than previous SVM methods.

PDF DOI [BibTex]

DeCoste, D., Schölkopf, B. Training invariant support vector machines Machine Learning, 46(1-3):161-190, January 2002 (article)

PDF DOI [BibTex]

Contrast discrimination with sinusoidal gratings of different spatial frequency

Bird, C., Henning, G., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1267-1273, 2002 (article)

Abstract

The detectability of contrast increments was measured as a function of the contrast of a masking or pedestal grating at a number of different spatial frequencies ranging from 2 to 16 cycles per degree of visual angle. The pedestal grating always had the same orientation, spatial frequency and phase as the signal. The shape of the contrast increment threshold versus pedestal contrast (TvC) functions depend of the performance level used to define the threshold, but when both axes are normalized by the contrast corresponding to 75% correct detection at each frequency, the (TvC) functions at a given performance level are identical. Confidence intervals on the slope of the rising part of the TvC functions are so wide that it is not possible with our data to reject Webers Law.

PDF [BibTex]

Bird, C., Henning, G., Wichmann, F. Contrast discrimination with sinusoidal gratings of different spatial frequency Journal of the Optical Society of America A, 19(7), pages: 1267-1273, 2002 (article)

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Support Vector Machines and Kernel Methods: The New Generation of Learning Machines

Cristianini, N., Schölkopf, B.

AI Magazine, 23(3):31-41, 2002 (article)

[BibTex]

Cristianini, N., Schölkopf, B. Support Vector Machines and Kernel Methods: The New Generation of Learning Machines AI Magazine, 23(3):31-41, 2002 (article)

[BibTex]

Contrast discrimination with pulse-trains in pink noise

Henning, G., Bird, C., Wichmann, F.

Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

Abstract

Detection performance was measured with sinusoidal and pulse-train gratings. Although the 2.09-c/deg pulse-train, or line gratings, contained at least 8 harmonics all at equal contrast, they were no more detectable than their most detectable component. The addition of broadband pink noise designed to equalize the detectability of the components of the pulse train made the pulse train about a factor of four more detectable than any of its components. However, in contrast-discrimination experiments, with a pedestal or masking grating of the same form and phase as the signal and 15% contrast, the noise did not affect the discrimination performance of the pulse train relative to that obtained with its sinusoidal components. We discuss the implications of these observations for models of early vision in particular the implications for possible sources of internal noise.

PDF [BibTex]

Henning, G., Bird, C., Wichmann, F. Contrast discrimination with pulse-trains in pink noise Journal of the Optical Society of America A, 19(7), pages: 1259-1266, 2002 (article)

PDF [BibTex]

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