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Empirical Inference Conference Paper Abstraction in Decision-Makers with Limited Information Processing Capabilities Genewein, T., Braun, D. 1-9, NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013
A distinctive property of human and animal intelligence is the ability to form abstractions by neglecting irrelevant information which allows to separate structure from noise. From an information theoretic point of view abstractions are desirable because they allow for very efficient information processing. In artificial systems abstractions are often implemented through computationally costly formations of groups or clusters. In this work we establish the relation between the free-energy framework for decision-making and rate-distortion theory and demonstrate how the application of rate-distortion for decision-making leads to the emergence of abstractions. We argue that abstractions are induced due to a limit in information processing capacity.
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Empirical Inference Conference Paper Bounded Rational Decision-Making in Changing Environments Grau-Moya, J., Braun, D. 1-9, NIPS 2013 Workshop Planning with Information Constraints for Control, Reinforcement Learning, Computational Neuroscience, Robotics and Games, December 2013
A perfectly rational decision-maker chooses the best action with the highest utility gain from a set of possible actions. The optimality principles that describe such decision processes do not take into account the computational costs of finding the optimal action. Bounded rational decision-making addresses this problem by specifically trading off information-processing costs and expected utility. Interestingly, a similar trade-off between energy and entropy arises when describing changes in thermodynamic systems. This similarity has been recently used to describe bounded rational agents. Crucially, this framework assumes that the environment does not change while the decision-maker is computing the optimal policy. When this requirement is not fulfilled, the decision-maker will suffer inefficiencies in utility, that arise because the current policy is optimal for an environment in the past. Here we borrow concepts from non-equilibrium thermodynamics to quantify these inefficiencies and illustrate with simulations its relationship with computational resources.
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Empirical Inference Probabilistic Numerics Thesis Camera-specific Image Denoising Schober, M. Eberhard Karls Universität Tübingen, Germany, October 2013 (Published) PDF BibTeX
Empirical Inference Article Structural learning Braun, D. Scholarpedia, 8(10):12312, October 2013
Structural learning in motor control refers to a metalearning process whereby an agent extracts (abstract) invariants from its sensorimotor stream when experiencing a range of environments that share similar structure. Such invariants can then be exploited for faster generalization and learning-to-learn when experiencing novel, but related task environments.
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Empirical Inference Article The effect of model uncertainty on cooperation in sensorimotor interactions Grau-Moya, J., Hez, E., Pezzulo, G., Braun, D. Journal of the Royal Society Interface, 10(87):1-11, October 2013
Decision-makers have been shown to rely on probabilistic models for perception and action. However, these models can be incorrect or partially wrong in which case the decision-maker has to cope with model uncertainty. Model uncertainty has recently also been shown to be an important determinant of sensorimotor behaviour in humans that can lead to risk-sensitive deviations from Bayes optimal behaviour towards worst-case or best-case outcomes. Here, we investigate the effect of model uncertainty on cooperation in sensorimotor interactions similar to the stag-hunt game, where players develop models about the other player and decide between a pay-off-dominant cooperative solution and a risk-dominant, non-cooperative solution. In simulations, we show that players who allow for optimistic deviations from their opponent model are much more likely to converge to cooperative outcomes. We also implemented this agent model in a virtual reality environment, and let human subjects play against a virtual player. In this game, subjects' pay-offs were experienced as forces opposing their movements. During the experiment, we manipulated the risk sensitivity of the computer player and observed human responses. We found not only that humans adaptively changed their level of cooperation depending on the risk sensitivity of the computer player but also that their initial play exhibited characteristic risk-sensitive biases. Our results suggest that model uncertainty is an important determinant of cooperation in two-player sensorimotor interactions.
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Empirical Inference Talk 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}, Web BibTeX
Empirical Inference Article Correlation of Simultaneously Acquired Diffusion-Weighted Imaging and 2-Deoxy-[18F] fluoro-2-D-glucose Positron Emission Tomography of Pulmonary Lesions in a Dedicated Whole-Body Magnetic Resonance/Positron Emission Tomography System Schmidt, H., Brendle, C., Schraml, C., Martirosian, P., Bezrukov, I., Hetzel, J., Müller, M., Sauter, A., Claussen, C., Pfannenberg, C., Schwenzer, N. Investigative Radiology, 48(5):247-255, May 2013 Web BibTeX
Empirical Inference Article Replacing Causal Faithfulness with Algorithmic Independence of Conditionals Lemeire, J., Janzing, D. Minds and Machines, 23(2):227-249, May 2013
Independence of Conditionals (IC) has recently been proposed as a basic rule for causal structure learning. If a Bayesian network represents the causal structure, its Conditional Probability Distributions (CPDs) should be algorithmically independent. In this paper we compare IC with causal faithfulness (FF), stating that only those conditional independences that are implied by the causal Markov condition hold true. The latter is a basic postulate in common approaches to causal structure learning. The common spirit of FF and IC is to reject causal graphs for which the joint distribution looks ‘non-generic’. The difference lies in the notion of genericity: FF sometimes rejects models just because one of the CPDs is simple, for instance if the CPD describes a deterministic relation. IC does not behave in this undesirable way. It only rejects a model when there is a non-generic relation between different CPDs although each CPD looks generic when considered separately. Moreover, it detects relations between CPDs that cannot be captured by conditional independences. IC therefore helps in distinguishing causal graphs that induce the same conditional independences (i.e., they belong to the same Markov equivalence class). The usual justification for FF implicitly assumes a prior that is a probability density on the parameter space. IC can be justified by Solomonoff’s universal prior, assigning non-zero probability to those points in parameter space that have a finite description. In this way, it favours simple CPDs, and therefore respects Occam’s razor. Since Kolmogorov complexity is uncomputable, IC is not directly applicable in practice. We argue that it is nevertheless helpful, since it has already served as inspiration and justification for novel causal inference algorithms.
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Empirical Inference Article Apprenticeship Learning with Few Examples Boularias, A., Chaib-draa, B. Neurocomputing, 104:83-96, March 2013
We consider the problem of imitation learning when the examples, provided by an expert human, are scarce. Apprenticeship learning via inverse reinforcement learning provides an efficient tool for generalizing the examples, based on the assumption that the expert's policy maximizes a value function, which is a linear combination of state and action features. Most apprenticeship learning algorithms use only simple empirical averages of the features in the demonstrations as a statistics of the expert's policy. However, this method is efficient only when the number of examples is sufficiently large to cover most of the states, or the dynamics of the system is nearly deterministic. In this paper, we show that the quality of the learned policies is sensitive to the error in estimating the averages of the features when the dynamics of the system is stochastic. To reduce this error, we introduce two new approaches for bootstrapping the demonstrations by assuming that the expert is near-optimal and the dynamics of the system is known. In the first approach, the expert's examples are used to learn a reward function and to generate furthermore examples from the corresponding optimal policy. The second approach uses a transfer technique, known as graph homomorphism, in order to generalize the expert's actions to unvisited regions of the state space. Empirical results on simulated robot navigation problems show that our approach is able to learn sufficiently good policies from a significantly small number of examples.
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Perceiving Systems Empirical Inference Probabilistic Numerics Article Quasi-Newton Methods: A New Direction Hennig, P., Kiefel, M. Journal of Machine Learning Research, 14(1):843-865, March 2013
Four decades after their invention, quasi-Newton methods are still state of the art in unconstrained numerical optimization. Although not usually interpreted thus, these are learning algorithms that fit a local quadratic approximation to the objective function. We show that many, including the most popular, quasi-Newton methods can be interpreted as approximations of Bayesian linear regression under varying prior assumptions. This new notion elucidates some shortcomings of classical algorithms, and lights the way to a novel nonparametric quasi-Newton method, which is able to make more efficient use of available information at computational cost similar to its predecessors.
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Empirical Inference Article What can neurons do for their brain? Communicate selectivity with bursts Balduzzi, D., Tononi, G. Theory in Biosciences , 132(1):27-39, Springer, March 2013 (Published)
Neurons deep in cortex interact with the environment extremely indirectly; the spikes they receive and produce are pre- and post-processed by millions of other neurons. This paper proposes two information-theoretic constraints guiding the production of spikes, that help ensure bursting activity deep in cortex relates meaningfully to events in the environment. First, neurons should emphasize selective responses with bursts. Second, neurons should propagate selective inputs by burst-firing in response to them. We show the constraints are necessary for bursts to dominate information-transfer within cortex, thereby providing a substrate allowing neurons to distribute credit amongst themselves. Finally, since synaptic plasticity degrades the ability of neurons to burst selectively, we argue that homeostatic regulation of synaptic weights is necessary, and that it is best performed offline during sleep.
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Empirical Inference Article Regional effects of magnetization dispersion on quantitative perfusion imaging for pulsed and continuous arterial spin labeling Cavusoglu, M., Pohmann, R., Burger, H. C., Uludag, K. Magnetic Resonance in Medicine, 69(2):524-530, February 2013
Most experiments assume a global transit delay time with blood flowing from the tagging region to the imaging slice in plug flow without any dispersion of the magnetization. However, because of cardiac pulsation, nonuniform cross-sectional flow profile, and complex vessel networks, the transit delay time is not a single value but follows a distribution. In this study, we explored the regional effects of magnetization dispersion on quantitative perfusion imaging for varying transit times within a very large interval from the direct comparison of pulsed, pseudo-continuous, and dual-coil continuous arterial spin labeling encoding schemes. Longer distances between tagging and imaging region typically used for continuous tagging schemes enhance the regional bias on the quantitative cerebral blood flow measurement causing an underestimation up to 37% when plug flow is assumed as in the standard model.
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Empirical Inference Article The multivariate Watson distribution: Maximum-likelihood estimation and other aspects Sra, S., Karp, D. Journal of Multivariate Analysis, 114:256-269, February 2013
This paper studies fundamental aspects of modelling data using multivariate Watson distributions. Although these distributions are natural for modelling axially symmetric data (i.e., unit vectors where View the MathML source are equivalent), for high-dimensions using them can be difficult—largely because for Watson distributions even basic tasks such as maximum-likelihood are numerically challenging. To tackle the numerical difficulties some approximations have been derived. But these are either grossly inaccurate in high-dimensions [K.V. Mardia, P. Jupp, Directional Statistics, second ed., John Wiley & Sons, 2000] or when reasonably accurate [A. Bijral, M. Breitenbach, G.Z. Grudic, Mixture of Watson distributions: a generative model for hyperspherical embeddings, in: Artificial Intelligence and Statistics, AISTATS 2007, 2007, pp. 35–42], they lack theoretical justification. We derive new approximations to the maximum-likelihood estimates; our approximations are theoretically well-defined, numerically accurate, and easy to compute. We build on our parameter estimation and discuss mixture-modelling with Watson distributions; here we uncover a hitherto unknown connection to the “diametrical clustering” algorithm of Dhillon et al. [I.S. Dhillon, E.M. Marcotte, U. Roshan, Diametrical clustering for identifying anticorrelated gene clusters, Bioinformatics 19 (13) (2003) 1612–1619].
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Empirical Inference Article Explicit eigenvalues of certain scaled trigonometric matrices Sra, S. Linear Algebra and its Applications, 438(1):173-181, January 2013 (Published) DOI BibTeX
Empirical Inference Conference Paper Falsification and future performance Balduzzi, D. In Algorithmic Probability and Friends. Bayesian Prediction and Artificial Intelligence, 7070:65-78, Lecture Notes in Computer Science, Springer, Berlin, Germany, Solomonoff 85th Memorial Conference, January 2013 (Published)
We information-theoretically reformulate two measures of capacity from statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. We show these capacity measures count the number of hypotheses about a dataset that a learning algorithm falsifies when it finds the classifier in its repertoire minimizing empirical risk. It then follows from that the future performance of predictors on unseen data is controlled in part by how many hypotheses the learner falsifies. As a corollary we show that empirical VC-entropy quantifies the message length of the true hypothesis in the optimal code of a particular probability distribution, the so-called actual repertoire.
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Empirical Inference Article How Sensitive Is the Human Visual System to the Local Statistics of Natural Images? Gerhard, H., Wichmann, F., Bethge, M. PLoS Computational Biology, 9(1):e1002873, January 2013
Several aspects of primate visual physiology have been identified as adaptations to local regularities of natural images. However, much less work has measured visual sensitivity to local natural image regularities. Most previous work focuses on global perception of large images and shows that observers are more sensitive to visual information when image properties resemble those of natural images. In this work we measure human sensitivity to local natural image regularities using stimuli generated by patch-based probabilistic natural image models that have been related to primate visual physiology. We find that human observers can learn to discriminate the statistical regularities of natural image patches from those represented by current natural image models after very few exposures and that discriminability depends on the degree of regularities captured by the model. The quick learning we observed suggests that the human visual system is biased for processing natural images, even at very fine spatial scales, and that it has a surprisingly large knowledge of the regularities in natural images, at least in comparison to the state-of-the-art statistical models of natural images.
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Empirical Inference Article How the result of graph clustering methods depends on the construction of the graph Maier, M., von Luxburg, U., Hein, M. ESAIM: Probability & Statistics, 17:370-418, January 2013 (Published)
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one rst has to construct a graph on the data points and then apply a graph clustering algorithm to nd a suitable partition of the graph. Our main question is if and how the construction of the graph (choice of the graph, choice of parameters, choice of weights) in uences the outcome of the nal clustering result. To this end we study the convergence of cluster quality measures such as the normalized cut or the Cheeger cut on various kinds of random geometric graphs as the sample size tends to in nity. It turns out that the limit values of the same objective function are systematically di erent on di erent types of graphs. This implies that clustering results systematically depend on the graph and can be very di erent for di erent types of graph. We provide examples to illustrate the implications on spectral clustering.
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Empirical Inference Conference Paper A Guided Hybrid Genetic Algorithm for Feature Selection with Expensive Cost Functions Jung, M., Zscheischler, J. In Proceedings of the International Conference on Computational Science, 18:2337 - 2346, Procedia Computer Science, (Editors: Alexandrov, V and Lees, M and Krzhizhanovskaya, V and Dongarra, J and Sloot, PMA), Elsevier, Amsterdam, Netherlands, ICCS, 2013 Web DOI BibTeX
Empirical Inference Book Chapter A Review of Performance Variations in SMR-Based Brain–Computer Interfaces (BCIs) Grosse-Wentrup, M., Schölkopf, B. In Brain-Computer Interface Research, 39-51, 4, SpringerBriefs in Electrical and Computer Engineering, (Editors: Guger, C., Allison, B. Z. and Edlinger, G.), Springer, 2013 PDF DOI BibTeX
Empirical Inference Article A Survey on Policy Search for Robotics, Foundations and Trends in Robotics Deisenroth, M., Neumann, G., Peters, J. Foundations and Trends in Robotics, 2(1-2):1-142, 2013 DOI BibTeX
Empirical Inference Conference Paper A machine learning approach for non-blind image deconvolution Schuler, C., Burger, H. C., Harmeling, S., Schölkopf, B. In IEEE Conference on Computer Vision and Pattern Recognition, 1067-1074, IEEE, CVPR, 2013 Web Web DOI BibTeX
Empirical Inference Article A neural population model for visual pattern detection Goris, R., Putzeys, T., Wagemans, J., Wichmann, F. Psychological Review, 120(3):472–496, 2013 DOI BibTeX
Empirical Inference Conference Paper A probabilistic approach to robot trajectory generation Paraschos, A., Neumann, G., Peters, J. In Proceedings of the 13th IEEE International Conference on Humanoid Robots (HUMANOIDS), 477-483, IEEE, 13th IEEE-RAS International Conference on Humanoid Robots, 2013 DOI BibTeX
Empirical Inference Article A probabilistic model for secondary structure prediction from protein chemical shifts Mechelke, M., Habeck, M. Proteins: Structure, Function, and Bioinformatics, 81(6):984–993, 2013 (Published) DOI BibTeX
Empirical Inference Article Accurate detection of differential RNA processing Drewe, P., Stegle, O., Hartmann, L., Kahles, A., Bohnert, R., Wachter, A., Borgwardt, K. M., Rätsch, G. Nucleic Acids Research, 41(10):5189-5198, 2013 (Published) DOI BibTeX
Empirical Inference Article Accurate indel prediction using paired-end short reads Grimm, D., Hagmann, J., Koenig, D., Weigel, D., Borgwardt, K. BMC Genomics, 14(132), 2013 Web DOI BibTeX
Empirical Inference Conference Paper Adaptivity to Local Smoothness and Dimension in Kernel Regression Kpotufe, S., Garg, V. In Advances in Neural Information Processing Systems 26, 3075-3083, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), 2013 PDF BibTeX
Empirical Inference Conference Paper Alignment-based Transfer Learning for Robot Models Bocsi, B., Csato, L., Peters, J. In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), 1-7, 2013 PDF DOI BibTeX
Empirical Inference Probabilistic Numerics Article Analytical probabilistic modeling for radiation therapy treatment planning Bangert, M., Hennig, P., Oelfke, U. Physics in Medicine and Biology, 58(16):5401-5419, 2013 PDF DOI BibTeX
Empirical Inference Probabilistic Numerics Conference Paper Analytical probabilistic proton dose calculation and range uncertainties Bangert, M., Hennig, P., Oelfke, U. In 17th International Conference on the Use of Computers in Radiation Therapy, 6-11, (Editors: A. Haworth and T. Kron), ICCR, 2013 BibTeX
Empirical Inference Poster Analyzing locking of spikes to spatio-temporal patterns in the macaque prefrontal cortex Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M. Bernstein Conference, 2013 DOI BibTeX
Empirical Inference Probabilistic Numerics Technical Report Animating Samples from Gaussian Distributions Hennig, P. (8), Max Planck Institute for Intelligent Systems, Tübingen, Germany, 2013 PDF BibTeX
Empirical Inference Conference Paper Auto-Calibrating Spherical Deconvolution Based on ODF Sparsity Schultz, T., Gröschel, S. In Proceedings of Medical Image Computing and Computer-Assisted Intervention, Part I, 663-670, (Editors: K Mori and I Sakuma and Y Sato and C Barillot and N Navab), Springer, MICCAI, 2013, Lecture Notes in Computer Science, vol. 8149 DOI BibTeX
Empirical Inference Conference Paper Automatic Malaria Diagnosis system Mehrjou, A., Abbasian, T., Izadi, M. In First RSI/ISM International Conference on Robotics and Mechatronics (ICRoM), 205-211, 2013 (Published) DOI BibTeX
Empirical Inference Conference Paper Autonomous Reinforcement Learning with Hierarchical REPS Daniel, C., Neumann, G., Peters, J. In Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN 2013), 1-8, 2013 DOI BibTeX
Empirical Inference Article Blind Retrospective Motion Correction of MR Images Loktyushin, A., Nickisch, H., Pohmann, R., Schölkopf, B. Magnetic Resonance in Medicine (MRM), 70(6):1608–1618, 2013 DOI BibTeX
Empirical Inference Conference Paper Causal Inference on Time Series using Restricted Structural Equation Models Peters, J., Janzing, D., Schölkopf, B. In Advances in Neural Information Processing Systems 26, 154-162, (Editors: C.J.C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), 2013 PDF BibTeX
Empirical Inference Poster Characterization of different types of sharp-wave ripple signatures in the CA1 of the macaque hippocampus Ramirez-Villegas, J., Logothetis, N., Besserve, M. 4th German Neurophysiology PhD Meeting Networks, 2013 Web BibTeX
Empirical Inference Article Climate Extremes and the Carbon Cycle Reichstein, M., Bahn, M., Ciais, P., Frank, D., Mahecha, M., Seneviratne, S., Zscheischler, J., Beer, C., Buchmann, N., Frank, D., Papale, D., Rammig, A., Smith, P., Thonicke, K., van der Velde, M., Vicca, S., Walz, A., Wattenbach, M. Nature, 500:287-295, 2013 DOI BibTeX
Empirical Inference Article Counterfactual Reasoning and Learning Systems: The Example of Computational Advertising Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D., Chickering, D., Portugualy, E., Ray, D., Simard, P., Snelson, E. Journal of Machine Learning Research, 14:3207-3260, 2013 (Published) Web URL BibTeX
Empirical Inference Poster Coupling between spiking activity and beta band spatio-temporal patterns in the macaque PFC Safavi, S., Panagiotaropoulos, T., Kapoor, V., Logothetis, N., Besserve, M. 43rd Annual Meeting of the Society for Neuroscience (Neuroscience 2013), 2013 BibTeX
Empirical Inference Conference Paper Data-Efficient Generalization of Robot Skills with Contextual Policy Search Kupcsik, A., Deisenroth, M., Peters, J., Neumann, G. In Proceedings of the 27th National Conference on Artificial Intelligence (AAAI 2013), (Editors: desJardins, M. and Littman, M. L.), AAAI Press, 2013 PDF BibTeX
Empirical Inference Conference Paper Density estimation from unweighted k-nearest neighbor graphs: a roadmap von Luxburg, U., Alamgir, M. In Advances in Neural Information Processing Systems 26, 225-233, (Editors: C.J.C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K.Q. Weinberger), 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), 2013 PDF BibTeX
Empirical Inference Article Detecting regulatory gene–environment interactions with unmeasured environmental factors Fusi, N., Lippert, C., Borgwardt, K. M., Lawrence, N. D., Stegle, O. Bioinformatics, 29(11):1382-1389, 2013 (Published) DOI BibTeX
Empirical Inference Article Detection and attribution of large spatiotemporal extreme events in Earth observation data Zscheischler, J., Mahecha, M., Harmeling, S., Reichstein, M. Ecological Informatics, 15:66-73, 2013
Latest climate projections suggest that both frequency and intensity of climate extremes will be substantially modified over the course of the coming decades. As a consequence, we need to understand to what extent and via which pathways climate extremes affect the state and functionality of terrestrial ecosystems and the associated biogeochemical cycles on a global scale. So far the impacts of climate extremes on the terrestrial biosphere were mainly investigated on the basis of case studies, while global assessments are widely lacking. In order to facilitate global analysis of this kind, we present a methodological framework that firstly detects spatiotemporally contiguous extremes in Earth observations, and secondly infers the likely pathway of the preceding climate anomaly. The approach does not require long time series, is computationally fast, and easily applicable to a variety of data sets with different spatial and temporal resolutions. The key element of our analysis strategy is to directly search in the relevant observations for spatiotemporally connected components exceeding a certain percentile threshold. We also put an emphasis on characterization of extreme event distribution, and scrutinize the attribution issue. We exemplify the analysis strategy by exploring the fraction of absorbed photosynthetically active radiation (fAPAR) from 1982 to 2011. Our results suggest that the hot spots of extremes in fAPAR lie in Northeastern Brazil, Southeastern Australia, Kenya and Tanzania. Moreover, we demonstrate that the size distribution of extremes follow a distinct power law. The attribution framework reveals that extremes in fAPAR are primarily driven by phases of water scarcity.
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Empirical Inference Conference Paper Domain Generalization via Invariant Feature Representation Muandet, K., Balduzzi, D., Schölkopf, B. In Proceedings of the 30th International Conference on Machine Learning, W&CP 28(1), 10-18, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013, Volume 28, number 1 Web BibTeX
Empirical Inference Talk Domain Generalization via Invariant Feature Representation Muandet, K. 30th International Conference on Machine Learning (ICML2013), 2013 PDF BibTeX
Empirical Inference Poster Domain Generalization via Invariant Feature Representation Muandet, K., Balduzzi, D., Schölkopf, B. 30th International Conference on Machine Learning (ICML2013), 2013 PDF BibTeX
Empirical Inference Conference Paper Domain adaptation under Target and Conditional Shift Zhang, K., Schölkopf, B., Muandet, K., Wang, Z. In Proceedings of the 30th International Conference on Machine Learning, W&CP 28 (3), 819–827, (Editors: S Dasgupta and D McAllester), JMLR, ICML, 2013 PDF Web BibTeX
Empirical Inference Article Efficient network-guided multi-locus association mapping with graph cuts Azencott, C., Grimm, D., Sugiyama, M., Kawahara, Y., Borgwardt, K. Bioinformatics, 29(13):i171-i179, 2013 DOI BibTeX