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The fields of machine learning and pattern recognition can arguably be considered as a modern-day incarnation of an endeavor which has challenged mankind since antiquity. In fact, fundamental questions pertaining to categorization, abstraction, generalization, induction, etc., have been on the agenda of mainstream philosophy, under different names and guises, since its inception. With the advent of modern digital computers and the availablity of enormous amount of raw data, these questions have now taken a computational flavor: instead of asking, say, "What is a dog?", we have started asking "How can one recognize a dog?" or, more technically, "What is an algorithm to recognize a dog?". Indeed, it has even been maintained that for a philosophical theory of knowledge to be respectable, it has to be described in computational terms (Thagard, 1988).
As it often happens with scientific research, in the early days of machine learning and pattern recognition there used to be a genuine interest around philosophical and conceptual issues (see, e.g., Minsky, 1961; Sutherland, 1968; Watanabe, 1969; Bongard, 1970; Nelson, 1976; Good, 1983), but over time the interest shifted almost entirely to technical and algorithmic aspects, and became driven mainly by practical applications. With this reality in mind, it is instructive to remark that although the dismissal of philosophical inquiry at times of intense incremental scientific progress is understandable to allow time for the immediate needs of problem-solving, it is also sometimes responsible for preventing or delaying the emergence of true scientific progress (Kuhn, 1962).
There are several points of contact between philosophy, machine learning, and pattern recognition worth exploiting. To begin, as pointed out by Duda, Hart, and Stork (2000), the very foundations of pattern recognition can be traced back to early Greek philosophers who distinguished between an “essential property” from an “accidental property” of an object, so that the whole field of pattern recognition can naturally be cast as the problem of finding such essential properties of a category. As a matter of fact, during the past centuries several varieties of "essentialism" have been put forward, and it is not clear which one, if any, is being used by present-day pattern recognition research (see Gelman, 2003, for a developmental psychology perspective). Interestingly, in modern times, the very essentialist assumption has been vigorously challenged (see, e.g., James, 1890/1983; Wittgenstein, 1953; Rorty, 1979), giving rise to a relativistic position which denies the existence of essences, thereby suggesting a relational view which is reminiscent of modern link-oriented approaches to social network analysis (Kleinberg, 1998; Easley and Kleinberg, 2010) as well to kernel- and purely similarity-based approaches to pattern analysis and recognition (see, e.g., Schölkopf and Smola, 2001; Shawe-Taylor and Cristianini, 2004; http://simbad-fp7.eu).
Besides the representation problem alluded to above, another all-important philosophical issue related to the machine learning endeavor concerns the very process of inference, and hence its connections to the philosophy of science. In fact, there are such striking analogies between the two disciplines that it has even been maintained that machine learning should be regarded as "experimental philosophy of science" (Korb, 2004). This is motivated by the observation that at the very heart of both fields there lies the notion of an inductive strategy (by way of algorithms or as they appear in scientific practice), and that the hypothesis choice in science is akin to model selection in machine learning (but see, Williamson, 2009, for a more elaborate position). The connecton with the philosophy of science touches upon such fundamental topics as the foundations of probability (Savage, 1972), Bayesianism and causality (Spirtes, Glymour, and Scheines, 2001; Bovens and Hartmann, 2004; Pearl, 2009; Koller and Friedman, 2009), inductionism vs. falsificationism (Popper, 1959; Lakatos, 1970), etc., each of which is on the agenda of present-day machine learning research.
Other fundamental topics which lie at the intersection of philosophy, machine learning and pattern recognition (and cognitive science as well) include: the nature of similarity and categorization (e.g., Quine, 1969; Goodman, 1972; Tversky, 1977; Lakoff, 1987; Eco, 2000; Hahn and Ramscar, 2001), (causal) decision theory (Lewis, 1981; Skyrms, 1980; Joyce, 1999), game theory (Nozick, 1994; Fudenberg and Levine, 1998; Shafer and Vovk, 2001; Cesa-Bianchi and Lugosi, 2006; Shoham and Leyton-Brown, 2009; Skyrms, 2010), and the nature of information (Watanabe, 1969; Hintikka and Suppes, 1970; Adams, 2003; Skyrms, 2010; Floridi, 2011).
In recent years there has been an increasing interest around the foundational and/or philosophical problems of machine learning and pattern recognition, from both the computer scientist's and the philosopher's camps. We mention, for example, Bob Williamson's project of "reconceiving machine learning" (http://users.cecs.anu.edu.au/~williams/rml.html), the NIPS'09 workshop on "Clustering: Science or art?" (http://stanford.edu/~rezab/nips2009workshop/) and the associated manifesto (von Luxburg, Williamson, and Guyon, 2011), the recent MIT Press book by Gilbert Harman (a philosopher) and S. Kulkarni (an engineer) on reliable inductive reasoning (Harman and Kulkarni, 2007), the ECML'2001 workshop on "Machine learning as experimental philosophy of science" (http://www.csse.monash.edu.au/~korb/posml.html) with the associated special issue of Minds and Machines (vol. 14, no. 4, 2004), the work of P. Thagard on "computational philosophy of science" (Thagard, 1988, 1990), Corfield et al.'s study on the connection between the Popper and the VC-dimension (Corfield, Schölkopf, and Vapnik, 2009), von Luxburg and Schölkopf 's contribution in the Handbook of the History of Logic (von Luxburg and Schölkopf, 2011), Halpern and Pearl's philosophical study on "causes and explanations" (Halpern and Pearl, 2005), and O. Bousquet's blog on "machine learning thoughts" (http://ml.typepad.com/machinelearningthoughts/), to name a few examples.
This suggests that the time is ripe to attempt establishing a long-term dialogue between the philosophy and the machine learning communities with a view to foster cross-fertilization of ideas. In particular, we do feel the present moment is appropriate for reflection, reassessment and eventually some synthesis, with the aim of providing the machine learning field a self-portrait of where it currently stands and where it is going as a whole, and hopefully suggesting new directions. The aim of this workshop is precisely to consolidate research efforts in this area, and to provide an informal discussion forum for researchers and practitioners interested in this important yet diverse subject.
Accordingly, topics of interest include (but are not limited to):
- connections to epistemology and philosophy of science (inductionism, falsificationism, etc)
- essentialism vs anti-essentialism (e.g., feature-based vs similarity/relational approaches)
- foundations of probability and causality (Bayesianism, etc.)
- abstraction and generalization
- connections to decision and game theory
- similarity and categorization
- the nature of information
References
Adams, F. (2003). The informational turn in philosophy. Minds and Machines 13(4):471–501.
Bongard, M. M. (1970). Pattern Recognition. Spartan Books, New York (original published in Russian in 1967).
Bovens, L., and Hartmann, S. (2004). Bayesian Epistemology. Oxford University Press, Oxford, UK.
Cesa-Bianchi, N., and Lugosi, G. (2006). Prediction, Learning, and Games. Cambridge University Press, Cambridge, UK.
Corfield, D., Schölkopf, B., and Vapnik, V. (2009). Falsificationism and statistical learning theory: Comparing the Popper and the Vapnik-Chervonenkis dimensions. J. Gen. Phil. Sci. 40:51-58.
Duda, R. O., Hart, P. E., and Stork, D. G. (2000). Pattern Classification. John Wiley & Sons, New York.
Easley, D., and Kleinberg, J. (2010). Networks, Crowds, and Markets: Reasoning About a Highly Connected World. Cambridge University Press, Cambridge, UK.
Eco, U. (2000). Kant and the Platypus: Essays on Language and Cognition. Harvest Books.
Floridi, L. (2011). The Philosophy of Information. Oxford University Press, Oxford, UK.
Fudenberg, D., and Levine, D. K. (1998). The Theory of Learning in Games. MIT Press, Cambridge, MA.
Gelman, S. A. (2003). The Essential Child: Origins of Essentialism in Everyday Thought. Oxford University Press, New York.
Good, I. J. (1983). The philosophy of exploratory data analysis. Phil. Sci. 50(2):283-295.
Goodman, N. (1972). Seven strictures on similarity. In: N. Goodman (Ed.), Problems and Projects. Bobs-Merrill, Indianapolis.
Hahn, U., and Ramscar, M. (Eds.) (2001). Similarity and Categorization. Oxford University Press, Oxford, UK.
Halpern, J., and Pearl, J. (2005). Causes and explanations: A structural-model approach. British J. Phil. Sci. 56:843-911.
Harman, G., and Kulkarni, S. (2007). Reliable Reasoning: Induction and Statistical Learning Theory. MIT Press, Cambridge, MA.
Hintikka, J., and Suppes, P. (Eds.) (1970). Information and Inference. Springer, Berlin.
James, W. (1983). The Principles of Psychology. Harvard University Press, Cambridge, MA (Originally published in 1890).
Joyce, J. (1999). The Foundations of Causal Decision Theory. Cambridge University Press, Cambridge, UK.
Kleinberg, J. (1998). Authoritative sources in a hyperlinked environment. Proc. 9th ACM-SIAM Symposium on Discrete Algorithms.
Koller, D., and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge, UK.
Korb, K. (2004). Introduction: Machine learning as philosophy of science. Minds and Machines 14(4).
Kuhn, T. S. (1962). The Structure of Scientific Revolutions. University of Chicago Press.
Lakatos, I. (1970). Falsification and the methodology of scientific research programmes. In Lakatos, I., and Musgrove, A. (Eds). Criticism and the Growth of Knowledge. Cambridge University Press, Cambridge.
Lakoff, G. (1987). Women, Fire, and Dangerous Things: What Categories Reveal about the Mind. The University of Chicago Press.
Lewis, D. (1981). Causal decision theory. Australasian J. Phil. 59:5–30.
Minsky, M. (1961). Steps toward artificial intelligence. Proc. IRE 49:8-30.
Nelson, R. J. (1976). On mechanical recognition. Phil. Sci. 43(1):24-52.
Nozick, R. (1994). The Nature of Rationality. Princeton University Press, Princeton, NJ.
Pearl, J. (2009). Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK (2nd edition).
Popper, K. R. (1959). The Logic of Scientific Discovery. Hutchinson & Co. (Originally published in German in 1935).
Quine, W. V. O. (1969). Natural kinds. In: Ontological Relativity and Other Essays. Columbia University Press.
Rorty, R. (1979). Philosophy and the Mirror of Nature. Princeton University Press, Princeton, NJ.
Savage, L. (1972). The Foundations of Statistics. Dover, New York (2nd edition).
Schölkopf, B., and Smola, A. (2001). Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA.
Shafer, G., and Vovk, V. (2001). Probability and Finance: It's Only a Game. John WIley & Sons, New York.
Shawe-Taylor, J., and Cristianini, N. (2004). Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge, UK.
Shoham, Y., and Leyton-Brown, K. (2009). Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge, UK.
Skyrms, B. (1980). Causal Necessity: A Pragmatic Investigation of the Necessity of Laws. Yale University Press, New Haven, CT.
Skyrms, B. (2010). Signals: Evolution, Learning and Information. Oxford University Press, Oxford, UK.
Spirtes, P., Glymour, C., and Scheines, R. (2001). Causation, Prediction, and Search. MIT Press, Cambridge, MA.
Sutherland, N. S. (1968). Outlines of a theory of visual pattern recognition in animals and man. Proc. Royal Soc. B 171:297-317.
Thagard, P. (1988). Computational Philosophy of Science. MIT Press, Cambridge, MA.
Thagard, P. (1990). Philosophy and machine learning. Canad. J. Phil. 20(2):261-276.
Tversky, A. (1977). Features of similarity. Psychol. Rev. 84(4):327-352.
von Luxburg, U., and Schölkopf, B. (2011). Statistical Learning Theory: Models, Concepts, and Results. In: D. Gabbay, S. Hartmann and J. Woods (Eds). Handbook of the History of Logic, vol 10: Inductive Logic. pp. 651-706. Elsevier.
von Luxburg, U., Williamson, R. C., and Guyon, I. (2011). Clustering: Science or art? (http://users.cecs.anu.edu.au/~williams/papers/P185.pdf)
Watanabe, S. (1969). Knowing and Guessing: A Quantitative Study of Inference and Information. John Wiley & Sons, New York.
Williamson, J. (2009). The philosophy of science and its relation to machine learning. In: M. M. Gaber (Ed.), Scientific Data Mining and Knowledge Discovery: Principles and Foundations. Springer, Berlin.
Wittgenstein, L. (1953). Philosophical Investigations. Blackwell Publishers.
Author Information
Marcello Pelillo (Università Ca' Foscari di Venezia)
Joachim M Buhmann (ETH Zurich)
Tiberio Caetano (NICTA Canberra)
Bernhard Schölkopf (MPI for Intelligent Systems)
Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.
Larry Wasserman (Carnegie Mellon University)
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Luigi Gresele · Giancarlo Fissore · Adrián Javaloy · Bernhard Schölkopf · Aapo Hyvarinen -
2019 : Bernhard Schölkopf »
Bernhard Schölkopf -
2019 : Poster Session »
Ethan Harris · Tom White · Oh Hyeon Choung · Takashi Shinozaki · Dipan Pal · Katherine L. Hermann · Judy Borowski · Camilo Fosco · Chaz Firestone · Vijay Veerabadran · Benjamin Lahner · Chaitanya Ryali · Fenil Doshi · Pulkit Singh · Sharon Zhou · Michel Besserve · Michael Chang · Anelise Newman · Mahesan Niranjan · Jonathon Hare · Daniela Mihai · Marios Savvides · Simon Kornblith · Christina M Funke · Aude Oliva · Virginia de Sa · Dmitry Krotov · Colin Conwell · George Alvarez · Alex Kolchinski · Shengjia Zhao · Mitchell Gordon · Michael Bernstein · Stefano Ermon · Arash Mehrjou · Bernhard Schölkopf · John Co-Reyes · Michael Janner · Jiajun Wu · Josh Tenenbaum · Sergey Levine · Yalda Mohsenzadeh · Zhenglong Zhou -
2019 Poster: On the Fairness of Disentangled Representations »
Francesco Locatello · Gabriele Abbati · Thomas Rainforth · Stefan Bauer · Bernhard Schölkopf · Olivier Bachem -
2019 Poster: On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset »
Muhammad Waleed Gondal · Manuel Wuethrich · Djordje Miladinovic · Francesco Locatello · Martin Breidt · Valentin Volchkov · Joel Akpo · Olivier Bachem · Bernhard Schölkopf · Stefan Bauer -
2019 Poster: Perceiving the arrow of time in autoregressive motion »
Kristof Meding · Dominik Janzing · Bernhard Schölkopf · Felix A. Wichmann -
2019 Poster: Selecting causal brain features with a single conditional independence test per feature »
Atalanti Mastakouri · Bernhard Schölkopf · Dominik Janzing -
2019 Poster: Kernel Stein Tests for Multiple Model Comparison »
Jen Ning Lim · Makoto Yamada · Bernhard Schölkopf · Wittawat Jitkrittum -
2019 Spotlight: Perceiving the arrow of time in autoregressive motion »
Kristof Meding · Dominik Janzing · Bernhard Schölkopf · Felix A. Wichmann -
2018 : Datasets and Benchmarks for Causal Learning »
Csaba Szepesvari · Isabelle Guyon · Nicolai Meinshausen · David Blei · Elias Bareinboim · Bernhard Schölkopf · Pietro Perona -
2018 : Learning Independent Mechanisms »
Bernhard Schölkopf -
2018 Poster: Informative Features for Model Comparison »
Wittawat Jitkrittum · Heishiro Kanagawa · Patsorn Sangkloy · James Hays · Bernhard Schölkopf · Arthur Gretton -
2018 Poster: Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models »
Alexander Neitz · Giambattista Parascandolo · Stefan Bauer · Bernhard Schölkopf -
2017 : Leveraging the Crowd to Detect and Reduce the Spread of Fake News and Misinformation »
Alice Oh · Bernhard Schölkopf -
2017 Poster: Avoiding Discrimination through Causal Reasoning »
Niki Kilbertus · Mateo Rojas Carulla · Giambattista Parascandolo · Moritz Hardt · Dominik Janzing · Bernhard Schölkopf -
2017 Poster: Efficient and Flexible Inference for Stochastic Systems »
Stefan Bauer · Nico S Gorbach · Djordje Miladinovic · Joachim M Buhmann -
2017 Poster: Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms »
Yatao Bian · Kfir Levy · Andreas Krause · Joachim M Buhmann -
2017 Poster: Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning »
Shixiang (Shane) Gu · Timothy Lillicrap · Richard Turner · Zoubin Ghahramani · Bernhard Schölkopf · Sergey Levine -
2017 Poster: Scalable Variational Inference for Dynamical Systems »
Nico S Gorbach · Stefan Bauer · Joachim M Buhmann -
2017 Poster: AdaGAN: Boosting Generative Models »
Ilya Tolstikhin · Sylvain Gelly · Olivier Bousquet · Carl-Johann SIMON-GABRIEL · Bernhard Schölkopf -
2016 Poster: Scalable Adaptive Stochastic Optimization Using Random Projections »
Gabriel Krummenacher · Brian McWilliams · Yannic Kilcher · Joachim M Buhmann · Nicolai Meinshausen -
2016 Poster: Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels »
Ilya Tolstikhin · Bharath Sriperumbudur · Bernhard Schölkopf -
2016 Poster: Statistical Inference for Cluster Trees »
Jisu KIM · Yen-Chi Chen · Sivaraman Balakrishnan · Alessandro Rinaldo · Larry Wasserman -
2016 Poster: Consistent Kernel Mean Estimation for Functions of Random Variables »
Carl-Johann Simon-Gabriel · Adam Scibior · Ilya Tolstikhin · Bernhard Schölkopf -
2015 Poster: Optimal Ridge Detection using Coverage Risk »
Yen-Chi Chen · Christopher Genovese · Shirley Ho · Larry Wasserman -
2015 Poster: Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations »
Kirthevasan Kandasamy · Akshay Krishnamurthy · Barnabas Poczos · Larry Wasserman · james m robins -
2014 Poster: (Almost) No Label No Cry »
Giorgio Patrini · Richard Nock · Tiberio Caetano · Paul Rivera -
2014 Poster: Fast and Robust Least Squares Estimation in Corrupted Linear Models »
Brian McWilliams · Gabriel Krummenacher · Mario Lucic · Joachim M Buhmann -
2014 Spotlight: (Almost) No Label No Cry »
Giorgio Patrini · Richard Nock · Tiberio Caetano · Paul Rivera -
2014 Spotlight: Fast and Robust Least Squares Estimation in Corrupted Linear Models »
Brian McWilliams · Gabriel Krummenacher · Mario Lucic · Joachim M Buhmann -
2014 Poster: Kernel Mean Estimation via Spectral Filtering »
Krikamol Muandet · Bharath Sriperumbudur · Bernhard Schölkopf -
2013 Workshop: Modern Nonparametric Methods in Machine Learning »
Arthur Gretton · Mladen Kolar · Samory Kpotufe · John Lafferty · Han Liu · Bernhard Schölkopf · Alexander Smola · Rob Nowak · Mikhail Belkin · Lorenzo Rosasco · peter bickel · Yue Zhao -
2013 Workshop: NIPS 2013 Workshop on Causality: Large-scale Experiment Design and Inference of Causal Mechanisms »
Isabelle Guyon · Leon Bottou · Bernhard Schölkopf · Alexander Statnikov · Evelyne Viegas · james m robins -
2013 Poster: The Randomized Dependence Coefficient »
David Lopez-Paz · Philipp Hennig · Bernhard Schölkopf -
2013 Poster: Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. »
Michel Besserve · Nikos K Logothetis · Bernhard Schölkopf -
2013 Poster: Causal Inference on Time Series using Restricted Structural Equation Models »
Jonas Peters · Dominik Janzing · Bernhard Schölkopf -
2013 Poster: Correlated random features for fast semi-supervised learning »
Brian McWilliams · David Balduzzi · Joachim M Buhmann -
2013 Poster: Cluster Trees on Manifolds »
Sivaraman Balakrishnan · Srivatsan Narayanan · Alessandro Rinaldo · Aarti Singh · Larry Wasserman -
2012 Workshop: Algebraic Topology and Machine Learning »
Sivaraman Balakrishnan · Alessandro Rinaldo · Donald Sheehy · Aarti Singh · Larry Wasserman -
2012 Poster: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Poster: Context-Sensitive Decision Forests for Object Detection »
Peter Kontschieder · Samuel Rota Bulò · Antonio Criminisi · Pushmeet Kohli · Marcello Pelillo · Horst Bischof -
2012 Poster: Learning as MAP Inference in Discrete Graphical Models »
Tiberio Caetano · Xianghang Liu · James Petterson -
2012 Poster: A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation »
Aaron Defazio · Tiberio Caetano -
2012 Session: Oral Session 8 »
Tiberio Caetano -
2012 Spotlight: Learning from Distributions via Support Measure Machines »
Krikamol Muandet · Kenji Fukumizu · Francesco Dinuzzo · Bernhard Schölkopf -
2012 Poster: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: Semi-Supervised Domain Adaptation with Non-Parametric Copulas »
David Lopez-Paz · José Miguel Hernández-Lobato · Bernhard Schölkopf -
2012 Spotlight: A Convex Formulation for Learning Scale-Free Networks via Submodular Relaxation »
Aaron Defazio · Tiberio Caetano -
2012 Poster: The representer theorem for Hilbert spaces: a necessary and sufficient condition »
Francesco Dinuzzo · Bernhard Schölkopf -
2012 Poster: Exponential Concentration for Mutual Information Estimation with Application to Forests »
Han Liu · John Lafferty · Larry Wasserman -
2011 Workshop: Cosmology meets Machine Learning »
Michael Hirsch · Sarah Bridle · Bernhard Schölkopf · Phil Marshall · Stefan Harmeling · Mark Girolami -
2011 Invited Talk: From kernels to causal inference »
Bernhard Schölkopf -
2011 Poster: Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance »
Peter Gehler · Carsten Rother · Martin Kiefel · Lumin Zhang · Bernhard Schölkopf -
2011 Poster: Causal Discovery with Cyclic Additive Noise Models »
Joris M Mooij · Dominik Janzing · Tom Heskes · Bernhard Schölkopf -
2011 Poster: Submodular Multi-Label Learning »
James Petterson · Tiberio Caetano -
2010 Spotlight: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Spotlight: Graph-Valued Regression »
Han Liu · Xi Chen · John Lafferty · Larry Wasserman -
2010 Poster: Graph-Valued Regression »
Han Liu · Xi Chen · John Lafferty · Larry Wasserman -
2010 Poster: Word Features for Latent Dirichlet Allocation »
James Petterson · Alexander Smola · Tiberio Caetano · Wray L Buntine · Shravan M Narayanamurthy -
2010 Poster: Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake »
Stefan Harmeling · Michael Hirsch · Bernhard Schölkopf -
2010 Poster: Switched Latent Force Models for Movement Segmentation »
Mauricio A Alvarez · Jan Peters · Bernhard Schölkopf · Neil D Lawrence -
2010 Poster: Probabilistic latent variable models for distinguishing between cause and effect »
Joris M Mooij · Oliver Stegle · Dominik Janzing · Kun Zhang · Bernhard Schölkopf -
2010 Poster: Reverse Multi-Label Learning »
James Petterson · Tiberio Caetano -
2010 Poster: Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models »
Han Liu · Kathryn Roeder · Larry Wasserman -
2010 Poster: Multitask Learning without Label Correspondences »
Novi Quadrianto · Alexander Smola · Tiberio Caetano · S.V.N. Vishwanathan · James Petterson -
2009 Workshop: Connectivity Inference in Neuroimaging »
Karl Friston · Moritz Grosse-Wentrup · Uta Noppeney · Bernhard Schölkopf -
2009 Workshop: Learning with Orderings »
Tiberio Caetano · Carlos Guestrin · Jonathan Huang · Risi Kondor · Guy Lebanon · Marina Meila -
2009 Poster: Convex Relaxation of Mixture Regression with Efficient Algorithms »
Novi Quadrianto · Tiberio Caetano · John Lim · Dale Schuurmans -
2009 Poster: A Game-Theoretic Approach to Hypergraph Clustering »
Samuel Rota Bulò · Marcello Pelillo -
2009 Poster: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Oral: Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions »
Bharath Sriperumbudur · Kenji Fukumizu · Arthur Gretton · Gert Lanckriet · Bernhard Schölkopf -
2009 Poster: Exponential Family Graph Matching and Ranking »
James Petterson · Tiberio Caetano · Julian J McAuley · Jin Yu -
2008 Workshop: Causality: objectives and assessment »
Isabelle Guyon · Dominik Janzing · Bernhard Schölkopf -
2008 Mini Symposium: Computational Photography »
Bill Freeman · Bernhard Schölkopf -
2008 Poster: Nonparametric regression and classification with joint sparsity constraints »
Han Liu · John Lafferty · Larry Wasserman -
2008 Poster: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Spotlight: Nonparametric regression and classification with joint sparsity constraints »
Han Liu · John Lafferty · Larry Wasserman -
2008 Oral: Characteristic Kernels on Groups and Semigroups »
Kenji Fukumizu · Bharath Sriperumbudur · Arthur Gretton · Bernhard Schölkopf -
2008 Poster: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Poster: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance »
Jeremy Hill · Jason Farquhar · Suzanne Martens · Felix Bießmann · Bernhard Schölkopf -
2008 Poster: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences »
Matthias Seeger · Hannes Nickisch · Rolf Pohmann · Bernhard Schölkopf -
2008 Spotlight: Nonlinear causal discovery with additive noise models »
Patrik O Hoyer · Dominik Janzing · Joris M Mooij · Jonas Peters · Bernhard Schölkopf -
2008 Spotlight: Bayesian Experimental Design of Magnetic Resonance Imaging Sequences »
Matthias Seeger · Hannes Nickisch · Rolf Pohmann · Bernhard Schölkopf -
2008 Spotlight: Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance »
Jeremy Hill · Jason Farquhar · Suzanne Martens · Felix Bießmann · Bernhard Schölkopf -
2008 Poster: Robust Near-Isometric Matching via Structured Learning of Graphical Models »
Julian J McAuley · Tiberio Caetano · Alexander Smola -
2008 Poster: An empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis »
Gabriele B Schweikert · Christian Widmer · Bernhard Schölkopf · Gunnar Rätsch -
2008 Poster: Diffeomorphic Dimensionality Reduction »
Christian Walder · Bernhard Schölkopf -
2007 Spotlight: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Poster: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Poster: Kernel Measures of Conditional Dependence »
Kenji Fukumizu · Arthur Gretton · Xiaohai Sun · Bernhard Schölkopf -
2007 Spotlight: An Analysis of Inference with the Universum »
Fabian H Sinz · Olivier Chapelle · Alekh Agarwal · Bernhard Schölkopf -
2007 Spotlight: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Poster: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Poster: A Kernel Statistical Test of Independence »
Arthur Gretton · Kenji Fukumizu · Choon Hui Teo · Le Song · Bernhard Schölkopf · Alexander Smola -
2007 Spotlight: SpAM: Sparse Additive Models »
Pradeep Ravikumar · Han Liu · John Lafferty · Larry Wasserman -
2007 Spotlight: Statistical Analysis of Semi-Supervised Regression »
John Lafferty · Larry Wasserman -
2007 Poster: Statistical Analysis of Semi-Supervised Regression »
John Lafferty · Larry Wasserman -
2007 Poster: Compressed Regression »
Shuheng Zhou · John Lafferty · Larry Wasserman -
2006 Poster: Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions »
Christian Walder · Bernhard Schölkopf · Olivier Chapelle -
2006 Poster: Learning Dense 3D Correspondence »
Florian Steinke · Bernhard Schölkopf · Volker Blanz -
2006 Poster: A Local Learning Approach for Clustering »
Mingrui Wu · Bernhard Schölkopf -
2006 Poster: Denoising and Dimension Reduction in Feature Space »
Mikio L Braun · Joachim M Buhmann · Klaus-Robert Müller -
2006 Poster: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Spotlight: Correcting Sample Selection Bias by Unlabeled Data »
Jiayuan Huang · Alexander Smola · Arthur Gretton · Karsten Borgwardt · Bernhard Schölkopf -
2006 Talk: A Kernel Method for the Two-Sample-Problem »
Arthur Gretton · Karsten Borgwardt · Malte J Rasch · Bernhard Schölkopf · Alexander Smola -
2006 Poster: A Nonparametric Approach to Bottom-Up Visual Saliency »
Wolf Kienzle · Felix A Wichmann · Bernhard Schölkopf · Matthias Franz -
2006 Poster: Learning with Hypergraphs: Clustering, Classification, and Embedding »
Denny Zhou · Jiayuan Huang · Bernhard Schölkopf