NIPS Proceedingsβ

Bernhard Schölkopf

62 Papers

  • AdaGAN: Boosting Generative Models (2017)
  • Avoiding Discrimination through Causal Reasoning (2017)
  • Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning (2017)
  • Consistent Kernel Mean Estimation for Functions of Random Variables (2016)
  • Minimax Estimation of Maximum Mean Discrepancy with Radial Kernels (2016)
  • Kernel Mean Estimation via Spectral Filtering (2014)
  • Causal Inference on Time Series using Restricted Structural Equation Models (2013)
  • Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators. (2013)
  • The Randomized Dependence Coefficient (2013)
  • Learning from Distributions via Support Measure Machines (2012)
  • Semi-Supervised Domain Adaptation with Non-Parametric Copulas (2012)
  • The representer theorem for Hilbert spaces: a necessary and sufficient condition (2012)
  • On Causal Discovery with Cyclic Additive Noise Models (2011)
  • Recovering Intrinsic Images with a Global Sparsity Prior on Reflectance (2011)
  • Probabilistic latent variable models for distinguishing between cause and effect (2010)
  • Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake (2010)
  • Switched Latent Force Models for Movement Segmentation (2010)
  • Kernel Choice and Classifiability for RKHS Embeddings of Probability Distributions (2009)
  • An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis (2008)
  • Bayesian Experimental Design of Magnetic Resonance Imaging Sequences (2008)
  • Characteristic Kernels on Groups and Semigroups (2008)
  • Diffeomorphic Dimensionality Reduction (2008)
  • Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance (2008)
  • Nonlinear causal discovery with additive noise models (2008)
  • A Kernel Statistical Test of Independence (2007)
  • An Analysis of Inference with the Universum (2007)
  • Kernel Measures of Conditional Dependence (2007)
  • A Kernel Method for the Two-Sample-Problem (2006)
  • A Local Learning Approach for Clustering (2006)
  • A Nonparametric Approach to Bottom-Up Visual Saliency (2006)
  • Correcting Sample Selection Bias by Unlabeled Data (2006)
  • Implicit Surfaces with Globally Regularised and Compactly Supported Basis Functions (2006)
  • Learning Dense 3D Correspondence (2006)
  • Learning with Hypergraphs: Clustering, Classification, and Embedding (2006)
  • An Auditory Paradigm for Brain-Computer Interfaces (2004)
  • Face Detection --- Efficient and Rank Deficient (2004)
  • Implicit Wiener Series for Higher-Order Image Analysis (2004)
  • Kernel Methods for Implicit Surface Modeling (2004)
  • Machine Learning Applied to Perception: Decision Images for Gender Classification (2004)
  • Methods Towards Invasive Human Brain Computer Interfaces (2004)
  • Semi-supervised Learning on Directed Graphs (2004)
  • Learning to Find Pre-Images (2003)
  • Learning with Local and Global Consistency (2003)
  • Prediction on Spike Data Using Kernel Algorithms (2003)
  • Ranking on Data Manifolds (2003)
  • Cluster Kernels for Semi-Supervised Learning (2002)
  • Kernel Dependency Estimation (2002)
  • Incorporating Invariances in Non-Linear Support Vector Machines (2001)
  • Sampling Techniques for Kernel Methods (2001)
  • Four-legged Walking Gait Control Using a Neuromorphic Chip Interfaced to a Support Vector Learning Algorithm (2000)
  • Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra (2000)
  • The Kernel Trick for Distances (2000)
  • Invariant Feature Extraction and Classification in Kernel Spaces (1999)
  • Support Vector Method for Novelty Detection (1999)
  • The Entropy Regularization Information Criterion (1999)
  • v-Arc: Ensemble Learning in the Presence of Outliers (1999)
  • Kernel PCA and De-Noising in Feature Spaces (1998)
  • Semiparametric Support Vector and Linear Programming Machines (1998)
  • Shrinking the Tube: A New Support Vector Regression Algorithm (1998)
  • From Regularization Operators to Support Vector Kernels (1997)
  • Prior Knowledge in Support Vector Kernels (1997)
  • Improving the Accuracy and Speed of Support Vector Machines (1996)
  • 3 Books

  • Advances in Neural Information Processing Systems 19 (2006)
  • Advances in Neural Information Processing Systems 18 (2005)
  • Advances in Neural Information Processing Systems 16 (2003)