Dataset Shift in Machine Learning
暂无分享,去创建一个
Neil D. Lawrence | Masashi Sugiyama | Anton Schwaighofer | Joaquin Quionero-Candela | Neil D. Lawrence | Masashi Sugiyama | Anton Schwaighofer | Joaquin Quionero-Candela | A. Schwaighofer
[1] N. Goodman. Fact, Fiction, and Forecast , 1955 .
[2] G. Pólya,et al. Mathematics and Plausible Reasoning , 1956 .
[3] W. Hoeffding. Probability Inequalities for sums of Bounded Random Variables , 1963 .
[4] G. Wahba,et al. A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines , 1970 .
[5] W. J. Studden,et al. Theory Of Optimal Experiments , 1972 .
[6] H. Akaike. A new look at the statistical model identification , 1974 .
[7] M. Stone. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[8] J. Heckman. Shadow prices, market wages, and labor supply , 1974 .
[9] Steven R. Lerman,et al. The Estimation of Choice Probabilities from Choice Based Samples , 1977 .
[10] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[11] Peter Craven,et al. Smoothing noisy data with spline functions , 1978 .
[12] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[13] J. Heckman. Sample selection bias as a specification error , 1979 .
[14] Lung-fei Lee. Some Approaches to the Correction of Selectivity Bias , 1982 .
[15] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[16] D. Rubin,et al. The central role of the propensity score in observational studies for causal effects , 1983 .
[17] P. Green. Iteratively reweighted least squares for maximum likelihood estimation , 1984 .
[18] Shun-ichi Amari,et al. Differential-geometrical methods in statistics , 1985 .
[19] John Law,et al. Robust Statistics—The Approach Based on Influence Functions , 1986 .
[20] C. Manski. Anatomy of the Selection Problem , 1989 .
[21] Jeffrey A. Dubin,et al. Selection Bias in Linear Regression, Logit and Probit Models , 1989 .
[22] Colin McDiarmid,et al. Surveys in Combinatorics, 1989: On the method of bounded differences , 1989 .
[23] H. James. VARIETIES OF SELECTION BIAS , 1990 .
[24] Geoffrey E. Hinton,et al. Adaptive Mixtures of Local Experts , 1991, Neural Computation.
[25] Chris J. Skinner,et al. Analysis of complex surveys , 1991 .
[26] David J. C. MacKay,et al. Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.
[27] H. Sebastian Seung,et al. Query by committee , 1992, COLT '92.
[28] Christopher Winship,et al. Models for Sample Selection Bias , 1992 .
[29] Robert Tibshirani,et al. An Introduction to the Bootstrap , 1994 .
[30] Noel A Cressie,et al. Statistics for Spatial Data. , 1992 .
[31] Heekuck Oh,et al. Neural Networks for Pattern Recognition , 1993, Adv. Comput..
[32] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[33] David A. Cohn,et al. Active Learning with Statistical Models , 1996, NIPS.
[34] B. Lindsay. Efficiency versus robustness : the case for minimum Hellinger distance and related methods , 1994 .
[35] B. Lindsay,et al. Minimum disparity estimation for continuous models: Efficiency, distributions and robustness , 1994 .
[36] C. Field,et al. Robust Estimation - a Weighted Maximum-Likelihood Approach , 1994 .
[37] M. P. Windham. Robustifying Model Fitting , 1995 .
[38] Kenji Fukumizu,et al. Active Learning in Multilayer Perceptrons , 1995, NIPS.
[39] Harris Drucker,et al. Comparison of learning algorithms for handwritten digit recognition , 1995 .
[40] László Györfi,et al. A Probabilistic Theory of Pattern Recognition , 1996, Stochastic Modelling and Applied Probability.
[41] Herbert Gish,et al. Speaker identification via support vector classifiers , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.
[42] M. Gibbs,et al. Efficient implementation of gaussian processes , 1997 .
[43] Federico Girosi,et al. Support Vector Machines: Training and Applications , 1997 .
[44] D. Haussler,et al. MUTUAL INFORMATION, METRIC ENTROPY AND CUMULATIVE RELATIVE ENTROPY RISK , 1997 .
[45] Yiming Yang,et al. A Comparative Study on Feature Selection in Text Categorization , 1997, ICML.
[46] Naoki Abe,et al. Query Learning Strategies Using Boosting and Bagging , 1998, ICML.
[47] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[48] F. Vella. Estimating Models with Sample Selection Bias: A Survey , 1998 .
[49] Alexander J. Smola,et al. Learning with kernels , 1998 .
[50] H. Goldstein,et al. Weighting for unequal selection probabilities in multilevel models , 1998 .
[51] David Barber,et al. Bayesian Classification With Gaussian Processes , 1998, IEEE Trans. Pattern Anal. Mach. Intell..
[52] Harris Drucker,et al. Support vector machines for spam categorization , 1999, IEEE Trans. Neural Networks.
[53] Lars Kai Hansen,et al. Bayesian Averaging is Well-Temperated , 1999, NIPS.
[54] Nello Cristianini,et al. Controlling the Sensitivity of Support Vector Machines , 1999 .
[55] Peter Sollich. Probabilistic interpretations and Bayesian methods for support vector machines , 1999 .
[56] D. Bertsekas,et al. Incremental subgradient methods for nondifferentiable optimization , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).
[57] H. Shimodaira,et al. Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .
[58] Volker Tresp,et al. Mixtures of Gaussian Processes , 2000, NIPS.
[59] Sayan Mukherjee,et al. Feature Selection for SVMs , 2000, NIPS.
[60] Shun-ichi Amari,et al. Methods of information geometry , 2000 .
[61] J. Pearl. Causality: Models, Reasoning and Inference , 2000 .
[62] Jonathan Baxter,et al. A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..
[63] Jerry D. Gibson,et al. Handbook of Image and Video Processing , 2000 .
[64] Ronitt Rubinfeld,et al. Testing that distributions are close , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[65] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[66] Kenji Fukumizu,et al. Statistical active learning in multilayer perceptrons , 2000, IEEE Trans. Neural Networks Learn. Syst..
[67] D. Wiens. Robust weights and designs for biased regression models: Least squares and generalized M-estimation , 2000 .
[68] Masashi Sugiyama,et al. Incremental Active Learning for Optimal Generalization , 2000, Neural Computation.
[69] Marco Saerens,et al. Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a multi-class problem in remote sensing , 2001, ICML.
[70] Katya Scheinberg,et al. Efficient SVM Training Using Low-Rank Kernel Representations , 2002, J. Mach. Learn. Res..
[71] J. Welsh,et al. Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. , 2001, Cancer research.
[72] Carl E. Rasmussen,et al. Infinite Mixtures of Gaussian Process Experts , 2001, NIPS.
[73] Masashi Sugiyama,et al. Subspace Information Criterion for Model Selection , 2001, Neural Computation.
[74] S. Dhanasekaran,et al. Delineation of prognostic biomarkers in prostate cancer , 2001, Nature.
[75] Charles Elkan,et al. The Foundations of Cost-Sensitive Learning , 2001, IJCAI.
[76] Bernhard Schölkopf,et al. Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.
[77] Sumio Watanabe,et al. Algebraic Analysis for Nonidentifiable Learning Machines , 2001, Neural Computation.
[78] Carsten O. Peterson,et al. Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.
[79] Bianca Zadrozny,et al. Learning and making decisions when costs and probabilities are both unknown , 2001, KDD '01.
[80] R. Spang,et al. Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[81] J. Copas,et al. Local sensitivity approximations for selectivity bias , 2001 .
[82] Masashi Sugiyama,et al. Optimal design of regularization term and regularization parameter by subspace information criterion , 2002, Neural Networks.
[83] Ingo Steinwart,et al. Support Vector Machines are Universally Consistent , 2002, J. Complex..
[84] Nitesh V. Chawla,et al. SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..
[85] T. Kanamori. Statistical Asymptotic Theory of Active Learning , 2002 .
[86] Shahar Mendelson,et al. A Few Notes on Statistical Learning Theory , 2002, Machine Learning Summer School.
[87] E. Lander,et al. Gene expression correlates of clinical prostate cancer behavior. , 2002, Cancer cell.
[88] Ben Taskar,et al. Max-Margin Markov Networks , 2003, NIPS.
[89] Zoubin Ghahramani,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[90] Jure Leskovec,et al. Linear Programming Boosting for Uneven Datasets , 2003, ICML.
[91] T. Ben-David,et al. Exploiting Task Relatedness for Multiple , 2003 .
[92] Bernhard Schölkopf,et al. Learning with Local and Global Consistency , 2003, NIPS.
[93] Masashi Sugiyama,et al. Active Learning with Model Selection — Simultaneous Optimization of Sample Points and Models for Trigonometric Polynomial Models , 2003 .
[94] Thore Graepel,et al. Invariant Pattern Recognition by Semi-Definite Programming Machines , 2003, NIPS.
[95] Matthias Hein,et al. Measure Based Regularization , 2003, NIPS.
[96] J. Lafferty,et al. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions , 2003, ICML 2003.
[97] L. Ghaoui,et al. Robust Classification with Interval Data , 2003 .
[98] Mk Warmuth,et al. Active Learning with SVMs in the Drug Discovery Process , 2003 .
[99] Hidetoshi Shimodaira,et al. Active learning algorithm using the maximum weighted log-likelihood estimator , 2003 .
[100] Michael I. Jordan,et al. Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..
[101] Ji Zhu,et al. A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning , 2004, NIPS.
[102] Nello Cristianini,et al. Learning the Kernel Matrix with Semidefinite Programming , 2002, J. Mach. Learn. Res..
[103] J. Lunceford,et al. Stratification and weighting via the propensity score in estimation of causal treatment effects: a comparative study , 2004, Statistics in medicine.
[104] Charles Elkan,et al. A Bayesian network framework for reject inference , 2004, KDD.
[105] Stephen Kwek,et al. Applying Support Vector Machines to Imbalanced Datasets , 2004, ECML.
[106] Shyhtsun Felix Wu,et al. On Attacking Statistical Spam Filters , 2004, CEAS.
[107] Bianca Zadrozny,et al. Learning and evaluating classifiers under sample selection bias , 2004, ICML.
[108] Nitesh V. Chawla,et al. Editorial: special issue on learning from imbalanced data sets , 2004, SKDD.
[109] Motoaki Kawanabe,et al. Trading Variance Reduction with Unbiasedness: The Regularized Subspace Information Criterion for Robust Model Selection in Kernel Regression , 2004, Neural Computation.
[110] Naftali Tishby,et al. Margin based feature selection - theory and algorithms , 2004, ICML.
[111] Yi Lin,et al. Support Vector Machines for Classification in Nonstandard Situations , 2002, Machine Learning.
[112] Peter Sollich,et al. Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities , 2002, Machine Learning.
[113] Bernhard Schölkopf,et al. Training Invariant Support Vector Machines , 2002, Machine Learning.
[114] H. Sung. Gaussian Mixture Regression and Classification , 2004 .
[115] Pedro M. Domingos,et al. Adversarial classification , 2004, KDD.
[116] Sebastian Thrun,et al. Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.
[117] Shai Ben-David,et al. Detecting Change in Data Streams , 2004, VLDB.
[118] Neil D. Lawrence,et al. Extensions of the Informative Vector Machine , 2004, Deterministic and Statistical Methods in Machine Learning.
[119] Masashi Sugiyama,et al. Input-dependent estimation of generalization error under covariate shift , 2005 .
[120] Christopher Meek,et al. Good Word Attacks on Statistical Spam Filters , 2005, CEAS.
[121] Thomas Hofmann,et al. Kernel Methods for Missing Variables , 2005, AISTATS.
[122] Stephen P. Boyd,et al. Robust Fisher Discriminant Analysis , 2005, NIPS.
[123] Roland Eils,et al. Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes , 2005, BMC Bioinformatics.
[124] Eytan Ruppin,et al. Feature Selection Based on the Shapley Value , 2005, IJCAI.
[125] Miroslav Dudík,et al. Correcting sample selection bias in maximum entropy density estimation , 2005, NIPS.
[126] Naftali Tishby,et al. Generalization in Clustering with Unobserved Features , 2005, NIPS.
[127] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[128] Nitesh V. Chawla,et al. Learning From Labeled And Unlabeled Data: An Empirical Study Across Techniques And Domains , 2011, J. Artif. Intell. Res..
[129] Klaus-Robert Müller,et al. Model Selection Under Covariate Shift , 2005, ICANN.
[130] Lang Tong,et al. Nonparametric change detection and estimation in large-scale sensor networks , 2006, IEEE Transactions on Signal Processing.
[131] Masashi Sugiyama,et al. Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error , 2006, J. Mach. Learn. Res..
[132] Matthias Hein,et al. Uniform Convergence of Adaptive Graph-Based Regularization , 2006, COLT.
[133] Klaus-Robert Müller,et al. Importance-Weighted Cross-Validation for Covariate Shift , 2006, DAGM-Symposium.
[134] Masashi Sugiyama,et al. Mixture Regression for Covariate Shift , 2006, NIPS.
[135] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[136] Roger Fletcher,et al. New algorithms for singly linearly constrained quadratic programs subject to lower and upper bounds , 2006, Math. Program..
[137] J. Horowitz,et al. Identification and estimation of statistical functionals using incomplete data , 2006 .
[138] Koby Crammer,et al. Analysis of Representations for Domain Adaptation , 2006, NIPS.
[139] John Blitzer,et al. Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.
[140] Alexander Zien,et al. Gaussian Processes and the Null-Category Noise Model , 2006 .
[141] Bernhard Schölkopf,et al. Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.
[142] Stefien Bickel. ECML-PKDD Discovery Challenge 2006 Overview , 2006 .
[143] Steffen Bickel,et al. Dirichlet-Enhanced Spam Filtering based on Biased Samples , 2006, NIPS.
[144] Alexander J. Smola,et al. Convex Learning with Invariances , 2007, NIPS.
[145] Bernhard Schölkopf,et al. Kernel Measures of Conditional Dependence , 2007, NIPS.
[146] Motoaki Kawanabe,et al. Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.
[147] Klaus-Robert Müller,et al. Covariate Shift Adaptation by Importance Weighted Cross Validation , 2007, J. Mach. Learn. Res..
[148] Olivier Chapelle,et al. Training a Support Vector Machine in the Primal , 2007, Neural Computation.
[149] Lawrence Carin,et al. Multi-Task Learning for Classification with Dirichlet Process Priors , 2007, J. Mach. Learn. Res..
[150] Masashi Sugiyama,et al. Generalization Error Estimation for Non-linear Learning Methods , 2007, IEICE Trans. Fundam. Electron. Commun. Comput. Sci..
[151] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2007, ICML '07.
[152] Takafumi Kanamori,et al. Pool-based active learning with optimal sampling distribution and its information geometrical interpretation , 2007, Neurocomputing.
[153] Edwin V. Bonilla,et al. Kernel Multi-task Learning using Task-specific Features , 2007, AISTATS.
[154] Alexander J. Smola,et al. A scalable modular convex solver for regularized risk minimization , 2007, KDD '07.
[155] Thomas Hofmann,et al. Active learning for misspecified generalized linear models , 2007 .
[156] Hidetoshi Shimodaira. Testing Regions with Nonsmooth Boundaries via Multiscale Bootstrap , 2008 .
[157] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[158] Robert Tibshirani,et al. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.
[159] E. B. Davies. TOWARDS A PHILOSOPHY OF REAL MATHEMATICS , 2011 .