Multiple-source adaptation theory and algorithms
We present a general theoretical and algorithmic analysis of the problem of multiple-source adaptation, a key learning problem in applications. We derive new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees th...
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| Vydané v: | Annals of mathematics and artificial intelligence Ročník 89; číslo 3-4; s. 237 - 270 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Cham
Springer International Publishing
01.03.2021
Springer Springer Nature B.V |
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| ISSN: | 1012-2443, 1573-7470 |
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| Abstract | We present a general theoretical and algorithmic analysis of the problem of multiple-source adaptation, a key learning problem in applications. We derive new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. We further present a novel analysis of the convergence properties of density estimation used in distribution-weighted combinations, and study their effects on the learning guarantees. Moreover, we give new algorithms for determining the distribution-weighted combination solution for the cross-entropy loss and other losses. We report the results of a series of experiments with real-world datasets. We find that our algorithm outperforms competing approaches by producing a single robust predictor that performs well on any target mixture distribution. Altogether, our theory, algorithms, and empirical results provide a full solution for the multiple-source adaptation problem with very practical benefits. |
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| AbstractList | We present a general theoretical and algorithmic analysis of the problem of multiple-source adaptation, a key learning problem in applications. We derive new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. We further present a novel analysis of the convergence properties of density estimation used in distribution-weighted combinations, and study their effects on the learning guarantees. Moreover, we give new algorithms for determining the distribution-weighted combination solution for the cross-entropy loss and other losses. We report the results of a series of experiments with real-world datasets. We find that our algorithm outperforms competing approaches by producing a single robust predictor that performs well on any target mixture distribution. Altogether, our theory, algorithms, and empirical results provide a full solution for the multiple-source adaptation problem with very practical benefits. We present a general theoretical and algorithmic analysis of the problem of multiple-source adaptation, a key learning problem in applications. We derive new normalized solutions with strong theoretical guarantees for the cross-entropy loss and other similar losses. We also provide new guarantees that hold in the case where the conditional probabilities for the source domains are distinct. We further present a novel analysis of the convergence properties of density estimation used in distribution-weighted combinations, and study their effects on the learning guarantees. Moreover, we give new algorithms for determining the distribution-weighted combination solution for the cross-entropy loss and other losses. We report the results of a series of experiments with real-world datasets. We find that our algorithm outperforms competing approaches by producing a single robust predictor that performs well on any target mixture distribution. Altogether, our theory, algorithms, and empirical results provide a full solution for the multiple-source adaptation problem with very practical benefits. Keywords Domain adaptation * Multiple-source adaptation * Renyi divergence * Transfer learning * DC programming Mathematics Subject Classification (2010) 68T05 |
| Audience | Academic |
| Author | Hoffman, Judy Zhang, Ningshan Mohri, Mehryar |
| Author_xml | – sequence: 1 givenname: Ningshan orcidid: 0000-0002-0175-8759 surname: Zhang fullname: Zhang, Ningshan email: nzhang@stern.nyu.edu organization: New York University – sequence: 2 givenname: Mehryar surname: Mohri fullname: Mohri, Mehryar organization: Google Research and Courant Institute of Mathematical Sciences – sequence: 3 givenname: Judy surname: Hoffman fullname: Hoffman, Judy organization: School of Interactive Computing, Georgia Institute of Technology |
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| Cites_doi | 10.1016/0885-064X(89)90017-4 10.1007/s10472-018-9613-y 10.1109/CVPR.2014.81 10.1023/A:1021765131316 10.1137/0115116 10.1109/CVPR.2017.316 10.1145/1390156.1390190 10.1016/j.tcs.2013.09.027 10.1007/978-3-642-15561-1_16 10.1145/1379759.1379761 10.1109/ICASSP.2013.6639212 10.1145/1772690.1772767 10.1609/aaai.v29i1.9542 10.1109/TIT.2014.2320500 10.7551/mitpress/7503.003.0022 10.1109/TNNLS.2011.2178556 10.1145/1553374.1553411 10.1073/pnas.61.4.1238 10.1109/ASRU.2011.6163899 10.1162/08997660360581958 10.1109/LSP.2014.2324759 10.2200/S00416ED1V01Y201204HLT016 10.1007/978-3-642-33718-5_12 10.1007/BF01456868 10.1162/NECO_a_00283 10.1145/1291233.1291276 10.7551/mitpress/7503.003.0080 10.1007/978-3-319-10578-9_41 10.1145/800057.808710 10.1007/978-3-642-33709-3_50 10.1007/BF01448847 10.1111/j.1751-5823.2002.tb00178.x 10.1162/COLI_a_00049 10.1145/2783258.2783368 10.1109/ICCV.2011.6126344 10.1007/BF01584975 10.1109/ICCV.2015.463 10.1109/TPAMI.2002.1008382 10.1109/CVPR.2011.5995347 10.1137/S1052623494274313 10.1609/aaai.v32i1.11767 |
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| Keywords | Domain adaptation DC programming Rényi divergence 68T05 Multiple-source adaptation Transfer learning |
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| References | Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. In: ICML, vol. 32, pp 647–655 (2014) Long, M., Cao, Y., Wang, J., Jordan, M. I.: Learning transferable features with deep adaptation networks. In: ICML, vol. 37, pp 97–105 (2015) Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: ICCV, pp 4068–4076 (2015) Girshick, R. B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp 580–587 (2014) CoverTMThomasJMElements of Information Theory2006New YorkWiley-Interscience1140.94001 Pan, S. J., Ni, X., Sun, J. -T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th International conference on World Wide Web, pp 751–760 (2010) Dredze, M., Crammer, K., Pereira, F.: Confidence-weighted linear classification. In: ICML, vol. 307, pp 264–271 (2008) Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp 2066–2073 (2012) Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. In: ICLR (2017) VapnikVStatistical Learning Theory1998New YorkWiley0935.62007 Gong, B., Grauman, K., Sha, F.: Reshaping visual datasets for domain adaptation. In: NIPS, pp 1286–1294 (2013b) Jiang, J., Zhai, C.: Instance weighting for domain adaptation in nlp. In: Annual Meeting of the Association of Computational Linguistics, pp 264–271 (2007) Rényi, A.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, pp 547–561 (1961) TaoPDAnLTHA DC optimization algorithm for solving the trust-region subproblemSIAM J. Optim.199882476505161853110.1137/S1052623494274313 DengJZhangZEybenFSchullerBAutoencoder-based unsupervised domain adaptation for speech emotion recognitionIEEE Signal Process. Lett.20142191068107210.1109/LSP.2014.2324759 Zhang, K., Gong, M., Schölkopf, B.: Multi-source domain adaptation: a causal view. In: AAAI, pp 3150–3157 (2015) Gong, B., Grauman, K., Sha, F.: Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation. In: ICML, vol. 8, pp 222–230 (2013a) ScarfHThe approximation of fixed points of a continuous mappingSIAM J. Appl. Math.19671551328134324248310.1137/0115116 KuhnHSimplicial approximations of fixed pointsProc. Natl Acad. Sci.19686141238124248801010.1073/pnas.61.4.1238 Valiant, L. G.: A theory of the learnable. In: Annual ACM Symposium on Theory of Computing, pp 436–445 (1984) Ganin, Y., Lempitsky, V. S.: Unsupervised domain adaptation by backpropagation. In: ICML, vol. 37, pp 1180–1189 (2015) DuanLXuDTsangIWDomain adaptation from multiple sources: a domain-dependent regularization approachIEEE Trans. Neural Netw. Learn. Syst.201223350451810.1109/TNNLS.2011.2178556 TaboadaMBrookeJTofiloskiMVollKStedeMLexicon-based methods for sentiment analysisComput. Linguist.201137226730710.1162/COLI_a_00049 HorstRThoaiNVDC programming: overviewJ. Optim. Theory Appl.19991031143171501610.1023/A:1021765131316 Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: ICML, pp 513–520 (2011) Xu, Z., Li, W., Niu, L., Xu, D.: Exploiting low-rank structure from latent domains for domain generalization. In: ECCV, vol. 8691, pp 628–643 (2014) Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: ICCV, pp 999–1006. IEEE (2011) Blanchard, G., Lee, G., Scott, C.: Generalizing from several related classification tasks to a new unlabeled sample. In: NIPS, pp 2178–2186 (2011) Merrill, O. H.: Applications and Extensions of an Algorithm That Computes Fixed Points of Certain Upper Semi-continuous Point to Set Mappings. PhD thesis, Dept. of Industrial Engineering. University of Michigan (1972) Liao, H.: Speaker adaptation of context dependent deep neural networks. In: ICASSP, pp 7947–7951 (2013) CrammerKKearnsMJWortmanJLearning from multiple sourcesJ. Mach. Learn. Res.200891757177424388231225.68168 CortesCGreenbergSMohriMRelative deviation learning bounds and generalization with unbounded loss functionsAnn. Math. Artif. Intell.20198514570390194610.1007/s10472-018-9613-y ChenXDengXMatching algorithmic bounds for finding a brouwer fixed pointJ. ACM200855313:113:26244491110.1145/1379759.1379761 Muandet, K., Balduzzi, D., Schölkopf, B.: Domain generalization via invariant feature representation. In: ICML, vol. 28, pp 10–18 (2013) Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent deep neural networks for conversational speech transcription. In: 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, pp 24–29. IEEE (2011) Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: learning bounds and algorithms. In: COLT (2009b) Roark, B., Sproat, R., Allauzen, C., Riley, M., Sorensen, J., Tai, T.: The opengrm open-source finite-state grammar software libraries. In: ACL (System Demonstrations), pp 61–66 (2012) Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: ECCV, vol. 6314, pp 213–226 (2010) von NeumannJZur theorie der gesellschaftsspieleMath. Ann.19281001295320151248610.1007/BF01448847 Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Conference on Computer Vision and Pattern Recognition, pp 7167–7176 (2017) MartínezAMRecognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per classIEEE Trans. Pattern Anal. Mach. Intell.200224674876310.1109/TPAMI.2002.1008382 ArndtCInformation Measures: Information and its Description in Science and Engineering. Signals and Communication Technology2004New YorkSpringer Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1106–1114 (2012) BrouwerLEJÜber eineindeutige, stetige Transformationen von Flächen in sichMath. Ann.1910692176180151158210.1007/BF01456868Springer Huang, J., Smola, A. J., Gretton, A., Borgwardt, K. M., Schölkopf, B.: Correcting sample selection bias by unlabeled data. In: NIPS, pp 601–608 (2006) Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: NIPS, pp 137–144 (2006) Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, pp 440–447 (2007) CortesCMohriMDomain adaptation and sample bias correction theory and algorithm for regressionTheor. Comput. Sci.2014519103126314799110.1016/j.tcs.2013.09.027 Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., Torralba, A.: Undoing the damage of dataset bias. In: ECCV, vol. 7572, pp 158–171 (2012) TaoPDAnLTHConvex analysis approach to DC programming: theory, algorithms and applicationsActa Math. Vietnam.199722128935514797510895.90152 Hoffman, J., Mohri, M., Zhang, N.: Algorithms and theory for multiple-source adaptation. In: Advances in Neural Information Processing Systems, pp 8246–8256 (2018) Yang, J., Yan, R., Hauptmann, A. G.: Cross-domain video concept detection using adaptive svms. In: ACM Multimedia, pp 188–197 (2007) Mansour, Y., Mohri, M., Rostamizadeh, A.: Multiple source adaptation and the Rényi divergence. In: UAI, pp 367–374 (2009a) Hoffman, J., Kulis, B., Darrell, T., Saenko, K.: Discovering latent domains for multisource domain adaptation. In: ECCV, vol. 7573, pp 702–715 (2012) HirschMDPapadimitriouCHVavasisSAExponential lower bounds for finding brouwer fix pointsJ. Complex.19895437941610.1016/0885-064X(89)90017-4 Duan, L., Tsang, I. W., Xu, D., Chua, T.: Domain adaptation from multiple sources via auxiliary classifiers. In: ICML, vol. 382, pp 289–296 (2009) YuilleALRangarajanAThe concave-convex procedureNeural Comput.200315491593610.1162/08997660360581958 Cortes, C., Mohri, M., Muñoz Medina, A.: Adaptation algorithm and theory based on generalized discrepancy. In: KDD, pp 169–178 (2015) EavesBCHomotopies for computation of fixed pointsMath. Program.19723112230395310.1007/BF01584975 Van ErvenTHarremosPRényi divergence and kullback-leibler divergenceIEEE Trans. Inf. Theory20146073797382010.1109/TIT.2014.2320500 Hoffman, J., Rodner, E., Donahue, J., Saenko, K., Darrell, T.: Efficient learning of domain-invariant image representations. In: ICLR (2013) LiuBSentiment analysis and opinion miningSynth. Lect. Hum. Lang. Technol.201251116710.2200/S00416ED1V01Y201204HLT016 GibbsALSuFEOn choosing and bounding probability metricsInt. Stat. Rev./Rev. Int. Stat.200270341943510.1111/j.1751-5823.2002.tb00178.x Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation with multiple sources. In: NIPS, pp 1041–1048 (2008) SriperumbudurBKLanckrietGRGA proof of convergence of the concave-convex procedure using Zangwill’s theoryNeural Comput.201224613911407296207210.1162/NECO_a_00283 Daumé, H III.: Frustratingly easy domain adaptation. In: Annual Meeting of the Association for Computational Linguistics (2007) Pei, Z., Cao, Z., Long, M., Wang, J.: Multi-adversarial domain adaptation. In: AAAI, pp 3934–3941 (2018) Torralba, A., Efros, A. A.: Unbiased look at dataset bias. In: CVPR, pp 1521–1528 (2011) 9716_CR47 9716_CR48 9716_CR45 9716_CR46 9716_CR44 9716_CR41 9716_CR42 9716_CR40 L Duan (9716_CR17) 2012; 23 MD Hirsch (9716_CR27) 1989; 5 9716_CR49 H Kuhn (9716_CR36) 1968; 61 9716_CR37 9716_CR34 9716_CR35 9716_CR32 9716_CR33 TM Cover (9716_CR10) 2006 9716_CR30 PD Tao (9716_CR57) 1998; 8 PD Tao (9716_CR56) 1997; 22 X Chen (9716_CR6) 2008; 55 J Deng (9716_CR13) 2014; 21 C Cortes (9716_CR9) 2019; 85 V Vapnik (9716_CR63) 1998 9716_CR39 9716_CR25 9716_CR26 9716_CR23 9716_CR24 9716_CR68 9716_CR21 9716_CR65 9716_CR22 9716_CR66 9716_CR61 H Scarf (9716_CR51) 1967; 15 M Taboada (9716_CR54) 2011; 37 C Arndt (9716_CR1) 2004 9716_CR60 K Crammer (9716_CR11) 2008; 9 BC Eaves (9716_CR18) 1972; 3 C Cortes (9716_CR7) 2014; 519 9716_CR29 9716_CR28 9716_CR14 AM Martínez (9716_CR43) 2002; 24 9716_CR58 9716_CR15 9716_CR59 9716_CR12 B Liu (9716_CR38) 2012; 5 9716_CR55 9716_CR52 9716_CR50 R Horst (9716_CR31) 1999; 103 J von Neumann (9716_CR64) 1928; 100 9716_CR8 9716_CR3 AL Gibbs (9716_CR20) 2002; 70 9716_CR4 BK Sriperumbudur (9716_CR53) 2012; 24 LEJ Brouwer (9716_CR5) 1910; 69 9716_CR2 T Van Erven (9716_CR62) 2014; 60 9716_CR19 9716_CR16 AL Yuille (9716_CR67) 2003; 15 |
| References_xml | – reference: Gong, B., Grauman, K., Sha, F.: Connecting the dots with landmarks: discriminatively learning domain-invariant features for unsupervised domain adaptation. In: ICML, vol. 8, pp 222–230 (2013a) – reference: Gong, B., Shi, Y., Sha, F., Grauman, K.: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp 2066–2073 (2012) – reference: TaoPDAnLTHConvex analysis approach to DC programming: theory, algorithms and applicationsActa Math. Vietnam.199722128935514797510895.90152 – reference: Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation with multiple sources. In: NIPS, pp 1041–1048 (2008) – reference: Pan, S. J., Ni, X., Sun, J. -T., Yang, Q., Chen, Z.: Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th International conference on World Wide Web, pp 751–760 (2010) – reference: Muandet, K., Balduzzi, D., Schölkopf, B.: Domain generalization via invariant feature representation. In: ICML, vol. 28, pp 10–18 (2013) – reference: Ganin, Y., Lempitsky, V. S.: Unsupervised domain adaptation by backpropagation. In: ICML, vol. 37, pp 1180–1189 (2015) – reference: HirschMDPapadimitriouCHVavasisSAExponential lower bounds for finding brouwer fix pointsJ. Complex.19895437941610.1016/0885-064X(89)90017-4 – reference: von NeumannJZur theorie der gesellschaftsspieleMath. Ann.19281001295320151248610.1007/BF01448847 – reference: Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: NIPS, pp 137–144 (2006) – reference: Hoffman, J., Rodner, E., Donahue, J., Saenko, K., Darrell, T.: Efficient learning of domain-invariant image representations. In: ICLR (2013) – reference: Cortes, C., Mohri, M., Muñoz Medina, A.: Adaptation algorithm and theory based on generalized discrepancy. In: KDD, pp 169–178 (2015) – reference: Torralba, A., Efros, A. A.: Unbiased look at dataset bias. In: CVPR, pp 1521–1528 (2011) – reference: Gong, B., Grauman, K., Sha, F.: Reshaping visual datasets for domain adaptation. In: NIPS, pp 1286–1294 (2013b) – reference: Blanchard, G., Lee, G., Scott, C.: Generalizing from several related classification tasks to a new unlabeled sample. In: NIPS, pp 2178–2186 (2011) – reference: Seide, F., Li, G., Chen, X., Yu, D.: Feature engineering in context-dependent deep neural networks for conversational speech transcription. In: 2011 IEEE Workshop on Automatic Speech Recognition & Understanding, pp 24–29. IEEE (2011) – reference: Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. In: ICLR (2017) – reference: DuanLXuDTsangIWDomain adaptation from multiple sources: a domain-dependent regularization approachIEEE Trans. Neural Netw. Learn. Syst.201223350451810.1109/TNNLS.2011.2178556 – reference: CrammerKKearnsMJWortmanJLearning from multiple sourcesJ. Mach. Learn. Res.200891757177424388231225.68168 – reference: YuilleALRangarajanAThe concave-convex procedureNeural Comput.200315491593610.1162/08997660360581958 – reference: Saenko, K., Kulis, B., Fritz, M., Darrell, T.: Adapting visual category models to new domains. In: ECCV, vol. 6314, pp 213–226 (2010) – reference: SriperumbudurBKLanckrietGRGA proof of convergence of the concave-convex procedure using Zangwill’s theoryNeural Comput.201224613911407296207210.1162/NECO_a_00283 – reference: Valiant, L. G.: A theory of the learnable. In: Annual ACM Symposium on Theory of Computing, pp 436–445 (1984) – reference: CortesCGreenbergSMohriMRelative deviation learning bounds and generalization with unbounded loss functionsAnn. Math. Artif. Intell.20198514570390194610.1007/s10472-018-9613-y – reference: CoverTMThomasJMElements of Information Theory2006New YorkWiley-Interscience1140.94001 – reference: TaboadaMBrookeJTofiloskiMVollKStedeMLexicon-based methods for sentiment analysisComput. Linguist.201137226730710.1162/COLI_a_00049 – reference: BrouwerLEJÜber eineindeutige, stetige Transformationen von Flächen in sichMath. Ann.1910692176180151158210.1007/BF01456868Springer – reference: Girshick, R. B., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: CVPR, pp 580–587 (2014) – reference: LiuBSentiment analysis and opinion miningSynth. Lect. Hum. Lang. Technol.201251116710.2200/S00416ED1V01Y201204HLT016 – reference: Dredze, M., Crammer, K., Pereira, F.: Confidence-weighted linear classification. In: ICML, vol. 307, pp 264–271 (2008) – reference: ChenXDengXMatching algorithmic bounds for finding a brouwer fixed pointJ. ACM200855313:113:26244491110.1145/1379759.1379761 – reference: KuhnHSimplicial approximations of fixed pointsProc. Natl Acad. Sci.19686141238124248801010.1073/pnas.61.4.1238 – reference: Long, M., Cao, Y., Wang, J., Jordan, M. I.: Learning transferable features with deep adaptation networks. In: ICML, vol. 37, pp 97–105 (2015) – reference: Duan, L., Tsang, I. W., Xu, D., Chua, T.: Domain adaptation from multiple sources via auxiliary classifiers. In: ICML, vol. 382, pp 289–296 (2009) – reference: Gopalan, R., Li, R., Chellappa, R.: Domain adaptation for object recognition: an unsupervised approach. In: ICCV, pp 999–1006. IEEE (2011) – reference: ScarfHThe approximation of fixed points of a continuous mappingSIAM J. Appl. Math.19671551328134324248310.1137/0115116 – reference: Zhang, K., Gong, M., Schölkopf, B.: Multi-source domain adaptation: a causal view. In: AAAI, pp 3150–3157 (2015) – reference: Roark, B., Sproat, R., Allauzen, C., Riley, M., Sorensen, J., Tai, T.: The opengrm open-source finite-state grammar software libraries. In: ACL (System Demonstrations), pp 61–66 (2012) – reference: Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: Simultaneous deep transfer across domains and tasks. In: ICCV, pp 4068–4076 (2015) – reference: MartínezAMRecognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per classIEEE Trans. Pattern Anal. Mach. Intell.200224674876310.1109/TPAMI.2002.1008382 – reference: Merrill, O. H.: Applications and Extensions of an Algorithm That Computes Fixed Points of Certain Upper Semi-continuous Point to Set Mappings. PhD thesis, Dept. of Industrial Engineering. University of Michigan (1972) – reference: HorstRThoaiNVDC programming: overviewJ. Optim. Theory Appl.19991031143171501610.1023/A:1021765131316 – reference: Van ErvenTHarremosPRényi divergence and kullback-leibler divergenceIEEE Trans. Inf. Theory20146073797382010.1109/TIT.2014.2320500 – reference: Xu, Z., Li, W., Niu, L., Xu, D.: Exploiting low-rank structure from latent domains for domain generalization. In: ECCV, vol. 8691, pp 628–643 (2014) – reference: TaoPDAnLTHA DC optimization algorithm for solving the trust-region subproblemSIAM J. Optim.199882476505161853110.1137/S1052623494274313 – reference: Yang, J., Yan, R., Hauptmann, A. G.: Cross-domain video concept detection using adaptive svms. In: ACM Multimedia, pp 188–197 (2007) – reference: Jiang, J., Zhai, C.: Instance weighting for domain adaptation in nlp. In: Annual Meeting of the Association of Computational Linguistics, pp 264–271 (2007) – reference: Glorot, X., Bordes, A., Bengio, Y.: Domain adaptation for large-scale sentiment classification: a deep learning approach. In: ICML, pp 513–520 (2011) – reference: Pei, Z., Cao, Z., Long, M., Wang, J.: Multi-adversarial domain adaptation. In: AAAI, pp 3934–3941 (2018) – reference: Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: Decaf: a deep convolutional activation feature for generic visual recognition. In: ICML, vol. 32, pp 647–655 (2014) – reference: Hoffman, J., Kulis, B., Darrell, T., Saenko, K.: Discovering latent domains for multisource domain adaptation. In: ECCV, vol. 7573, pp 702–715 (2012) – reference: GibbsALSuFEOn choosing and bounding probability metricsInt. Stat. Rev./Rev. Int. Stat.200270341943510.1111/j.1751-5823.2002.tb00178.x – reference: Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: ACL, pp 440–447 (2007) – reference: Hoffman, J., Mohri, M., Zhang, N.: Algorithms and theory for multiple-source adaptation. In: Advances in Neural Information Processing Systems, pp 8246–8256 (2018) – reference: Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: Conference on Computer Vision and Pattern Recognition, pp 7167–7176 (2017) – reference: Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: learning bounds and algorithms. In: COLT (2009b) – reference: Khosla, A., Zhou, T., Malisiewicz, T., Efros, A. A., Torralba, A.: Undoing the damage of dataset bias. In: ECCV, vol. 7572, pp 158–171 (2012) – reference: DengJZhangZEybenFSchullerBAutoencoder-based unsupervised domain adaptation for speech emotion recognitionIEEE Signal Process. Lett.20142191068107210.1109/LSP.2014.2324759 – reference: Huang, J., Smola, A. J., Gretton, A., Borgwardt, K. M., Schölkopf, B.: Correcting sample selection bias by unlabeled data. In: NIPS, pp 601–608 (2006) – reference: Liao, H.: Speaker adaptation of context dependent deep neural networks. In: ICASSP, pp 7947–7951 (2013) – reference: Rényi, A.: On measures of entropy and information. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, pp 547–561 (1961) – reference: CortesCMohriMDomain adaptation and sample bias correction theory and algorithm for regressionTheor. Comput. Sci.2014519103126314799110.1016/j.tcs.2013.09.027 – reference: Mansour, Y., Mohri, M., Rostamizadeh, A.: Multiple source adaptation and the Rényi divergence. In: UAI, pp 367–374 (2009a) – reference: Daumé, H III.: Frustratingly easy domain adaptation. In: Annual Meeting of the Association for Computational Linguistics (2007) – reference: VapnikVStatistical Learning Theory1998New YorkWiley0935.62007 – reference: ArndtCInformation Measures: Information and its Description in Science and Engineering. Signals and Communication Technology2004New YorkSpringer – reference: EavesBCHomotopies for computation of fixed pointsMath. Program.19723112230395310.1007/BF01584975 – reference: Krizhevsky, A., Sutskever, I., Hinton, G. E.: Imagenet classification with deep convolutional neural networks. In: NIPS, pp 1106–1114 (2012) – volume: 5 start-page: 379 issue: 4 year: 1989 ident: 9716_CR27 publication-title: J. Complex. doi: 10.1016/0885-064X(89)90017-4 – volume: 85 start-page: 45 issue: 1 year: 2019 ident: 9716_CR9 publication-title: Ann. Math. Artif. Intell. doi: 10.1007/s10472-018-9613-y – ident: 9716_CR21 doi: 10.1109/CVPR.2014.81 – volume: 103 start-page: 1 issue: 1 year: 1999 ident: 9716_CR31 publication-title: J. Optim. Theory Appl. doi: 10.1023/A:1021765131316 – volume: 9 start-page: 1757 year: 2008 ident: 9716_CR11 publication-title: J. Mach. Learn. Res. – ident: 9716_CR40 – ident: 9716_CR25 – ident: 9716_CR44 – ident: 9716_CR48 – volume: 15 start-page: 1328 issue: 5 year: 1967 ident: 9716_CR51 publication-title: SIAM J. Appl. Math. doi: 10.1137/0115116 – ident: 9716_CR29 – ident: 9716_CR60 doi: 10.1109/CVPR.2017.316 – ident: 9716_CR35 – ident: 9716_CR4 – ident: 9716_CR12 – ident: 9716_CR15 doi: 10.1145/1390156.1390190 – volume: 519 start-page: 103 year: 2014 ident: 9716_CR7 publication-title: Theor. Comput. Sci. doi: 10.1016/j.tcs.2013.09.027 – ident: 9716_CR50 doi: 10.1007/978-3-642-15561-1_16 – volume: 55 start-page: 13:1 issue: 3 year: 2008 ident: 9716_CR6 publication-title: J. ACM doi: 10.1145/1379759.1379761 – ident: 9716_CR37 doi: 10.1109/ICASSP.2013.6639212 – ident: 9716_CR39 – ident: 9716_CR24 – ident: 9716_CR46 doi: 10.1145/1772690.1772767 – volume-title: Information Measures: Information and its Description in Science and Engineering. Signals and Communication Technology year: 2004 ident: 9716_CR1 – ident: 9716_CR41 – ident: 9716_CR68 doi: 10.1609/aaai.v29i1.9542 – volume: 60 start-page: 3797 issue: 7 year: 2014 ident: 9716_CR62 publication-title: IEEE Trans. Inf. Theory doi: 10.1109/TIT.2014.2320500 – ident: 9716_CR49 – ident: 9716_CR2 doi: 10.7551/mitpress/7503.003.0022 – volume: 23 start-page: 504 issue: 3 year: 2012 ident: 9716_CR17 publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2011.2178556 – ident: 9716_CR45 – ident: 9716_CR16 doi: 10.1145/1553374.1553411 – volume: 22 start-page: 289 issue: 1 year: 1997 ident: 9716_CR56 publication-title: Acta Math. Vietnam. – volume-title: Elements of Information Theory year: 2006 ident: 9716_CR10 – ident: 9716_CR30 – ident: 9716_CR55 – volume: 61 start-page: 1238 issue: 4 year: 1968 ident: 9716_CR36 publication-title: Proc. Natl Acad. Sci. doi: 10.1073/pnas.61.4.1238 – ident: 9716_CR23 – ident: 9716_CR52 doi: 10.1109/ASRU.2011.6163899 – ident: 9716_CR42 – volume: 15 start-page: 915 issue: 4 year: 2003 ident: 9716_CR67 publication-title: Neural Comput. doi: 10.1162/08997660360581958 – volume: 21 start-page: 1068 issue: 9 year: 2014 ident: 9716_CR13 publication-title: IEEE Signal Process. Lett. doi: 10.1109/LSP.2014.2324759 – volume: 5 start-page: 1 issue: 1 year: 2012 ident: 9716_CR38 publication-title: Synth. Lect. Hum. Lang. Technol. doi: 10.2200/S00416ED1V01Y201204HLT016 – ident: 9716_CR33 – ident: 9716_CR34 doi: 10.1007/978-3-642-33718-5_12 – volume: 69 start-page: 176 issue: 2 year: 1910 ident: 9716_CR5 publication-title: Math. Ann. doi: 10.1007/BF01456868 – volume: 24 start-page: 1391 issue: 6 year: 2012 ident: 9716_CR53 publication-title: Neural Comput. doi: 10.1162/NECO_a_00283 – ident: 9716_CR66 doi: 10.1145/1291233.1291276 – ident: 9716_CR14 – ident: 9716_CR32 doi: 10.7551/mitpress/7503.003.0080 – ident: 9716_CR65 doi: 10.1007/978-3-319-10578-9_41 – ident: 9716_CR22 – volume-title: Statistical Learning Theory year: 1998 ident: 9716_CR63 – ident: 9716_CR61 doi: 10.1145/800057.808710 – ident: 9716_CR28 doi: 10.1007/978-3-642-33709-3_50 – volume: 100 start-page: 295 issue: 1 year: 1928 ident: 9716_CR64 publication-title: Math. Ann. doi: 10.1007/BF01448847 – volume: 70 start-page: 419 issue: 3 year: 2002 ident: 9716_CR20 publication-title: Int. Stat. Rev./Rev. Int. Stat. doi: 10.1111/j.1751-5823.2002.tb00178.x – volume: 37 start-page: 267 issue: 2 year: 2011 ident: 9716_CR54 publication-title: Comput. Linguist. doi: 10.1162/COLI_a_00049 – ident: 9716_CR8 doi: 10.1145/2783258.2783368 – ident: 9716_CR3 – ident: 9716_CR26 doi: 10.1109/ICCV.2011.6126344 – volume: 3 start-page: 1 issue: 1 year: 1972 ident: 9716_CR18 publication-title: Math. Program. doi: 10.1007/BF01584975 – ident: 9716_CR59 doi: 10.1109/ICCV.2015.463 – volume: 24 start-page: 748 issue: 6 year: 2002 ident: 9716_CR43 publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2002.1008382 – ident: 9716_CR58 doi: 10.1109/CVPR.2011.5995347 – ident: 9716_CR19 – volume: 8 start-page: 476 issue: 2 year: 1998 ident: 9716_CR57 publication-title: SIAM J. Optim. doi: 10.1137/S1052623494274313 – ident: 9716_CR47 doi: 10.1609/aaai.v32i1.11767 |
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