Fetching the paper…
Reading the bibliography…
This paper focuses on the problem of semi-supervised domain adaptation for time-series forecasting, which is underexplored in literatures, despite being often encountered in practice.
C. W. Granger, “Investigating causal relations by econometric models and cross-spectral methods,” Econometrica: journal of the Econometric Society , pp. 424–438, 1969
1969
Earlier work this paper cites.
R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological) , vol. 58, no. 1, pp. 267–288, 1996
1996
Earlier work this paper cites.
J. Pearl, “Graphs, causality, and structural equation models,” Sociological Methods & Research , vol. 27, no. 2, pp. 226–284, 1998
1998
Earlier work this paper cites.
C. Chen, K. Petty, A. Skabardonis, P. Varaiya, and Z. Jia, “Freeway performance measurement system: mining loop detector data,” Transportation Research Record , vol. 1748, no. 1, pp. 96–102, 2001
2001
Earlier work this paper cites.
H. Lütkepohl, New introduction to multiple time series analysis . Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
C. Diks and V. Panchenko, “A new statistic and practical guidelines for nonparametric granger causality testing,” Journal of Economic Dynamics and Control , vol. 30, no. 9-10, pp. 1647–1669, 2006
2006
Earlier work this paper cites.
W. W. Wei, “Time series analysis,” in The Oxford Handbook of Quantitative Methods in Psychology: Vol. 2 , 2006
2006
Earlier work this paper cites.
M. Yuan and Y. Lin, “Model selection and estimation in regression with grouped variables,” Journal of the Royal Statistical Society: Series B (Statistical Methodology) , vol. 68, no. 1, pp. 49–67, 2006
2006
Earlier work this paper cites.
A. Seth, “Granger causality,” Scholarpedia , vol. 2, no. 7, p. 1667, 2007, revision #127333
2007
Earlier work this paper cites.
S. Ben-David, J. Blitzer, K. Crammer, F. Pereira et al. , “Analysis of representations for domain adaptation,” Advances in neural information processing systems , vol. 19, p. 137, 2007
2007
Earlier work this paper cites.
S. Z. Chiou-Wei, C.-F. Chen, and Z. Zhu, “Economic growth and energy consumption revisited—evidence from linear and nonlinear granger causality,” Energy Economics , vol. 30, no. 6, pp. 3063–3076, 2008
2008
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Transactions on knowledge and data engineering , vol. 22, no. 10, pp. 1345–1359, 2009
2009
Earlier work this paper cites.
Y. Mansour, M. Mohri, and A. Rostamizadeh, “Domain adaptation: Learning bounds and algorithms,” in COLT 2009 - The 22nd Conference on Learning Theory, Montreal, Quebec, Canada, June 18-21, 2009 , 2009
2009
Earlier work this paper cites.
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang, “Domain adaptation via transfer component analysis,” IEEE transactions on neural networks , vol. 22, no. 2, pp. 199–210, 2010
2010
Earlier work this paper cites.
C. Cortes and M. Mohri, “Domain adaptation in regression,” in International Conference on Algorithmic Learning Theory . Springer, 2011, pp. 308–323
2011
Earlier work this paper cites.
K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang, “Domain adaptation under target and conditional shift,” in International Conference on Machine Learning . PMLR, 2013, pp. 819–827
2013
Earlier work this paper cites.
C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu, “Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments,” IEEE transactions on pattern analysis and machine intelligence , vol. 36, no. 7, pp. 1325–1339, 2013
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in 2nd International Conference on Learning Representations, ICLR 2014, Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings , Y. Bengio and Y. LeCun, Eds
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
E. B. Fox, M. C. Hughes, E. B. Sudderth, and M. I. Jordan, “Joint modeling of multiple time series via the beta process with application to motion capture segmentation,” The Annals of Applied Statistics , vol. 8, no. 3, pp. 1281–1313, 2014
2014
Earlier work this paper cites.
M. Long, Y. Cao, J. Wang, and M. Jordan, “Learning transferable features with deep adaptation networks,” in International conference on machine learning . PMLR, 2015, pp. 97–105
2015
Cited alongside, same era.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in International conference on machine learning . PMLR, 2015, pp. 1180–1189
2015
Cited alongside, same era.
K. Zhang, M. Gong, and B. Schölkopf, “Multi-source domain adaptation: A causal view,” in Twenty-ninth AAAI conference on artificial intelligence , 2015
2015
Cited alongside, same era.
J. Chung, K. Kastner, L. Dinh, K. Goel, A. C. Courville, and Y. Bengio, “A recurrent latent variable model for sequential data,” Advances in neural information processing systems , vol. 28, pp. 2980–2988, 2015
2015
Cited alongside, same era.
P. R. d. O. da Costa, A. Akçay, Y. Zhang, and U. Kaymak, “Remaining useful lifetime prediction via deep domain adaptation,” Reliability Engineering & System Safety , vol. 195, p. 106682, 2020
2020
Later among the works it cites.
B. Li, Y. Wang, S. Zhang, D. Li, K. Keutzer, T. Darrell, and H. Zhao, “Learning invariant representations and risks for semi-supervised domain adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1104–1113
2021
Closest in time.
P. Stojanov, Z. Li, M. Gong, R. Cai, J. Carbonell, and K. Zhang, “Domain adaptation with invariant representation learning: What transformations to learn?” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
Closest in time.
2021
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li, Forecasting Fine-Grained Air Quality Based on Big Data . New York, NY, USA: Association for Computing Machinery, 2015, p. 2267–2276. [Online]. Available: https://doi.org/10.1145/2783258.2788573
2015
Cited alongside, same era.
S. Purushotham, W. Carvalho, T. Nilanon, and Y. Liu, “Variational recurrent adversarial deep domain adaptation,” 2016
2016
Cited alongside, same era.
E. Jang, S. Gu, and B. Poole, “Categorical reparameterization with gumbel-softmax,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings
2017
Cited alongside, same era.
W. B. Nicholson, D. S. Matteson, and J. Bien, “Varx-l: Structured regularization for large vector autoregressions with exogenous variables,” International Journal of Forecasting , vol. 33, no. 3, pp. 627–651, 2017
2017
Cited alongside, same era.
L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein, “Unrolled generative adversarial networks,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings
2017
Cited alongside, same era.
K. Zhang, B. Huang, J. Zhang, C. Glymour, and B. Schölkopf, “Causal discovery from nonstationary/heterogeneous data: Skeleton estimation and orientation determination,” in IJCAI: Proceedings of the Conference , vol. 2017. NIH Public Access, 2017, p. 1347
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Z. Hao, D. Lv, Z. Li, R. Cai, W. Wen, and B. Xu, “Semi-supervised disentangled framework for transferable named entity recognition,” Neural Networks , vol. 135, pp. 127–138, 2021
2021
Closest in time.
C. Shui, Z. Li, J. Li, C. Gagné, C. X. Ling, and B. Wang, “Aggregating from multiple target-shifted sources,” in Proceedings of the 38th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 139. PMLR, 18–24 Jul 2021, pp. 9638–9648
2021
Closest in time.
Z. Li, R. Cai, H. W. Ng, M. Winslett, T. Z. Fu, B. Xu, X. Yang, and Z. Zhang, “Causal mechanism transfer network for time series domain adaptation in mechanical systems,” ACM Transactions on Intelligent Systems and Technology (TIST) , vol. 12, no. 2, pp. 1–21, 2021
2021
Closest in time.
R. Cai, J. Chen, Z. Li, W. Chen, K. Zhang, J. Ye, Z. Li, X. Yang, and Z. Zhang, “Time series domain adaptation via sparse associative structure alignment,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 8, pp. 6859–6867, May 2021. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/16846
2021
Closest in time.
R. Marcinkevics and J. E. Vogt, “Interpretable models for granger causality using self-explaining neural networks,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021
2021
Closest in time.
A. Tank, I. Covert, N. Foti, A. Shojaie, and E. B. Fox, “Neural granger causality,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–1, 2021
2021
Closest in time.
G. Valvano, A. Leo, and S. A. Tsaftaris, “Self-supervised multi-scale consistency for weakly supervised segmentation learning,” in Domain Adaptation and Representation Transfer, and Affordable Healthcare and AI for Resource Diverse Global Health . Springer, 2021, pp. 14–24
2021
Closest in time.
H. Wu, J. Xu, J. Wang, and M. Long, “Autoformer: Decomposition transformers with Auto-Correlation for long-term series forecasting,” in Advances in Neural Information Processing Systems , 2021
2021
Closest in time.
2021
Closest in time.
A. Tank, I. Covert, N. Foti, A. Shojaie, and E. B. Fox, “Neural granger causality,” IEEE Transactions on Pattern Analysis and Machine Intelligence , pp. 1–1, 2021
2021
Closest in time.
H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 12, 2021, pp. 11 106–11 115
2021
Closest in time.
X. Jin, Y. Park, D. Maddix, H. Wang, and Y. Wang, “Domain adaptation for time series forecasting via attention sharing,” in International Conference on Machine Learning . PMLR, 2022, pp. 10 280–10 297
2022
Closest in time.
S. Löwe, D. Madras, R. Z. Shilling, and M. Welling, “Amortized causal discovery: Learning to infer causal graphs from time-series data,” in 1st Conference on Causal Learning and Reasoning, CLeaR 2022, Sequoia Conference Center, Eureka, CA, USA, 11-13 April, 2022
2022
Closest in time.
A. Gu, K. Goel, A. Gupta, and C. Ré, “On the parameterization and initialization of diagonal state space models,” Advances in Neural Information Processing Systems , vol. 35, pp. 35 971–35 983, 2022
2022
Closest in time.
R. Cai, Z. Li, P. Wei, J. Qiao, K. Zhang, and Z. Hao, “Learning disentangled semantic representation for domain adaptation,” in IJCAI: proceedings of the conference , vol. 2019. NIH Public Access, 2019, p. 2060
2060
Closest in time.