Fetching the paper…
Reading the bibliography…
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of finite samples), which greatly limits the teacher's capability.
P. J. Werbos, “Generalization of backpropagation with application to a recurrent gas market model,” Neural networks
1988
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning
1992
Earlier work this paper cites.
G.-H. Lin and M. Fukushima, “Some exact penalty results for nonlinear programs and mathematical programs with equilibrium constraints,” Journal of Optimization Theory and Applications
2003
Earlier work this paper cites.
I. W. Tsang, J. T. Kwok, P.-M. Cheung, and N. Cristianini, “Core vector machines: Fast svm training on very large data sets.,” Journal of Machine Learning Research
2005
Earlier work this paper cites.
A. Angelova, Y. Abu-Mostafam, and P. Perona, “Pruning training sets for learning of object categories,” in CVPR
2005
Earlier work this paper cites.
Princeton University Press, 2007
D. A. MacKenzie, F. Muniesa, L. Siu, et al · 2007
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in ICML
2009
Earlier work this paper cites.
A. Singla, I. Bogunovic, G. Bartók, A. Karbasi, and A. Krause, “On actively teaching the crowd to classify,” in NeurIPS Workshop on Data Driven Education
2013
Earlier work this paper cites.
X. Zhu, “Machine teaching for bayesian learners in the exponential family,” in NeurIPS
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
2013
Earlier work this paper cites.
A. Singla, I. Bogunovic, G. Bartok, A. Karbasi, and A. Krause, “Near-optimally teaching the crowd to classify.,” in ICML
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
X. Zhu, “Machine teaching: An inverse problem to machine learning and an approach toward optimal education.,” in AAAI
2015
Earlier work this paper cites.
E. Johns, O. Mac Aodha, and G. J. Brostow, “Becoming the expert-interactive multi-class machine teaching,” in CVPR
2015
Earlier work this paper cites.
S. Mei and X. Zhu, “Using machine teaching to identify optimal training-set attacks on machine learners.,” in AAAI
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
K. Healy, “The performativity of networks,” European Journal of Sociology/Archives Européennes de Sociologie
2015
Earlier work this paper cites.
S. Alfeld, X. Zhu, and P. Barford, “Data poisoning attacks against autoregressive models.,” in AAAI
2016
Earlier work this paper cites.
J. Liu, X. Zhu, and H. G. Ohannessian, “The teaching dimension of linear learners,” in ICML
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. de Freitas, “Learning to learn by gradient descent by gradient descent,” in NeurIPS
2016
Earlier work this paper cites.
S. Alfeld, X. Zhu, and P. Barford, “Explicit defense actions against test-set attacks,” in AAAI
2017
Earlier work this paper cites.
W. Liu, B. Dai, A. Humayun, C. Tay, C. Yu, L. B. Smith, J. M. Rehg, and L. Song, “Iterative machine teaching,” in ICML
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Communications of the ACM
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in ICML
2017
Earlier work this paper cites.
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, “Sphereface: Deep hypersphere embedding for face recognition,” in CVPR
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Zhou, A. R. Nelakurthi, and J. He, “Unlearn what you have learned: Adaptive crowd teaching with exponentially decayed memory learners,” in KDD
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Chen, A. Singla, O. Mac Aodha, P. Perona, and Y. Yue, “Understanding the role of adaptivity in machine teaching: The case of version space learners,” in NeurIPS
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in CVPR
2019
Later among the works it cites.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in CVPR
2019
Later among the works it cites.
M. Zhang, J. Lucas, J. Ba, and G. E. Hinton, “Lookahead optimizer: k steps forward, 1 step back,” in NeurIPS
2019
Later among the works it cites.
S. Vaswani, A. Mishkin, I. Laradji, M. Schmidt, G. Gidel, and S. Lacoste-Julien, “Painless stochastic gradient: Interpolation, line-search, and convergence rates,” in NeurIPS
2019
Later among the works it cites.
Y. Zhou, A. R. Nelakurthi, R. Maciejewski, W. Fan, and J. He, “Crowd teaching with imperfect labels,” in WWW
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
W. Liu, B. Dai, X. Li, Z. Liu, J. Rehg, and L. Song, “Towards black-box iterative machine teaching,” in ICML
2018
Cited alongside, same era.
L. Haug, S. Tschiatschek, and A. Singla, “Teaching inverse reinforcement learners via features and demonstrations,” in NeurIPS
2018
Cited alongside, same era.
Y. Chen, O. Mac Aodha, S. Su, P. Perona, and Y. Yue, “Near-optimal machine teaching via explanatory teaching sets,” in AISTATS
2018
Cited alongside, same era.
O. Mac Aodha, S. Su, Y. Chen, P. Perona, and Y. Yue, “Teaching categories to human learners with visual explanations,” in CVPR
2018
Cited alongside, same era.
X. Zhang, X. Zhu, and S. Wright, “Training set debugging using trusted items,” in AAAI
2018
Cited alongside, same era.
T. Campbell and T. Broderick, “Bayesian coreset construction via greedy iterative geodesic ascent,” in ICML
2018
Cited alongside, same era.
O. Sener and S. Savarese, “Active learning for convolutional neural networks: A core-set approach,” in ICLR
2018
Cited alongside, same era.
A. Rakhsha, G. Radanovic, R. Devidze, X. Zhu, and A. Singla, “Policy teaching via environment poisoning: Training-time adversarial attacks against reinforcement learning,” in ICML
2020
Later among the works it cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in ICML
2020
Later among the works it cites.
J. Perdomo, T. Zrnic, C. Mendler-Dünner, and M. Hardt, “Performative prediction,” in ICML
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
X. Zhang, Y. Ma, A. Singla, and X. Zhu, “Adaptive reward-poisoning attacks against reinforcement learning,” in ICML
2020
Later among the works it cites.
X. Zhang, X. Zhu, and L. Lessard, “Online data poisoning attacks,” in L4DC
2020
Later among the works it cites.
P. Wang, J. Wang, P. Paranamana, and P. Shafto, “A mathematical theory of cooperative communication,” in NeurIPS
2020
Later among the works it cites.
J. Wang, P. Wang, and P. Shafto, “Sequential cooperative bayesian inference,” in ICML
2020
Later among the works it cites.
K. He, H. Fan, Y. Wu, S. Xie, and R. Girshick, “Momentum contrast for unsupervised visual representation learning,” in CVPR
2020
Later among the works it cites.
B. Mirzasoleiman, J. Bilmes, and J. Leskovec, “Coresets for data-efficient training of machine learning models,” in ICML
2020
Later among the works it cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM
2020
Later among the works it cites.
B. Chen, W. Liu, Z. Yu, J. Kautz, A. Shrivastava, A. Garg, and A. Anandkumar, “Angular visual hardness,” in ICML
2020
Later among the works it cites.
W. Liu, Z. Liu, H. Wang, L. Paull, B. Schölkopf, and A. Weller, “Iterative teaching by label synthesis,” in NeurIPS
2021
Later among the works it cites.
B. Zhao, K. R. Mopuri, and H. Bilen, “Dataset condensation with gradient matching.,” in ICLR
2021
Later among the works it cites.
C. Wang, A. Singla, and Y. Chen, “Teaching an active learner with contrastive examples,” in NeurIPS
2021
Later among the works it cites.
L. Yuan, D. Zhou, J. Shen, J. Gao, J. L. Chen, Q. Gu, Y. N. Wu, and S.-C. Zhu, “Iterative teacher-aware learning,” in NeurIPS
2021
Later among the works it cites.
Z. Xu, B. Chen, C. Li, W. Liu, L. Song, Y. Lin, and A. Shrivastava, “Locality sensitive teaching,” in NeurIPS
2021
Later among the works it cites.
P. Shafto, J. Wang, and P. Wang, “Cooperative communication as belief transport,” Trends in Cognitive Sciences
2021
Later among the works it cites.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning (still) requires rethinking generalization,” Communications of the ACM
2021
Later among the works it cites.
W. Liu, R. Lin, Z. Liu, J. M. Rehg, L. Paull, L. Xiong, L. Song, and A. Weller, “Orthogonal over-parameterized training,” in CVPR
2021
Later among the works it cites.
G. Cazenavette, T. Wang, A. Torralba, A. A. Efros, and J.-Y. Zhu, “Dataset distillation by matching training trajectories,” in CVPR Workshops
2022
Closest in time.