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One of the most studied problems in machine learning is finding reasonable constraints that guarantee the generalization of a learning algorithm.
InfoBot: Transfer and Exploration via the Information Bottleneck
Goyal, A., Islam, R., Strouse, D., Ahmed, Z., Larochelle, H., Botvinick, M., Bengio, Y., Levine, S., 2019 · 1901
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Bounds for the distribution function of a sum of independent, identically distributed random variables
Hoeffding, W., Shrikhande, S.S., 1955 · 1955
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On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
Vapnik, V.N., Chervonenkis, A.Y., 1971 · 1971
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Document clustering using word clusters via the information bottleneck method, in: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM. pp. 208–215
Slonim, N., Tishby, N., 2000 · 2000
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The information bottleneck method
Tishby, N., Pereira, F.C., Bialek, W., 2000 · 2000
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Learning and generalization with the information bottleneck, in: International Conference on Algorithmic Learning Theory, Springer. pp. 92–107
Shamir, O., Sabato, S., Tishby, N., 2008 · 2008
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Learning and generalization with the information bottleneck
Shamir, O., Sabato, S., Tishby, N., 2010 · 2010
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Learning from Data: A Short Course
Abu-Mostafa, Y.S., Magdon-Ismail, M., Lin, H.T., 2012 · 2012
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Elements of Information Theory
Cover, T.M., Thomas, J.A., 2012 · 2012
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Understanding Machine Learning: From Foundations to Algorithms
Shalev-Shwartz, S., Ben-David, S., 2014 · 2014
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How much does your data exploration overfit? Controlling bias via information usage
Russo, D., Zou, J., 2015 · 2015
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Deep Learning and the Information Bottleneck Principle
Tishby, N., Zaslavsky, N., 2015 · 2015
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Flow of information in feed-forward deep neural networks
Khadivi, P., Tandon, R., Ramakrishnan, N., 2016 · 2016
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Information Dropout: Learning Optimal Representations Through Noisy Computation
Achille, A., Soatto, S., 2018 · 2017
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Deep Variational Information Bottleneck
Alemi, A.A., Fischer, I., Dillon, J.V., Murphy, K., 2017 · 2017
Cited alongside, same era.
Nonlinear Information Bottleneck
Kolchinsky, A., Tracey, B.D., Wolpert, D.H., 2017 · 2017
Cited alongside, same era.
On maximal tail probability of sums of nonnegative, independent and identically distributed random variables
Luczak, T., Mieczkowska, K., Šileikis, M., 2017 · 2017
Cited alongside, same era.
Layer-wise learning of stochastic neural networks with information bottleneck
Nguyen, T.T., Choi, J., 2017 · 2017
Cited alongside, same era.
Opening the Black Box of Deep Neural Networks via Information
Shwartz-Ziv, R., Tishby, N., 2017 · 2017
Cited alongside, same era.
The Role of the Information Bottleneck in Representation Learning, in: 2018 IEEE International Symposium on Information Theory (ISIT), IEEE, Vail, CO. pp. 1580–1584
Vera, M., Piantanida, P., Vega, L.R., 2018 · 2018
Later among the works it cites.
Time Series Prediction Via Recurrent Neural Networks with the Information Bottleneck Principle, in: 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5
Xu, D., Fekri, F., 2018 · 2018
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Understanding Convolutional Neural Network Training with Information Theory
Yu, S., Jenssen, R., Principe, J.C., 2018 · 2018
Later among the works it cites.
Learning representations for neural network-based classification using the information bottleneck principle
Amjad, R.A., Geiger, B.C., 2019 · 2019
Closest in time.
Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification
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The Deterministic Information Bottleneck
Strouse, D., Schwab, D.J., 2017 · 2017
Cited alongside, same era.
Understanding deep learning requires rethinking generalization
Zhang, C., Bengio, S., Hardt, M., Recht, B., Vinyals, O., 2017 · 2017
Cited alongside, same era.
How (Not) To Train Your Neural Network Using the Information Bottleneck Principle
Amjad, R.A., Geiger, B.C., 2018 · 2018
Cited alongside, same era.
Learners that Use Little Information, in: Algorithmic Learning Theory, pp. 25–55
Bassily, R., Moran, S., Nachum, I., Shafer, J., Yehudayoff, A., 2018 · 2018
Cited alongside, same era.
Evaluating Capability of Deep Neural Networks for Image Classification via Information Plane, in: Proceedings of the European Conference on Computer Vision (ECCV), pp. 168–182
Cheng, H., Lian, D., Gao, S., Geng, Y., 2018 · 2018
Cited alongside, same era.
Information Bottleneck Methods for Distributed Learning, in: 2018 56th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2018, pp. 24–31
Farajiparvar, P., Beirami, A., Nokleby, M., 2019 · 2018
Cited alongside, same era.
Implementation and Verification of the Information Bottleneck Interpretation of Deep Neural Networks
Liu, F., 2018 · 2018
Cited alongside, same era.
Cheng, H., Lian, D., Gao, S., Geng, Y., 2019 · 2019
Closest in time.
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Ghimire, S., Gyawali, P., Dhamala, J., Sapp, J., Horacek, M., Wang, L., 2019 · 2019
Closest in time.
Estimating Information Flow in Deep Neural Networks, in: International Conference on Machine Learning, pp. 2299–2308
Goldfeld, Z., Van Den Berg, E., Greenewald, K., Melnyk, I., Nguyen, N., Kingsbury, B., Polyanskiy, Y., 2019 · 2019
Closest in time.
Caveats for information bottleneck in deterministic scenarios, in: International Conference on Learning Representations
Kolchinsky, A., Tracey, B.D., Kuyk, S.V., 2019 · 2019
Closest in time.
The Information Bottleneck: Connections to Other Problems, Learning and Exploration of the IB Curve
Rodriguez Galvez, B., 2019 · 2019
Closest in time.
The Information Bottleneck and Geometric Clustering
Strouse, D., Schwab, D.J., 2019 · 2019
Closest in time.
Stanford Seminar - Information Theory of Deep Learning
Tishby, N., 2018 · 2019
Closest in time.
Deep Multi-view Information Bottleneck, in: Proceedings of the 2019 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics. Proceedings, pp. 37–45
Wang, Q., Boudreau, C., Luo, Q., Tan, P., Zhou, J., 2019 · 2019
Closest in time.
Learnability for the Information Bottleneck, in: International Conference on Learning Representations
Wu, T., Fischer, I., Chuang, I.L., Tegmark, M., 2019 · 2019
Closest in time.