Information Theory

Improving generalization by controlling label-noise information in neural network weights ICML 2020 Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback NeurIPS 2020 Information theoretic limits of learning a sparse rule NeurIPS 2020 Information-Theoretic Probing for Linguistic Structure ACL 2020 Information-Theoretic Probing with Minimum Description Length EMNLP 2020 Inverse Learning of Symmetries NeurIPS 2020 Learning Optimal Representations with the Decodable Information Bottleneck NeurIPS 2020 Learning Structured Latent Factors from Dependent Data:A Generative Model Framework from Information-Theoretic Perspective ICML 2020 Limits on Testing Structural Changes in Ising Models NeurIPS 2020 Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications IJCAI 2020 Modeling Dialogues with Hashcode Representations: A Nonparametric Approach AAAI 2020 Mutual Information Gradient Estimation for Representation Learning ICLR 2020 Open Problem: Information Complexity of VC Learning COLT 2020 Predicting Declension Class from Form and Meaning ACL 2020 Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions NeurIPS 2020 Reasoning About Generalization via Conditional Mutual Information COLT 2020 Revisiting Probability Distribution Assumptions for Information Theoretic Feature Selection AAAI 2020 Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms NeurIPS 2020 Surprisal Predicts Code-Switching in Chinese-English Bilingual Text EMNLP 2020 The All-or-Nothing Phenomenon in Sparse Tensor PCA NeurIPS 2020 An Information-Theoretic Approach to Minimax Regret in Partial Monitoring COLT 2019 Average-Case Information Complexity of Learning ALT 2019 CCMI : Classifier based Conditional Mutual Information Estimation UAI 2019 Exact Rate-Distortion in Autoencoders via Echo Noise NeurIPS 2019 Experimenting with Power Divergences for Language Modeling EMNLP 2019