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Collaboration networkThis graph maps the connections between all the collaborators of {}'s publications listed on this page.
Each link represents a collaboration on the same publication. The thickness of the link represents the number of collaborations.
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Word cloudA word cloud is a visual representation of the most frequently used words in a text or a set of texts. The words appear in different sizes, with the size of each word being proportional to its frequency of occurrence in the text. The more frequently a word is used, the larger it appears in the word cloud. This technique allows for a quick visualization of the most important themes and concepts in a text.
In the context of this page, the word cloud was generated from the publications of the author {}. The words in this cloud come from the titles, abstracts, and keywords of the author's articles and research papers. By analyzing this word cloud, you can get an overview of the most recurring and significant topics and research areas in the author's work.
The word cloud is a useful tool for identifying trends and main themes in a corpus of texts, thus facilitating the understanding and analysis of content in a visual and intuitive way.
Adler, A., & Tang, J. (2026, July). Efficient mismatch-tolerant coding for model-driven compression [Poster]. 43rd International Conference on Machine Learning (ICML 2026), Seoul, South Korea. External link
Adler, A., & Tang, J. (2026, April). Synchronizing probabilities in model-driven lossless compression [Poster]. Fourteenth International Conference on Learning Representations (ICLR 2026), Rio de Janeiro, Brazil (18 pages). External link
Adler, A., Tang, J., & Polyanskiy, Y. (2022). Efficient representation of large-alphabet probability distributions. IEEE Journal on Selected Areas in Information Theory, 3(4), 651-663. External link
Adler, A., Tang, J., & Polyanskiy, Y. (2022, June). Efficient Representation of Large-Alphabet Probability Distributions via Arcsinh-Compander [Paper]. IEEE International Symposium on Information Theory (ISIT 2022), Espoo, Finland. External link
Adler, A., Tang, J., & Polyanskiy, Y. (2021, July). Quantization of random distributions under KL divergence [Paper]. 2021 IEEE International Symposium on Information Theory (ISIT 2021), Melbourne, Australia. External link
Tang, J., Adler, A., Ajorlou, A., & Jadbabaie, A. (2025). Stochastic opinion dynamics under social pressure in arbitrary networks. IEEE Transactions on Automatic Control, 70(10), 6937-6944. External link
Tang, J., Adler, A., Ajorlou, A., & Jadbabaie, A. (2024). Estimating true beliefs in opinion dynamics with social pressure. IEEE Transactions on Automatic Control, 70(5), 3072-3087. External link
Tang, J., Adler, A., Ajorlou, A., & Jadbabaie, A. (2024, July). Estimating true beliefs from declared opinions [Paper]. 2024 American Control Conference (ACC 2024), Toronto, ON, Canada. External link
