Personal profile
Research interests
Algorithms for massive high-dimensional datasets and scalable machine learning
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Dive into the research topics where Tal Wagner is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
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Fast Private Kernel Density Estimation via Locality Sensitive Quantization
Wagner, T., Naamad, Y. & Mishra, N., 2023, In: Proceedings of Machine Learning Research. 202, p. 35339-35367 29 p.Research output: Contribution to journal › Conference article › peer-review
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Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees
Schiefer, N., Chen, J. Y., Indyk, P., Narayanan, S., Silwal, S. & Wagner, T., 2023, SIAM Conference on Applied and Computational Discrete Algorithms, ACDA 2023. Berry, J., Shmoys, D., Cowen, L. & Naumann, U. (eds.). Society for Industrial and Applied Mathematics (SIAM), p. 87-97 11 p. (SIAM Conference on Applied and Computational Discrete Algorithms, ACDA 2023).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Learned Interpolation for Better Streaming Quantile Approximation with Worst-Case Guarantees
Schiefer, N., Chen, J. Y., Indyk, P., Narayanan, S., Silwal, S. & Wagner, T., 2023, SIAM Conference on Applied and Computational Discrete Algorithms (ACDA23). p. 87-97 11 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review
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Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Aamand, A., Chen, J. Y., Indyk, P., Narayanan, S., Rubinfeld, R., Schiefer, N., Silwal, S. & Wagner, T., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Generalization Bounds for Data-Driven Numerical Linear Algebra
Bartlett, P., Indyk, P. & Wagner, T., 2022, In: Proceedings of Machine Learning Research. 178, p. 2013-2024 12 p.Research output: Contribution to journal › Conference article › peer-review