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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IMPROVED ALGORITHMS FOR KERNEL MATRIX-VECTOR MULTIPLICATION UNDER SPARSITY ASSUMPTIONS
Indyk, P., Kapralov, M., Sheth, K. & Wagner, T., 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 92647-92662 16 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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LEARNING FROM END USER DATA WITH SHUFFLED DIFFERENTIAL PRIVACY OVER KERNEL DENSITIES
Wagner, T., 2025, 13th International Conference on Learning Representations, ICLR 2025. International Conference on Learning Representations, ICLR, p. 71532-71563 32 p. (13th International Conference on Learning Representations, ICLR 2025).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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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
5 Scopus citations -
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
2 Scopus citations -
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
13 Scopus citations