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Praneeth Kacham
Praneeth Kacham
Graduate Student, Carnegie Mellon University
Verified email at cs.cmu.edu - Homepage
Title
Cited by
Cited by
Year
Robust -means++
A Deshpande, P Kacham, R Pratap
Conference on Uncertainty in Artificial Intelligence, 799-808, 2020
242020
Dimensionality reduction for the sum-of-distances metric
Z Feng, P Kacham, D Woodruff
International conference on machine learning, 3220-3229, 2021
19*2021
Polysketchformer: Fast transformers via sketches for polynomial kernels
P Kacham, V Mirrokni, P Zhong
arXiv preprint arXiv:2310.01655, 2023
162023
Near-optimal algorithms for linear algebra in the current matrix multiplication time
N Chepurko, KL Clarkson, P Kacham, DP Woodruff
Proceedings of the 2022 Annual ACM-SIAM Symposium on Discrete Algorithms …, 2022
92022
Optimal deterministic coresets for ridge regression
P Kacham, D Woodruff
International Conference on Artificial Intelligence and Statistics, 4141-4150, 2020
92020
Sub-quadratic algorithms for kernel matrices via kernel density estimation
A Bakshi, P Indyk, P Kacham, S Silwal, S Zhou
arXiv preprint arXiv:2212.00642, 2022
52022
Pseudorandom hashing for space-bounded computation with applications in streaming
P Kacham, R Pagh, M Thorup, DP Woodruff
2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS …, 2023
32023
Sketching algorithms and lower bounds for ridge regression
P Kacham, D Woodruff
International Conference on Machine Learning, 10539-10556, 2022
22022
Reduced-rank regression with operator norm error
P Kacham, D Woodruff
Conference on Learning Theory, 2679-2716, 2021
22021
Optimal Communication for Classic Functions in the Coordinator Model and Beyond
H Esfandiari, P Kacham, V Mirrokni, DP Woodruff, P Zhong
arXiv preprint arXiv:2403.20307, 2024
2024
Lower Bounds on Adaptive Sensing for Matrix Recovery
P Kacham, D Woodruff
Advances in Neural Information Processing Systems 36, 2024
2024
High-Dimensional Geometric Streaming for Nearly Low Rank Data
H Esfandiari, P Kacham, V Mirrokni, D Woodruff, P Zhong
2023
Differentially Private Vertical Federated Learning Primitives
V Cohen-Addad, P Kacham, V Mirrokni, P Zhong
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Articles 1–13