Strong Coresets for Subspace Approximation and k-Median in Nearly Linear Time. - Zhili Feng; Praneeth Kacham; David P. Woodruff (CoRR, 2019)

Optimal Deterministic Coresets for Ridge Regression. - Praneeth Kacham; David P. Woodruff (AISTATS, 2020)

Robust k-means++. - Amit Deshpande 001; Praneeth Kacham; Rameshwar Pratap (UAI, 2020)

Reduced-Rank Regression with Operator Norm Error. - Praneeth Kacham; David P. Woodruff (COLT, 2021)

Dimensionality Reduction for the Sum-of-Distances Metric. - Zhili Feng; Praneeth Kacham; David P. Woodruff (ICML, 2021)

Sketching Algorithms and Lower Bounds for Ridge Regression. - Praneeth Kacham; David P. Woodruff (ICML, 2022)

Near-Optimal Algorithms for Linear Algebra in the Current Matrix Multiplication Time. - Nadiia Chepurko; Kenneth L. Clarkson; Praneeth Kacham; David P. Woodruff (SODA, 2022)

Sub-quadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (CoRR, 2022)

Pseudorandom Hashing for Space-bounded Computation with Applications in Streaming. - Praneeth Kacham; Rasmus Pagh; Mikkel Thorup; David P. Woodruff (FOCS, 2023)

Subquadratic Algorithms for Kernel Matrices via Kernel Density Estimation. - Ainesh Bakshi; Piotr Indyk; Praneeth Kacham; Sandeep Silwal; Samson Zhou (ICLR, 2023)

Lower Bounds on Adaptive Sensing for Matrix Recovery. - Praneeth Kacham; David P. Woodruff (NeurIPS, 2023)

PolySketchFormer: Fast Transformers via Sketches for Polynomial Kernels. - Praneeth Kacham; Vahab Mirrokni; Peilin Zhong (CoRR, 2023)

Faster Algorithms for Schatten-p Low Rank Approximation. - Praneeth Kacham; David P. Woodruff (APPROX/RANDOM, 2024)

High-Dimensional Geometric Streaming for Nearly Low Rank Data. - Hossein Esfandiari; Praneeth Kacham; Vahab Mirrokni; David P. Woodruff; Peilin Zhong (ICML, 2024)

PolySketchFormer: Fast Transformers via Sketching Polynomial Kernels. - Praneeth Kacham; Vahab Mirrokni; Peilin Zhong (ICML, 2024)

Approximating the Top Eigenvector in Random Order Streams. - Praneeth Kacham; David P. Woodruff (NeurIPS, 2024)

Optimal Communication Bounds for Classic Functions in the Coordinator Model and Beyond. - Hossein Esfandiari; Praneeth Kacham; Vahab Mirrokni; David P. Woodruff; Peilin Zhong (STOC, 2024)

Optimal Communication for Classic Functions in the Coordinator Model and Beyond. - Hossein Esfandiari; Praneeth Kacham; Vahab Mirrokni; David P. Woodruff; Peilin Zhong (CoRR, 2024)

On Efficient Sketching Algorithms - Praneeth Kacham (2024)

LevAttention: Time, Space and Streaming Efficient Algorithm for Heavy Attentions. - Ravindran Kannan; Chiranjib Bhattacharyya; Praneeth Kacham; David P. Woodruff (ICLR, 2025)

PolarQuant: Quantizing KV Caches with Polar Transformation. - Insu Han; Praneeth Kacham; Amin Karbasi; Vahab Mirrokni; Amir Zandieh (CoRR, 2025)

ATLAS: Learning to Optimally Memorize the Context at Test Time. - Ali Behrouz; Zeman Li; Praneeth Kacham; Majid Daliri; Yuan Deng; Peilin Zhong; Meisam Razaviyayn; Vahab Mirrokni (CoRR, 2025)