On Sketching the q to p Norms. - Aditya Krishnan 001; Sidhanth Mohanty; David P. Woodruff (APPROX-RANDOM, 2018)

Schatten Norms in Matrix Streams: Hello Sparsity, Goodbye Dimension. - Vladimir Braverman; Robert Krauthgamer; Aditya Krishnan 001; Roi Sinoff (ICML, 2020)

Competitively Pricing Parking in a Tree. - Max Bender; Jacob Gilbert; Aditya Krishnan 001; Kirk Pruhs (WINE, 2020)

Lifelong Learning with Sketched Structural Regularization. - Haoran Li 006; Aditya Krishnan 001; Jingfeng Wu; Soheil Kolouri; Praveen K. Pilly; Vladimir Braverman (ACML, 2021)

Near-Optimal Entrywise Sampling of Numerically Sparse Matrices. - Vladimir Braverman; Robert Krauthgamer; Aditya Krishnan 001; Shay Sapir (COLT, 2021)

Linear and Sublinear Time Spectral Density Estimation. - Vladimir Braverman; Aditya Krishnan 001; Christopher Musco (CoRR, 2021)

Projective Clustering Product Quantization. - Aditya Krishnan 001; Edo Liberty (CoRR, 2021)

Sublinear time spectral density estimation. - Vladimir Braverman; Aditya Krishnan 001; Christopher Musco (STOC, 2022)

Lower Bounds for Pseudo-Deterministic Counting in a Stream. - Vladimir Braverman; Robert Krauthgamer; Aditya Krishnan 001; Shay Sapir (ICALP, 2023)

Optimistic Query Routing in Clustering-based Approximate Maximum Inner Product Search. - Sebastian Bruch 001; Aditya Krishnan 001; Franco Maria Nardini (CoRR, 2024)