Velingker, Ameya

12 publications

NeurIPSW 2024 A Theory for Compressibility of Graph Transformers for Transductive Learning Hamed Shirzad, Honghao Lin, Ameya Velingker, Balaji Venkatachalam, David Woodruff, Danica J. Sutherland

NeurIPS 2024 Even Sparser Graph Transformers Hamed Shirzad, Honghao Lin, Balaji Venkatachalam, Ameya Velingker, David P. Woodruff, Danica J. Sutherland

ICLR 2024 Locality-Aware Graph Rewiring in GNNs Federico Barbero, Ameya Velingker, Amin Saberi, Michael M. Bronstein, Francesco Di Giovanni

ICML 2024 Weisfeiler-Leman at the Margin: When More Expressivity Matters Billy Joe Franks, Christopher Morris, Ameya Velingker, Floris Geerts

NeurIPS 2023 Affinity-Aware Graph Networks Ameya Velingker, Ali Sinop, Ira Ktena, Petar Veličković, Sreenivas Gollapudi

ICMLW 2023 Efficient Location Sampling Algorithms for Road Networks Sara Ahmadian, Kostas Kollias, Ameya Velingker, Sreenivas Gollapudi, Vivek Kumar, Santhoshini Velusamy

ICML 2023 Exphormer: Sparse Transformers for Graphs Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J. Sutherland, Ali Kemal Sinop

ICML 2023 Fast $(1+\varepsilon)$-Approximation Algorithms for Binary Matrix Factorization Ameya Velingker, Maximilian Vötsch, David Woodruff, Samson Zhou

NeurIPSW 2023 Low-Width Approximations and Sparsification for Scaling Graph Transformers Hamed Shirzad, Balaji Venkatachalam, Ameya Velingker, Danica Sutherland, David Woodruff

COLT 2022 Private Robust Estimation by Stabilizing Convex Relaxations Pravesh Kothari, Pasin Manurangsi, Ameya Velingker

AISTATS 2020 Scaling up Kernel Ridge Regression via Locality Sensitive Hashing Amir Zandieh, Navid Nouri, Ameya Velingker, Michael Kapralov, Ilya Razenshteyn

ICML 2017 Random Fourier Features for Kernel Ridge Regression: Approximation Bounds and Statistical Guarantees Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh