Publications

Federico Barbero, Ameya Velingker, Amin Saberi, Michael Bronstein, Francesco Di Giovanni (2023). Locality-Aware Graph-Rewiring in GNNs. arXiv:2310.01668.

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

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

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Ameya Velingker, Maximilian Vötsch, David P. Woodruff, Samson Zhou (2023). Fast (1+ε)-Approximation Algorithms for Binary Matrix Factorization. ICML 2023.

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

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Chi-Ning Chou, Alexander Golovnev, Madhu Sudan, Ameya Velingker, Santhoshini Velusamy (2022). Linear space streaming lower bounds for approximating CSPs. STOC 2022.

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Badih Ghazi, Noah Golowich, Ravi Kumar, Rasmus Pagh, Ameya Velingker (2021). On the Power of Multiple Anonymous Messages: Frequency Estimation and Selection in the Shuffle Model of Differential Privacy. EUROCRYPT 2021. FORC 2020.

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

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Badih Ghazi, Noah Golowich, Ravi Kumar, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker (2020). Pure Differentially Private Summation from Anonymous Messages. ITC 2020.

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Badih Ghazi, Pasin Manurangsi, Rasmus Pagh, Ameya Velingker (2020). Private Aggregation from Fewer Anonymous Messages. EUROCRYPT 2020.

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Thomas D. Ahle, Michael Kapralov, Jakob Bæk Tejs Knudsen, Rasmus Pagh, Ameya Velingker, David P. Woodruff, Amir Zandieh (2020). Oblivious Sketching of High-Degree Polynomial Kernels. SODA 2020.

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Haim Avron, Michael Kapralov, Cameron Musco, Christopher Musco, Ameya Velingker, Amir Zandieh (2019). A Universal Sampling Method for Reconstructing Signals with Simple Fourier Transforms. STOC 2019.

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Michael Kapralov, Ameya Velingker, Amir Zandieh (2019). Dimension-independent Sparse Fourier Transform. SODA 2019.

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Venkatesan Guruswami, Ameya Velingker, Santhoshini Velusamy (2017). Streaming Complexity of Approximating Max 2CSP and Max Acyclic Subgraph. APPROX 2017.

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

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Michael Kapralov, Sanjeev Khanna, Madhu Sudan, Ameya Velingker (2017). (1 + Ω(1))-Αpproximation to MAX-CUT Requires Linear Space. SODA 2017.

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Bernhard Haeupler, Ameya Velingker (2017). Bridging the Capacity Gap Between Interactive and One-Way Communication. SODA 2017.

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Karthekeyan Chandrasekaran, Ameya Velingker (2017). Constructing Ramanujan Graphs Using Shift Lifts. Linear Algebra and its Applications 529 (2017).

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Mitali Bafna, Satyanarayana V. Lokam, Sébastien Tavenas, Ameya Velingker (2016). On the Sensitivity Conjecture for Read-k Formulas. MFCS 2016.

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Bernhard Haeupler, Pritish Kamath, Ameya Velingker (2015). Communication with Partial Noiseless Feedback. RANDOM 2015.

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Michael B. Cohen, Brittany Terese Fasy, Gary L. Miller, Amir Nayyeri, Donald R. Sheehy, Ameya Velingker (2015). Approximating Nearest Neighbor Distances. WADS 2015.

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Venkatesan Guruswami, Ameya Velingker (2015). An Entropy Sumset Inequality and Polynomially Fast Convergence to Shannon Capacity Over All Alphabets. CCC 2015.

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Venkatesan Guruswami, Madhu Sudan, Ameya Velingker, Carol Wang (2015). Limitations on Testable Affine-Invariant Codes in the High-Rate Regime. SODA 2015.

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Gary L. Miller, Donald R. Sheehy, Ameya Velingker (2013). A Fast Algorithm for Well-Spaced Points and Approximate Delaunay Graphs. SoCG 2013.

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Mahdi Cheraghchi, Venkatesan Guruswami, Ameya Velingker (2013). Restricted Isometry of Fourier Matrices and List Decodability of Random Linear Codes. SODA 2013. SIAM Journal on Computing 42(5).

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