Vassilvitskii, Sergei

33 publications

NeurIPS 2025 Escaping Collapse: The Strength of Weak Data for Large Language Model Training Kareem Amin, Sara Babakniya, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii

ICLRW 2025 Escaping Collapse: The Strength of Weak Data for Large Language Model Training Kareem Amin, Sara Babakniya, Alex Bie, Weiwei Kong, Umar Syed, Sergei Vassilvitskii

ICLR 2025 Scaling Laws for Downstream Task Performance in Machine Translation Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, Sanmi Koyejo

NeurIPS 2024 Binary Search with Distributional Predictions Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Aidin Niaparast, Sergei Vassilvitskii

ICLRW 2024 Scaling Laws for Downstream Task Performance of Large Language Models Berivan Isik, Natalia Ponomareva, Hussein Hazimeh, Dimitris Paparas, Sergei Vassilvitskii, Sanmi Koyejo

NeurIPS 2024 Warm-Starting Push-Relabel Sami Davies, Sergei Vassilvitskii, Yuyan Wang

NeurIPSW 2023 A New Framework for Measuring Re-Identification Risk Cj Carey, Travis Dick, Alessandro Epasto, Adel Javanmard, Josh Karlin, Shankar Kumar, Andres Munoz Medina, Vahab Mirrokni, Gabriel Nunes, Sergei Vassilvitskii, Peilin Zhong

ICLR 2023 Easy Differentially Private Linear Regression Kareem Amin, Matthew Joseph, Mónica Ribero, Sergei Vassilvitskii

JAIR 2023 How to DP-Fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H. Brendan McMahan, Sergei Vassilvitskii, Steve Chien, Abhradeep Guha Thakurta

ICML 2023 Label Differential Privacy and Private Training Data Release Robert Istvan Busa-Fekete, Andres Munoz Medina, Umar Syed, Sergei Vassilvitskii

ICML 2023 Learning-Augmented Private Algorithms for Multiple Quantile Release Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii

ICMLW 2023 Learning-Augmented Private Algorithms for Multiple Quantile Release Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii

ICML 2023 Predictive Flows for Faster Ford-Fulkerson Sami Davies, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang

ICML 2023 Speeding up Bellman Ford via Minimum Violation Permutations Silvio Lattanzi, Ola Svensson, Sergei Vassilvitskii

AISTATS 2022 Label Differential Privacy via Clustering Hossein Esfandiari, Vahab Mirrokni, Umar Syed, Sergei Vassilvitskii

NeurIPS 2022 Algorithms with Prediction Portfolios Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

NeurIPS 2022 Learning Predictions for Algorithms with Predictions Misha Khodak, Maria-Florina F Balcan, Ameet Talwalkar, Sergei Vassilvitskii

NeurIPS 2021 Faster Matchings via Learned Duals Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, Sergei Vassilvitskii

NeurIPSW 2021 On the Pitfalls of Label Differential Privacy Andres Munoz Medina, Robert Istvan Busa-Fekete, Umar Syed, Sergei Vassilvitskii

NeurIPSW 2021 Population Level Privacy Leakage in Binary Classification Wtih Label Noise Robert Istvan Busa-Fekete, Andres Munoz Medina, Umar Syed, Sergei Vassilvitskii

NeurIPS 2021 Robust Online Correlation Clustering Silvio Lattanzi, Benjamin Moseley, Sergei Vassilvitskii, Yuyan Wang, Rudy Zhou

NeurIPS 2020 Fair Hierarchical Clustering Sara Ahmadian, Alessandro Epasto, Marina Knittel, Ravi Kumar, Mohammad Mahdian, Benjamin Moseley, Philip Pham, Sergei Vassilvitskii, Yuyan Wang

NeurIPS 2020 Sliding Window Algorithms for K-Clustering Problems Michele Borassi, Alessandro Epasto, Silvio Lattanzi, Sergei Vassilvitskii, Morteza Zadimoghaddam

NeurIPS 2019 Differentially Private Covariance Estimation Kareem Amin, Travis Dick, Alex Kulesza, Andres Munoz, Sergei Vassilvitskii

NeurIPS 2018 Maximizing Induced Cardinality Under a Determinantal Point Process Jennifer A Gillenwater, Alex Kulesza, Sergei Vassilvitskii, Zelda E. Mariet

ICML 2017 Consistent K-Clustering Silvio Lattanzi, Sergei Vassilvitskii

NeurIPS 2017 Fair Clustering Through Fairlets Flavio Chierichetti, Ravi Kumar, Silvio Lattanzi, Sergei Vassilvitskii

NeurIPS 2017 Revenue Optimization with Approximate Bid Predictions Andres Munoz, Sergei Vassilvitskii

NeurIPS 2017 Statistical Cost Sharing Eric Balkanski, Umar Syed, Sergei Vassilvitskii

NeurIPS 2016 On Mixtures of Markov Chains Rishi Gupta, Ravi Kumar, Sergei Vassilvitskii

ICML 2016 Pricing a Low-Regret Seller Hoda Heidari, Mohammad Mahdian, Umar Syed, Sergei Vassilvitskii, Sadra Yazdanbod

AISTATS 2016 Sketching, Embedding and Dimensionality Reduction in Information Theoretic Spaces Amirali Abdullah, Ravi Kumar, Andrew McGregor, Sergei Vassilvitskii, Suresh Venkatasubramanian

ICML 2013 Near-Optimal Bounds for Cross-Validation via Loss Stability Ravi Kumar, Daniel Lokshtanov, Sergei Vassilvitskii, Andrea Vattani