Singer, Yaron

2019
Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2019. “An Exponential Speedup in Parallel Running Time for Submodular Maximization without Loss in Approximation.” In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA) 2019 . Download
2018
Eric Balkanski and Yaron Singer. 2018. “Approximation Guarantees for Adaptive Sampling.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018).
Eric Balkanski and Yaron Singer. 2018. “The Adaptive Complexity of Maximizing a Submodular Function.” In Proceedings of the ACM Symposium on the Theory of Computation (STOC) 2018 . Download
Dimitris Kalimeris, Karthik Subbian, Udi Weinsberg, and Yaron Singer. 2018. “Learning Diffusion using Hyperparameters.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018).
Nir Rosenfeld, Eric Balkanski, Amir Globerson, and Yaron Singer. 2018. “Learning to Optimize Combinatorial Functions.” In Proceedings of the 35th International Conference on Machine Learning (ICML 2018). Download
Eric Balkanski, Adam Breuer, and Yaron Singer. 2018. “Non-monotone Submodular Maximization in Exponentially Fewer Iterations.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2018.
Avinatan Hassidim and Yaron Singer. 2018. “Optimization for Approximate Submodularity.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2018.
2017
Eric Balkanski, Nicole Immorlica, and Yaron Singer. 2017. “The Importance of Communities for Learning to Influence.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2017. Download
Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2017. “The Limitations of Optimization from Samples.” In ACM Symposium on the Theory of Computing (STOC 2017). Download
Eric Balkanski and Yaron Singer. 2017. “Minimizing a Submodular Function from Samples.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2017. Download
Hassidim Avinatan and Yaron Singer. 2017. “Robust Guarantees of Stochastic Greedy Algorithms.” In International Conference of Machine Learning (ICML) . Download
Robert Chen, Brendan Lucier, Vasilis Syrgkanis, and Yaron Singer. 2017. “Robust Optimization for Non-Convex Objectives.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2017. Download
Eric Balkanski and Yaron Singer. 2017. “The Sample Complexity of Optimizing a Convex Function.” In Proc. of the Conference on Learning Theory (COLT-17). Download
Avinatan Hassidim and Yaron Singer. 2017. “Submodular Optimization under Noise.” In Proc. of the Conference on Learning Theory (COLT-17). Download
2016
Eric Balkanski, Andreas Krause, Baharan Mirzasoleiman, and Yaron Singer. 2016. “Learning Sparse Combinatorial Representations viaTwo-stage Submodular Maximization.” In International Conference of Machine Learning (ICML) , Pp. 2207-2216. Download
Ashwinkumar Badanidiyuru, Christos Papadimitriou, Aviad Rubinstein, Lior Seeman, and Yaron Singer. 2016. “Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions.” In ACM-SIAM Symposium on Discrete Algorithms (SODA), Pp. 414-429. Download
Thibaut Horel and Yaron Singer. 2016. “Maximizing Approximately Submodular Functions.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2016. Download
Eric Balkanski, Aviad Rubinstein, and Yaron Singer. 2016. “The Power of Optimization from Samples.” In Proceedings of the Conference on Neural Information Processing Systems (NIPS) 2016. Download
2015
Aviad Rubinstein, Lior Seeman, and Yaron Singer. 2015. “Approximability of Adaptive Seeding under Knapsack Constraints.” In The ACM Conference on Economics and Computation (EC) 2015, Pp. 797-814. Download
Brendan Lucier, Joel Oren, and Yaron Singer. 2015. “Influence at Scale: Distributed Computation of Contagion in Networks.” In The ACM Conference on Knowledge Discovery and Data Mining (KDD), Pp. 735-744. Download

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