פרופ' מורן פלדמן

• Submodular Maximization over a Matroid k-Intersection: Multiplicative Improvement over Greedy, Feldman, M. & Ward, J., 1 Jul 2026, 53rd International Colloquium on Automata, Languages, and Programming, ICALP 2026. Bhattacharya, S., Nanongkai, D., Benedikt, M. & Puppis, G. (eds.). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 89. (Leibniz International Proceedings in Informatics, LIPIcs; vol. 374).
• Streaming Submodular Maximization Under Matroid Constraints, Feldman, M., Liu, P., Norouzi-Fard, A., Svensson, O. & Zenklusen, R., Feb 2026, In: Mathematics of Operations Research. 51, 1, p. 299-332 34 p.
• Nearly Tight Sample Complexity for Matroid Online Contention Resolution, Feldman, M., Svensson, O. & Zenklusen, R., 2026, Proceedings of the 2026 Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2026. Larsen, K. G. & Saha, B. (eds.). Association for Computing Machinery, p. 4692-4711 20 p. (Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms; vol. 2026-January).
• Extending the Extension: Deterministic Algorithm for Non-monotone Submodular Maximization, Buchbinder, N. & Feldman, M., 15 Jun 2025, STOC 2025 - Proceedings of the 57th Annual ACM Symposium on Theory of Computing. Koucky, M. & Bansal, N. (eds.). Association for Computing Machinery, p. 1130-1141 12 p. (Proceedings of the Annual ACM Symposium on Theory of Computing).
• Constrained Submodular Maximization via New Bounds for DR-Submodular Functions, Buchbinder, N. & Feldman, M., 10 Jun 2024, STOC 2024 - Proceedings of the 56th Annual ACM Symposium on Theory of Computing. Mohar, B., Shinkar, I. & O�Donnell, R. (eds.). Association for Computing Machinery, p. 1820-1831 12 p. (Proceedings of the Annual ACM Symposium on Theory of Computing).
• Maximum Matching Sans Maximal Matching: A New Approach for Finding Maximum Matchings in the Data Stream Model, Feldman, M. & Szarf, A., Apr 2024, In: Algorithmica. 86, 4, p. 1173-1209 37 p.
• Practical 0.385-Approximation for Submodular Maximization Subject to a Cardinality Constraint, Tukan, M., Mualem, L. & Feldman, M., 2024, In: Advances in Neural Information Processing Systems. 37
• Deterministic Algorithm and Faster Algorithm for Submodular Maximization Subject to a Matroid Constraint, Buchbinder, N. & Feldman, M., 2024, Proceedings - 2024 IEEE 65th Annual Symposium on Foundations of Computer Science, FOCS 2024. IEEE Computer Society, p. 700-712 13 p. (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS).
• Bridging the Gap Between General and Down-Closed Convex Sets in Submodular Maximization, Mualem, L., Tukan, M. & Feldman, M., 2024, Proceedings of the 33rd International Joint Conference on Artificial Intelligence, IJCAI 2024. Larson, K. (ed.). International Joint Conferences on Artificial Intelligence, p. 1926-1934 9 p. (IJCAI International Joint Conference on Artificial Intelligence).
• Submodular Minimax Optimization: Finding Effective Sets, Mualem, L., Elenberg, E. R., Feldman, M. & Karbasi, A., 2024, In: Proceedings of Machine Learning Research. 238, p. 1081-1089 9 p.
• The One-Way Communication Complexity of Submodular Maximization with Applications to Streaming and Robustness, Feldman, M., Norouzi-Fard, A., Svensson, O. & Zenklusen, R., 12 Aug 2023, In: Journal of the ACM. 70, 4, 3588564.
• Practical Budgeted Submodular Maximization, Feldman, M., Nutov, Z. & Shoham, E., May 2023, In: Algorithmica. 85, 5, p. 1332-1371 40 p.
• Resolving the Approximability of Offline and Online Non-monotone DR-Submodular Maximization over General Convex Sets, Mualem, L. & Feldman, M., 2023, In: Proceedings of Machine Learning Research. 206, p. 2542-2564 23 p.
• Deterministic (boldsymbol{(unicode{x00BD}+varepsilon)}) -Approximation for Submodular Maximization over a Matroid., Buchbinder, N., Feldman, M. & Garg, M., 2023, In: SIAM Journal on Computing. 52, 4, p. 945-967 23 p.
• How Do You Want Your Greedy: Simultaneous or Repeated?, Feldman, M., Harshaw, C. & Karbasi, A., 2023, In: Journal of Machine Learning Research. 24
• An Optimal Streaming Algorithm for Submodular Maximization with a Cardinality Constraint, Alaluf, N., Ene, A., Feldman, M., Nguyen, H. L. & Suh, A., Nov 2022, In: Mathematics of Operations Research. 47, 4, p. 2667-2690 24 p.
• Correction to: Guess Free Maximization of Submodular and Linear Sums. Guess Free Maximization of Submodular and Linear Sums (Algorithmica, (2021), 83, 3, (853-878), 10.1007/s00453-020-00757-9), Feldman, M., Oct 2022, In: Algorithmica. 84, 10, p. 3101-3102 2 p.
• Submodular Maximization Subject to Matroid Intersection on the Fly, Feldman, M., Norouzi-Fard, A., Svensson, O. & Zenklusen, R., 1 Sep 2022, 30th Annual European Symposium on Algorithms, ESA 2022. Chechik, S., Navarro, G., Rotenberg, E. & Herman, G. (eds.). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, (Leibniz International Proceedings in Informatics, LIPIcs; vol. 244).
• Maximizing Sums of Non-Monotone Submodular and Linear Functions: Understanding the Unconstrained Case, Bodek, K. & Feldman, M., 1 Sep 2022, 30th Annual European Symposium on Algorithms, ESA 2022. Chechik, S., Navarro, G., Rotenberg, E. & Herman, G. (eds.). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, (Leibniz International Proceedings in Informatics, LIPIcs; vol. 244).
• Maximum Matching Sans Maximal Matching: A New Approach for Finding Maximum Matchings in the Data Stream Model, Feldman, M. & Szarf, A., 1 Sep 2022, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2022. Chakrabarti, A. & Swamy, C. (eds.). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 33. (Leibniz International Proceedings in Informatics, LIPIcs; vol. 245).
• Streaming Submodular Maximization Under Matroid Constraints, Feldman, M., Liu, P., Norouzi-Fard, A., Svensson, O. & Zenklusen, R., 1 Jul 2022, 49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022. Bojanczyk, M., Merelli, E. & Woodruff, D. P. (eds.). Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing, 59. (Leibniz International Proceedings in Informatics, LIPIcs; vol. 229).
• The Power of Subsampling in Submodular Maximization, Harshaw, C., Kazemi, E., Feldman, M. & Karbasi, A., May 2022, In: Mathematics of Operations Research. 47, 2, p. 1365-1393 29 p.
• Submodular Maximization in Clean Linear Time, Li, W., Feldman, M., Kazemi, E. & Karbasi, A., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).
• Using Partial Monotonicity in Submodular Maximization, Mualem, L. & Feldman, M., 2022, Advances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022. Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K. & Oh, A. (eds.). Neural information processing systems foundation, (Advances in Neural Information Processing Systems; vol. 35).
• A FRAMEWORK FOR THE SECRETARY PROBLEM ON THE INTERSECTION OF MATROIDS, Feldman, M., Svensson, O. & Zenklusen, R., 2022, In: SIAM Journal on Computing. 51, 3, p. 766-819 54 p.
• Online contention resolution schemes with applications to Bayesian selection problems, Feldman, M., Svensson, O. & Zenklusen, R., 2021, In: SIAM Journal on Computing. 50, 2, p. 255-300 46 p.
• Regularized Submodular Maximization at Scale, Kazemi, E., Minaee, S., Feldman, M. & Karbasi, A., 2021, Proceedings of the 38th International Conference on Machine Learning, ICML 2021. ML Research Press, p. 5356-5366 11 p. (Proceedings of Machine Learning Research; vol. 139).
• Submodular + Concave, Mitra, S., Feldman, M. & Karbasi, A., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 11577-11591 15 p. (Advances in Neural Information Processing Systems; vol. 14).
• Continuous submodular maximization: Beyond DR-submodularity, Feldman, M. & Karbasi, A., Dec 2020, In: Advances in Neural Information Processing Systems. 2020-December
• Algorithms for Big Data, Feldman, M., 2020, World Scientific. 460 p.
• Streaming submodular maximization under a k-set system constraint, Haba, R., Kazemi, E., Feldman, M. & Karbasi, A., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 3897-3907 11 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-6).
• Streaming Submodular Maximization under a k-Set System Constraint, Haba, R., Kazemi, E., Feldman, M. & Karbasi, A., 2020, In: Proceedings of Machine Learning Research. 119
• Adaptive sequence submodularity, Mitrovic, M., Kazemi, E., Feldman, M., Krause, A. & Karbasi, A., 2019, In: Advances in Neural Information Processing Systems. 32
• Deterministic (1/2 + ε)-approximation for submodular maximization over a matroid, Buchbinder, N., Feldman, M. & Garg, M., 2019, p. 241-254. 14 p.
• Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications, Harshaw, C., Feldman, M., Ward, J. & Karbasi, A., 2019, In: Proceedings of Machine Learning Research. 97, p. 2634-2643 10 p.
• Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?, Chen, L., Feldman, M. & Karbasi, A., 1 Oct 2018, Proceedings of the 35th International Conference on Machine Learning. Dy, J. & Krause, A. (eds.). PMLR, Vol. 80. p. 804-813 10 p. (Proceedings of Machine Learning Research).
• Submodularity on hypergraphs: From sets to sequences, Mitrovic, M., Feldman, M., Krause, A. & Karbasi, A., 2018, p. 1177-1184. 8 p.
• Do less, get more: Streaming submodular maximization with subsampling, Feldman, M., Karbasi, A. & Kazemi, E., 2018, In: Advances in Neural Information Processing Systems. 2018-December, p. 732-742 11 p.
• Submodular Functions Maximization Problems, Buchbinder, N. & Feldman, M., 2018, Handbook of Approximation Algorithms and Metaheuristics. Gonzalez, T. F. (ed.). 2 ed. Chapman and Hall/CRC, 36 p.
• Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy?, Chen, L., Feldman, M. & Karbasi, A., 2018, In: Proceedings of Machine Learning Research. 80, p. 804-813 10 p.
• Submodularity on Hypergraphs: From Sets to Sequences, Mitrovic, M., Feldman, M., Krause, A. & Karbasi, A., 2018, In: Proceedings of Machine Learning Research. 84
• Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization, Feldman, M., Harshaw, C. & Karbasi, A., 5 Apr 2017, Proceedings of the 2017 Conference on Learning Theory (COLT). p. 758–784
• Streaming weak submodularity: Interpreting neural networks on the fly, Elenberg, E. R., Dimakis, A. G., Feldman, M. & Karbasi, A., 2017, In: Advances in Neural Information Processing Systems. 2017-December, p. 4045-4055 11 p.
• Greed Is Good: Near-Optimal Submodular Maximization via Greedy Optimization, Feldman, M., Harshaw, C. & Karbasi, A., 2017, In: Proceedings of Machine Learning Research. 65, p. 758-784 27 p.
• Deterministic algorithms for submodular maximization problems, Buchbinder, N. & Feldman, M., 2016, 27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016. Krauthgamer, R. (ed.). Association for Computing Machinery, p. 392-403 12 p. (Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms; vol. 1).
• A simple O(log log(rank))-competitive algorithm for the matroid secretary problem, Feldman, M., Svensson, O. & Zenklusen, R., 2015, Proceedings of the Twenty-Sixth Annual ACM-SIAM Symposium on Discrete Algorithms. USA: Society for Industrial and Applied Mathematics Publications, p. 1189–1201 (SODA '15).
• A Simple O (1oglog (rank))-competitive algorithm for the matroid secretary problem, Feldman, M., Svensson, O. & Zenklusen, R., 2015, p. 1189-1201. 13 p.
• A tight linear time (1/2)-approximation for unconstrained submodular maximization, Buchbinder, N., Feldman, M., Naor, J. & Schwartz, R., 2012, In: Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS. p. 649-658 10 p., 6375344.
• Effective management and energy efficiency in management of very large scale sensor network, Feldman, M. & Feldman, S., 2012, Sensors and Transducers, 14, SPEC. 2, p. 47-63 17 p.
• Improved Approximating Algorithms for Directed Steiner Forest, Feldman, M., Kortsarz, G. & Nutov, Z., 2009, Proceedings of the 2009 Annual ACM-SIAM Symposium on Discrete Algorithms (SODA). p. 932-941 10 p.

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