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  1. Pooya Jalaly, and Eva Tardos, Simple and Efficient Budget Feasible Mechanisms for Monotone Submodular Valuations, The 14th Conference on Web and Internet Economics, Oxford, December 2018. Thomas Kesselheim, Robert Kleinberg, Eva Tardos, Smooth Online Mechanisms, in the proceedings of the ACM Conference on Economics and Computation, Portland, OR June 2015.

  2. Fast approximation algorithms for fractional packing and covering problems. SA Plotkin, DB Shmoys, É Tardos. Mathematics of Operations Research 20 (2), 257-301. , 1995. 790. 1995. Approximation algorithms for classification problems with pairwise relationships: Metric labeling and Markov random fields.

  3. Tardos's research interest is algorithms and algorithmic game theory, an area concerned with designing and analyzing systems and algorithms for selfish users. Her research focuses on algorithms and games on networks.

  4. Éva Tardos (Budapeste, 1 de outubro de 1957) é uma matemática húngara. É professora da cátedra Jacob Gould Schurman de ciência da computação da Universidade Cornell. Tardos obteve o diploma de matemática em 1981 e um doutorado em 1984 pela Universidade Eötvös Loránd, orientada por András Frank. [1] Gábor Tardos é seu ...

  5. Research Interests. Tardos's research interest is algorithms and algorithmic game theory, an area concerned with designing and analyzing systems and algorithms for selfish users. Her research focuses on algorithms and games on networks.

  6. en.wikipedia.org › wiki › Éva_TardosÉva Tardos - Wikipedia

    Éva Tardos (born 1 October 1957) is a Hungarian mathematician and the Jacob Gould Schurman Professor of Computer Science at Cornell University. Tardos's research interest is algorithms. Her work focuses on the design and analysis of efficient methods for combinatorial optimization problems on graphs or networks.

  7. Algorithmic game theory is an emerging new area of designing systems and algorithms for selfish users. My research focuses on algorithms and games on graphs or networks. I am mostly interested in designing algorithms and games that provide provably close-to-optimal results.