Niv Buchbinder
Computational Theory and Mathematics · 2007–2015
A researcher in the Science corpus: 6 papers; trusts 29 researchers and is trusted by 22 researchers here, as inferred from the citation graph.
Subfields
- Computational Theory and Mathematics — 31 citations into it; #4,865 of 12,182 by trust here, #3,796 by citations
- Computer Networks and Communications — 22 citations into it; #2,057 of 9,886 by trust here, #3,993 by citations
- Artificial Intelligence — 15 citations into it; #15,427 of 35,345 by trust here, #17,412 by citations
- Algorithms & Complexity — 11 citations into it; #502 of 516 by trust here, #343 by citations
- Computer Graphics and Computer-Aided Design — 11 citations into it; #989 of 1,584 by trust here, #1,034 by citations
- Optimization & Training — 10 citations into it; #1,708 of 2,601 by trust here, #1,904 by citations
- Management Science and Operations Research — 8 citations into it; #2,884 of 11,946 by trust here, #4,921 by citations
- Industrial and Manufacturing Engineering — 6 citations into it; #1,034 of 2,521 by trust here, #2,150 by citations
Whom Niv Buchbinder trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Andreas Krause
- Hui-Ching Lin
- Uriel Feige
- Carlos Guestrin
- J. Bilmes
- Aranyak Mehta
- Joseph (Seffi) Naor
- Thomas S. Ferguson
- Jan Vondr'ak
- Moran Feldman
Who trusts Niv Buchbinder overall
- Aranyak Mehta
- Nikhil R. Devanur
- Sanjeev Arora
- Susanne Albers
- Jiashi Feng
- Shaojie Tang
- Moran Feldman
- Sebastian Buschjäger
- T-H. Hubert Chan
- Joseph (Seffi) Naor
Papers from Niv Buchbinder's perspective
Ordered by how strongly the researchers Niv Buchbinder trusts endorse each paper. Same corpus, a different order for every researcher.
- A threshold of ln n for approximating set cover (preliminary version)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Gradient-based learning applied to document recognition
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- An analysis of approximations for maximizing submodular set functions—I
- Maximizing the spread of influence through a social network
- ImageNet: A large-scale hierarchical image database
- Near-optimal Nonmyopic Value of Information in Graphical Models
Their papers
- A Tight Linear Time (1/2)-Approximation for Unconstrained Submodular Maximization (2012) — 477 citations
- The Design of Competitive Online Algorithms via a Primal-Dual Approach (2009) — 424 citations
- Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue (2007) — 379 citations
- Submodular Maximization with Cardinality Constraints (2014) — 341 citations
- Online Submodular Maximization with Preemption (2015) — 128 citations
- Secretary Problems via Linear Programming (2010) — 79 citations