Vasilis Syrgkanis

Management Science and Operations Research · 2015–2017

A researcher in the Science corpus: 5 papers; trusts 26 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Management Science and Operations Research — 30 citations into it
• Artificial Intelligence — 26 citations into it; #25,559 of 35,345 by trust here, #21,316 by citations
• Statistics and Probability — 16 citations into it
• Optimization & Training — 14 citations into it
• Reinforcement Learning — 9 citations into it
• Computer Networks and Communications — 7 citations into it
• Computational Mechanics — 5 citations into it
• Generative Models — 5 citations into it; #592 of 1,956 by trust here, #909 by citations

Whom Vasilis Syrgkanis trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alexander Rakhlin
2. Yurii E. Nesterov
3. Yoav Freund
4. Karthik Sridharan
5. Nick Littlestone
6. Adam Tauman Kalai
7. Sergiu Hart
8. Robert E. Schapire
9. Shai Shalev-Shwartz
10. Joel A. Tropp

Who trusts Vasilis Syrgkanis overall

1. Gauthier Gidel
2. Constantinos Daskalakis
3. Aleksandrs Slivkins
4. Panayotis Mertikopoulos
5. Andrew Cotter
6. Waïss Azizian
7. Yu-Guan Hsieh
8. Tengyuan Liang
9. Alistair Letcher
10. David Balduzzi

Papers from Vasilis Syrgkanis's perspective

Ordered by how strongly the researchers Vasilis Syrgkanis trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. GENERATIVE ADVERSARIAL NETS
5. Gradient-based learning applied to document recognition
6. ImageNet: A large-scale hierarchical image database
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Fast Convergence of Regularized Learning in Games (2015) — 305 citations
• Robust Optimization for Non-Convex Objectives (2017) — 130 citations
• Accurate Inference for Adaptive Linear Models (2017) — 72 citations
• Training GANs with Optimism (2017) — 593 citations
• No-Regret Learning in Bayesian Games (2015) — 72 citations