Constantinos Daskalakis

Artificial Intelligence · 2006–2020

A researcher in the Science corpus: 13 papers; trusts 106 researchers and is trusted by 59 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #5,628 of 35,345 by trust here, #8,706 by citations
• Optimization & Training — 76 citations into it; #1,105 of 2,601 by trust here, #1,804 by citations
• Management Science and Operations Research — 59 citations into it; #1,763 of 11,946 by trust here, #1,695 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #12,529 of 21,575 by trust here, #11,083 by citations
• Safety, Robustness & Interpretability — 41 citations into it; #1,245 of 2,251 by trust here, #1,437 by citations
• Computational Theory and Mathematics — 38 citations into it; #9,996 of 12,182 by trust here, #10,901 by citations
• Generative Models — 33 citations into it; #538 of 1,956 by trust here, #661 by citations
• Numerical Analysis — 32 citations into it

Whom Constantinos Daskalakis trusts overall

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

1. Alexander Rakhlin
2. Yurii E. Nesterov
3. Diederik P. Kingma
4. Individualized Treat
5. Shai Shalev-Shwartz
6. Ian J. Goodfellow
7. Geoffrey E. Hinton
8. Yoshua Bengio
9. Alex Krizhevsky
10. Martín Arjovsky

Who trusts Constantinos Daskalakis overall

1. Gauthier Gidel
2. Chi Jin
3. Waïss Azizian
4. Xi Chen
5. Michael I. Jordan
6. Yu-Guan Hsieh
7. Simon Lacoste-Julien
8. Tianyi Lin
9. Maher Nouiehed
10. Tengyuan Liang

Papers from Constantinos Daskalakis's perspective

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

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

Their papers

• The Robust Manifold Defense: Adversarial Training using Generative Models (2017) — 176 citations
• The Limit Points of (Optimistic) Gradient Descent in Min-Max Optimization (2018) — 294 citations
• The complexity of constrained min-max optimization (2020) — 169 citations
• Last Iterate is Slower than Averaged Iterate in Smooth Convex-Concave Saddle Point Problems (2020) — 117 citations
• A Counter-example to Karlin's Strong Conjecture for Fictitious Play (2014) — 50 citations
• The complexity of computing a Nash equilibrium (2006) — 1,530 citations
• The complexity of computing a Nash equilibrium (2009) — 450 citations
• Which Distribution Distances are Sublinearly Testable? (2017) — 50 citations
• Efficient Methods for Structured Nonconvex-Nonconcave Min-Max Optimization (2020) — 178 citations
• Tight last-iterate convergence rates for no-regret learning in multi-player games (2020) — 97 citations
• Efficient Statistics, in High Dimensions, from Truncated Samples (2018) — 59 citations
• Training GANs with Optimism (2017) — 593 citations
• Priv'IT: Private and Sample Efficient Identity Testing (2017) — 56 citations