Yevgeniy Vorobeychik

Artificial Intelligence · 2004–2018

A researcher in the Science corpus: 12 papers; trusts 69 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #7,982 of 35,345 by trust here, #10,942 by citations
• Safety, Robustness & Interpretability — 87 citations into it; #500 of 2,251 by trust here, #705 by citations
• Computer Networks and Communications — 54 citations into it; #1,766 of 9,886 by trust here, #2,242 by citations
• Signal Processing — 43 citations into it; #2,195 of 7,572 by trust here, #5,340 by citations
• Information Systems — 36 citations into it
• Privacy & Security — 23 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Game & Decision Theory — 13 citations into it

Whom Yevgeniy Vorobeychik trusts overall

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

1. Daniel Lowd
2. Michael Brückner
3. Blaine Nelson
4. Battista Biggio
5. Christopher Meek
6. Bo Li
7. Tobias Scheffer
8. Nedim Srndic
9. Nilesh N. Dalvi
10. Robin Sommer

Who trusts Yevgeniy Vorobeychik overall

1. Bo Li
2. Liang Tong
3. Nicolas Papernot
4. Liyiming Ke
5. Patrick Drew McDaniel
6. Arunesh Sinha
7. Ahmed Salem
8. Chen Hajaj
9. Chaowei Xiao
10. Binghui Wang

Papers from Yevgeniy Vorobeychik's perspective

Ordered by how strongly the researchers Yevgeniy Vorobeychik 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Data Poisoning Attacks on Factorization-Based Collaborative Filtering (2016) — 383 citations
• Optimal randomized classification in adversarial settings (2014) — 71 citations
• Behavioral dynamics and influence in networked coloring and consensus (2010) — 117 citations
• It’s all in the timing: calibrating temporal penalties for biomedical data sharing (2018) — 12 citations
• Hardening Classifiers against Evasion: the Good, the Bad, and the Ugly (2017) — 9 citations
• Robust Linear Regression Against Training Data Poisoning (2017) — 99 citations
• Feature Conservation in Adversarial Classifier Evasion: A Case Study (2017) — 9 citations
• Robust High-Dimensional Linear Regression (2016)
• Behavioral Experiments in Email Filter Evasion (2016) — 10 citations
• Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings (2015) — 66 citations
• Feature Cross-Substitution in Adversarial Classification (2014) — 124 citations
• Notes on Equilibria in Symmetric Games (2004) — 165 citations