Himanshu Tyagi

Artificial Intelligence · with Acharya · 2014–2021

A researcher in the Science corpus: 11 papers; trusts 78 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 194 citations into it; #5,494 of 35,345 by trust here, #8,157 by citations
• Statistics and Probability — 102 citations into it; #2,672 of 12,932 by trust here, #2,965 by citations
• Computational Theory and Mathematics — 71 citations into it; #5,562 of 12,182 by trust here, #6,798 by citations
• Privacy & Security — 45 citations into it
• Computer Networks and Communications — 43 citations into it; #2,117 of 9,886 by trust here, #3,426 by citations
• Electrical and Electronic Engineering — 42 citations into it; #1,636 of 16,539 by trust here, #6,657 by citations
• Statistical Learning Theory — 28 citations into it
• Information Theory — 23 citations into it; #53 of 196 by trust here, #62 by citations

Whom Himanshu Tyagi trusts overall

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

1. Oded Goldreich
2. Jayadev Acharya
3. Liam Paninski
4. Ilias Diakonikolas
5. Clément Louis Canonne
6. Ronitt Rubinfeld
7. Yanjun Han
8. Gregory Valiant
9. John C. Duchi
10. Te Sun Han

Who trusts Himanshu Tyagi overall

1. Jayadev Acharya
2. Clément Louis Canonne
3. Wei-Ning Chen
4. Ziteng Sun
5. Ayfer Özgür
6. Leighton Pate Barnes
7. Yanjun Han
8. Peter Kairouz
9. Matthew Joseph
10. Prathamesh Mayekar

Papers from Himanshu Tyagi's perspective

Ordered by how strongly the researchers Himanshu Tyagi 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. Learning Multiple Layers of Features from Tiny Images
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Information Theory: 1948-1998 - Guest Editorial
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Inference Under Information Constraints I: Lower Bounds From Chi-Square Contraction (2018) — 126 citations
• Distributed Simulation and Distributed Inference (2018) — 26 citations
• Domain Compression and its Application to Randomness-Optimal Distributed Goodness-of-Fit (2019) — 28 citations
• Inference Under Information Constraints II: Communication Constraints and Shared Randomness (2019) — 66 citations
• Extra Samples can Reduce the Communication for Independence Testing (2018) — 12 citations
• Inference Under Information Constraints III: Local Privacy Constraints (2021) — 40 citations
• Limits on Gradient Compression for Stochastic Optimization (2020) — 26 citations
• Unified lower bounds for interactive high-dimensional estimation under information constraints (2020) — 41 citations
• Distributed Signal Detection under Communication Constraints (2020) — 26 citations
• Test without Trust: Optimal Locally Private Distribution Testing (2018) — 64 citations
• Estimating Renyi Entropy of Discrete Distributions (2014) — 84 citations