Lalitha Sankar

Artificial Intelligence · 2010–2020

A researcher in the Science corpus: 11 papers; trusts 86 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 205 citations into it; #12,684 of 35,345 by trust here, #12,387 by citations
• Privacy & Security — 66 citations into it
• Safety, Robustness & Interpretability — 56 citations into it
• Sociology and Political Science — 44 citations into it
• Statistics and Probability — 44 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it
• Safety Research — 29 citations into it; #4,858 of 5,829 by trust here, #3,512 by citations
• Electrical and Electronic Engineering — 20 citations into it

Whom Lalitha Sankar trusts overall

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

1. Cynthia Dwork
2. S. Chawla
3. Frank McSherry
4. Latanya Sweeney
5. Oded Goldreich
6. Flávio du Pin Calmon
7. Adam D. Smith
8. Peter Kairouz
9. Kobbi Nissim
10. William I. Gasarch

Who trusts Lalitha Sankar overall

1. Isabel Wagner
2. Peter Kairouz
3. Mario Díaz
4. Fatemehsadat Mireshghallah
5. David Eckhoff
6. Mohammadkazem Taram
7. Anand D. Sarwate
8. Chong Huang
9. Jihun Hamm
10. Hao Wang

Papers from Lalitha Sankar's perspective

Ordered by how strongly the researchers Lalitha Sankar 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Long Short-Term Memory
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• A rate-disortion perspective on local differential privacy (2014) — 50 citations
• On the Robustness of Information-Theoretic Privacy Measures and Mechanisms (2018) — 58 citations
• A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via f-Divergences (2020) — 42 citations
• Theoretical Guarantees for Model Auditing with Finite Adversaries (2019)
• Data-Driven Generation of Synthetic Load Datasets Preserving Spatio-Temporal Features (2019) — 23 citations
• Learning Generative Adversarial RePresentations (GAP) under Fairness and Censoring Constraints (2019) — 22 citations
• Censored and Fair Universal Representations using Generative Adversarial Models (2019) — 18 citations
• Generative Adversarial Privacy: A Data-Driven Approach to Information-Theoretic Privacy (2018) — 44 citations
• Context-Aware Generative Adversarial Privacy (2017) — 167 citations
• Utility-Privacy Tradeoffs in Databases: An Information-Theoretic Approach (2011) — 390 citations
• An information-theoretic approach to privacy (2010) — 31 citations