Adarsh Subbaswamy

Artificial Intelligence · 2017–2021

A researcher in the Science corpus: 4 papers; trusts 81 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 44 citations into it; #28,439 of 35,345 by trust here, #24,417 by citations
• Statistics and Probability — 23 citations into it; #10,513 of 12,932 by trust here, #11,487 by citations
• Causal Inference & Discovery — 21 citations into it
• Probabilistic & Bayesian Methods — 16 citations into it
• Representation & Self-Supervised Learning — 11 citations into it; #2,978 of 3,145 by trust here, #2,516 by citations
• Epidemiology — 8 citations into it
• Management Science and Operations Research — 8 citations into it
• Medical & Health AI — 7 citations into it

Whom Adarsh Subbaswamy trusts overall

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

1. Judea Pearl
2. Donald B. Rubin
3. Elias Bareinboim
4. Bernhard Schölkopf
5. Kun Zhang
6. Ilya Shpitser
7. Tin Kam Ho
8. Masashi Sugiyama
9. Thomas S. Richardson
10. Jin Tian

Who trusts Adarsh Subbaswamy overall

1. Bernhard Schölkopf
2. Maggie Makar
3. Harvineet Singh
4. Ioana Bica
5. Ben Packer
6. Daniel Coelho de Castro
7. Francesco Locatello
8. Rina Singh

Papers from Adarsh Subbaswamy's perspective

Ordered by how strongly the researchers Adarsh Subbaswamy 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms (2018) — 27 citations
• The Clinician and Dataset Shift in Artificial Intelligence (2021) — 593 citations
• Learning Treatment-Response Models from Multivariate Longitudinal Data (2017) — 21 citations
• Preventing Failures Due to Dataset Shift: Learning Predictive Models That Transport (2018) — 163 citations