Niki Kilbertus

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 8 papers; trusts 77 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it; #12,637 of 35,345 by trust here, #5,359 by citations
• Statistics and Probability — 49 citations into it
• Safety, Robustness & Interpretability — 47 citations into it; #1,632 of 2,251 by trust here, #660 by citations
• Causal Inference & Discovery — 44 citations into it; #260 of 364 by trust here, #181 by citations
• Safety Research — 35 citations into it; #2,228 of 5,829 by trust here, #456 by citations
• Representation & Self-Supervised Learning — 33 citations into it; #1,960 of 3,145 by trust here, #2,066 by citations
• Computer Vision and Pattern Recognition — 32 citations into it; #14,846 of 21,575 by trust here, #12,419 by citations
• Sociology and Political Science — 25 citations into it

Whom Niki Kilbertus trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Bernhard Schölkopf
4. Yoshua Bengio
5. Josh Tenenbaum
6. Nathan Kallus
7. Muhammad Bilal Zafar
8. Alex Krizhevsky
9. Ruslan Salakhutdinov
10. Dominik Janzing

Who trusts Niki Kilbertus overall

1. Bernhard Schölkopf
2. Silvia Chiappa
3. Dana Pessach
4. Simon Caton
5. Luca Oneto
6. Adrian Weller
7. Francesco Locatello
8. Amir-Hossein Karimi
9. Sahil Verma
10. Cynthia Dwork

Papers from Niki Kilbertus's perspective

Ordered by how strongly the researchers Niki Kilbertus 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. GENERATIVE ADVERSARIAL NETS

Their papers

• Learning Independent Causal Mechanisms (2017) — 205 citations
• Avoiding Discrimination through Causal Reasoning (2017) — 640 citations
• Generalization in anti-causal learning (2018) — 56 citations
• Blind Justice: Fairness with Encrypted Sensitive Attributes (2018) — 167 citations
• The Sensitivity of Counterfactual Fairness to Unmeasured Confounding (2019) — 65 citations
• Causal machine learning for predicting treatment outcomes (2024) — 334 citations
• Is Independence all you need? On the Generalization of Representations Learned from Correlated Data (2020) — 19 citations
• Fair Decisions Despite Imperfect Predictions (2019) — 70 citations