Kailash Budhathoki

Causal Inference & Discovery · 2016–2017

A researcher in the Science corpus: 2 papers; trusts 26 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Causal Inference & Discovery — 23 citations into it
• Artificial Intelligence — 21 citations into it; #13,882 of 35,345 by trust here, #29,412 by citations
• Computational Theory and Mathematics — 11 citations into it; #5,511 of 12,182 by trust here, #10,349 by citations
• Information Theory — 9 citations into it
• Statistics and Probability — 9 citations into it; #9,122 of 12,932 by trust here, #11,487 by citations
• Probabilistic & Bayesian Methods — 7 citations into it
• Statistical Learning Theory — 5 citations into it

Whom Kailash Budhathoki trusts overall

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

1. Dominik Janzing
2. Jorma Rissanen
3. Jan Peters
4. Bernhard Schölkopf
5. Tom L. Burr
6. Joris M. Mooij
7. Peter D. Grünwald
8. Ray J. Solomonoff
9. Patrik O. Hoyer
10. Eleni Sgouritsa

Who trusts Kailash Budhathoki overall

1. Alexander Marx
2. Bernhard Schölkopf
3. Niki Kilbertus
4. Jovana Mitrovi'c
5. Nan Rosemary Ke
6. Jilles Vreeken
7. Dino Sejdinovic
8. Giambattista Parascandolo
9. Olexa Bilaniuk
10. Yee Whye Teh

Papers from Kailash Budhathoki's perspective

Ordered by how strongly the researchers Kailash Budhathoki 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. GENERATIVE ADVERSARIAL NETS
6. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Causal Inference by Stochastic Complexity (2017) — 9 citations
• Origo: causal inference by compression (2016) — 67 citations