Aadirupa Saha

Reinforcement Learning · 2015–2022

A researcher in the Science corpus: 8 papers; trusts 101 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 112 citations into it; #867 of 2,242 by trust here, #1,689 by citations
• Artificial Intelligence — 94 citations into it; #12,974 of 35,345 by trust here, #23,731 by citations
• Management Science and Operations Research — 78 citations into it; #3,546 of 11,946 by trust here, #6,314 by citations
• Computer Networks and Communications — 34 citations into it
• Statistical Learning Theory — 17 citations into it; #1,001 of 1,038 by trust here, #985 by citations
• Optimization & Training — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it; #1,822 of 3,147 by trust here, #3,037 by citations
• Information Retrieval & Search — 12 citations into it

Whom Aadirupa Saha trusts overall

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

1. Yisong Yue
2. Masrour Zoghi
3. Thorsten Joachims
4. Peter Auer
5. Nir Ailon
6. Lihong Li
7. Nicolò Cesa-Bianchi
8. Róbert Istvan Busa-Fekete
9. Zohar S. Karnin
10. Miroslav Dud'ik

Who trusts Aadirupa Saha overall

1. Aldo Pacchiano
2. Suprovat Ghoshal
3. Banghua Zhu
4. Jonathan Lee
5. Wenbo Ren
6. Jiantao Jiao
7. Wei Xiong
8. Jia Liu
9. Michael I. Jordan
10. Harikrishna Narasimhan

Papers from Aadirupa Saha's perspective

Ordered by how strongly the researchers Aadirupa Saha 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Reinforcement Learning: An Introduction
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Dueling RL: Reinforcement Learning with Trajectory Preferences (2021) — 120 citations
• Exploiting Correlation to Achieve Faster Learning Rates in Low-Rank Preference Bandits (2022) — 13 citations
• PAC Battling Bandits in the Plackett-Luce Model (2018) — 40 citations
• Active Ranking with Subset-wise Preferences (2018) — 31 citations
• Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability (2021) — 49 citations
• Optimal Algorithms for Stochastic Contextual Preference Bandits (2021) — 62 citations
• Battle of Bandits (2018) — 48 citations
• Consistent Multiclass Algorithms for Complex Performance Measures (2015) — 68 citations