Rishabh Agarwal

1.1k citations
19 papers · 186 · h-index 7

Impact in

Papers in

Rishabh Agarwal

16 papers receiving 181 citations

Peers

Rishabh Agarwal
Comparison fields: 5 of 76
  • Anesthesiology and Pain Medicine 11
  • Artificial Intelligence 55
  • Cellular and Molecular Neuroscience 20
  • Management Science and Operations Research 14
  • Biomaterials 11
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Citations per field
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Citations per year

Countries citing papers authored by Rishabh Agarwal

Since Specialization
Citations

This map shows the geographic impact of Rishabh Agarwal's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rishabh Agarwal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishabh Agarwal more than expected).

Fields of papers citing papers by Rishabh Agarwal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Rishabh Agarwal. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rishabh Agarwal. The network helps show where Rishabh Agarwal may publish in the future.

Co-authors

The 25 scholars most cited alongside Rishabh Agarwal, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Rishabh Agarwal Line = papers co-authored together Rishabh Agarwal links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1 201564
2 202033
3
An Optimistic Perspective on Offline Deep Reinforcement Learning
202016
4 202115
5 202013
6
Learning to Generalize from Sparse and Underspecified Rewards
201912
7 20247
8 20186
9 20225
10 20175
11
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning.
20202
12 20232
13 20202
14 20191
15 20191
16
Intracoronary stent placement in thrombus containing vein graft lesions.
19951
17 20151
18 20230
19 20240

About Rishabh Agarwal

Rishabh Agarwal is a scholar working on Artificial Intelligence, Surgery, Pulmonary and Respiratory Medicine, Molecular Biology and Electrical and Electronic Engineering, having authored 19 papers that have together received 186 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (3 papers), RNA Interference and Gene Delivery (2 papers), Natural Language Processing Techniques (2 papers), Topic Modeling (2 papers), Cerebrospinal fluid and hydrocephalus (1 paper), Venous Thromboembolism Diagnosis and Management (1 paper), Traumatic Brain Injury and Neurovascular Disturbances (1 paper) and Spinal Dysraphism and Malformations (1 paper). The work is most often cited by research in Anesthesiology and Pain Medicine (11 citations), Artificial Intelligence (55 citations), Cellular and Molecular Neuroscience (20 citations), Management Science and Operations Research (14 citations) and Biomaterials (11 citations). Rishabh Agarwal has collaborated with scholars based in United States, India and Canada. Frequent co-authors include Mohammad Norouzi, Dale Schuurmans, Mark Rowland, Michael H. Stewart, William Fedus, Hugo Larochelle, Alan L. Huston, Yoshua Bengio, Philip E. Dawson and Glyn Dawson. Their work appears in journals such as Korean journal of anesthesiology, ACS Chemical Neuroscience, Nanoscience & Nanotechnology-Asia, Radiology Case Reports and Cureus.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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