Countries citing papers authored by Akshay Krishnamurthy
Since
Specialization
Citations
This map shows the geographic impact of Akshay Krishnamurthy'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 Akshay Krishnamurthy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akshay Krishnamurthy more than expected).
Fields of papers citing papers by Akshay Krishnamurthy
This network shows the impact of papers produced by Akshay Krishnamurthy. 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 Akshay Krishnamurthy. The network helps show where Akshay Krishnamurthy may publish in the future.
Co-authorship network of co-authors of Akshay Krishnamurthy
This figure shows the co-authorship network connecting the top 25 collaborators of Akshay Krishnamurthy.
A scholar is included among the top collaborators of Akshay Krishnamurthy based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Akshay Krishnamurthy. Akshay Krishnamurthy is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Wang, Yining, Ruosong Wang, Simon S. Du, & Akshay Krishnamurthy. (2021). Optimism in Reinforcement Learning with Generalized Linear Function Approximation. arXiv (Cornell University).2 indexed citations
2.
Simchowitz, Max, Akshay Krishnamurthy, Daniel Hsu, et al.. (2021). Bayesian decision-making under misspecified priors with applications to meta-learning. arXiv (Cornell University). 34.1 indexed citations
Foster, Dylan J., Akshay Krishnamurthy, & Haipeng Luo. (2020). Open Problem: Model Selection for Contextual Bandits. Conference on Learning Theory. 3842–3846.1 indexed citations
5.
Su, Yi, et al.. (2020). Doubly robust off-policy evaluation with shrinkage. International Conference on Machine Learning. 1. 9167–9176.2 indexed citations
6.
Ash, Jordan T., Chicheng Zhang, Akshay Krishnamurthy, John Langford, & Alekh Agarwal. (2020). Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds. International Conference on Learning Representations.22 indexed citations
7.
Kakade, Sham M., et al.. (2020). Information Theoretic Regret Bounds for Online Nonlinear Control.. arXiv (Cornell University). 33. 15312–15325.1 indexed citations
8.
Misra, Dipendra, Mikael Henaff, Akshay Krishnamurthy, & John Langford. (2020). Kinematic State Abstraction and Provably Efficient Rich-Observation Reinforcement Learning. International Conference on Machine Learning. 1. 6961–6971.6 indexed citations
9.
Krishnamurthy, Akshay, et al.. (2020). Corrupted Multidimensional Binary Search: Learning in the Presence of Irrational Agents. arXiv (Cornell University).
10.
Kandasamy, Kirthevasan, et al.. (2019). Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments.. International Conference on Machine Learning. 3222–3232.5 indexed citations
11.
Sun, Wen, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, & John Langford. (2019). Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. Conference on Learning Theory. 2898–2933.9 indexed citations
12.
Foster, Dylan J., Akshay Krishnamurthy, & Haipeng Luo. (2019). Model Selection for Contextual Bandits. arXiv (Cornell University). 32. 14714–14725.1 indexed citations
13.
Krishnamurthy, Akshay, et al.. (2019). Sample Complexity of Learning Mixture of Sparse Linear Regressions. arXiv (Cornell University). 32. 10531–10540.2 indexed citations
14.
Krishnamurthy, Akshay, John Langford, Aleksandrs Slivkins, & Chicheng Zhang. (2019). Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adapting. Journal of Machine Learning Research. 21(137). 1–2027.1 indexed citations
15.
Kandasamy, Kirthevasan, Akshay Krishnamurthy, Jeff Schneider, & Barnabás Póczos. (2018). Parallelised Bayesian Optimisation via Thompson Sampling. International Conference on Artificial Intelligence and Statistics. 133–142.44 indexed citations
16.
Das, Rajarshi, Shehzaad Dhuliawala, Manzil Zaheer, et al.. (2017). Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning.. Neural Information Processing Systems.4 indexed citations
17.
Das, Rajarshi, Shehzaad Dhuliawala, Manzil Zaheer, et al.. (2017). Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning.. arXiv (Cornell University).20 indexed citations
18.
Krishnamurthy, Akshay, et al.. (2017). Active Learning for Cost-Sensitive Classification. arXiv (Cornell University). 70. 1915–1924.4 indexed citations
19.
Agarwal, Alekh, Akshay Krishnamurthy, John Langford, Haipeng Luo, & Robert E. Schapire. (2017). Open Problem: First-Order Regret Bounds for Contextual Bandits. Conference on Learning Theory. 65. 4–7.
20.
Krishnamurthy, Akshay, et al.. (2017). An online hierarchical algorithm for extreme clustering. Knowledge Discovery and Data Mining.2 indexed citations
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.