Anand Rajagopalan
Anand Rajagopalan
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Batch active learning at scale
G Citovsky, G DeSalvo, C Gentile, L Karydas, A Rajagopalan, ...
Advances in Neural Information Processing Systems 34, 11933-11944, 2021
Scaling hierarchical agglomerative clustering to billion-sized datasets
B Sumengen, A Rajagopalan, G Citovsky, D Simcha, O Bachem, P Mitra, ...
arXiv preprint arXiv:2105.11653, 2021
Online hierarchical clustering approximations
AK Menon, A Rajagopalan, B Sumengen, G Citovsky, Q Cao, S Kumar
arXiv preprint arXiv:1909.09667, 2019
Hierarchical clustering of data streams: Scalable algorithms and approximation guarantees
A Rajagopalan, F Vitale, D Vainstein, G Citovsky, CM Procopiuc, ...
International conference on machine learning, 8799-8809, 2021
Hierarchical clustering via sketches and hierarchical correlation clustering
D Vainstein, V Chatziafratis, G Citovsky, A Rajagopalan, M Mahdian, ...
International Conference on Artificial Intelligence and Statistics, 559-567, 2021
Flattening a hierarchical clustering through active learning
F Vitale, A Rajagopalan, C Gentile
Advances in Neural Information Processing Systems 32, 2019
A 5nm 3.4 GHz tri-gear ARMv9 CPU subsystem in a fully integrated 5G flagship mobile SoC
A Nayak, HC Chen, H Mair, R Lagerquist, T Chen, A Rajagopalan, ...
2022 IEEE International Solid-State Circuits Conference (ISSCC) 65, 50-52, 2022
Outlier eigenvalue fluctuations of perturbed iid matrices
AB Rajagopalan
University of California, Los Angeles, 2015
4.1 A 7nm 5G Mobile SoC Featuring a 3.0 GHz Tri-Gear Application Processor Subsystem
H Chen, R Lagerquist, A Nayak, H Mair, G Manoharan, E Wang, ...
2021 IEEE International Solid-State Circuits Conference (ISSCC) 64, 54-56, 2021
A 20nm 2.5 GHz ultra-low-power tri-cluster CPU subsystem with adaptive power allocation for optimal mobile SoC performance. In 2016 IEEE International Solid-State Circuits …
HT Mair, G Gammie, A Wang, R Lagerquist, CJ Chung, S Gururajarao, ...
IEEE, 2016
Circuit and method to measure simulation to silicon timing correlation
AK Nayak, HT Mair, A Varma, A Rajagopalan
US Patent 11,835,580, 2023
Batch Active Learning at Scale
A Rostamizadeh, C Gentile, G DeSalvo, G Citovsky, L Karydas, S Kumar
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