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ORCIDAnup B. Rao
Anup Rao 0002
Person information
- affiliation: Adobe Research, San Jose, CA, USA
- affiliation (former): Yale University, New Haven, CT, USA
Other persons with the same name
- Anup Rao 0001
![[0000-0002-6449-9547]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-6449-9547")
— University of Washington, Seattle, WA, USA (and 2 more) - Anup Rao 0003 — Cisco, USA (and 1 more)
- Anup Rao 0004 — Georgia Institute of Technology, Collecge of Computing, Atlanta, GA, USA
- Anup Rao 0005 — University of California at Los Angeles, David Geffen School of Medicine, CA, USA
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2020 – today
- 2024
[c45]Junda Wu, Tong Yu, Xiang Chen, Haoliang Wang, Ryan A. Rossi, Sungchul Kim, Anup B. Rao, Julian J. McAuley:
DeCoT: Debiasing Chain-of-Thought for Knowledge-Intensive Tasks in Large Language Models via Causal Intervention. ACL (1) 2024: 14073-14087- [c44]Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan A. Rossi, Anup B. Rao, Tung Mai, Shuai Li:
Hallucination Diversity-Aware Active Learning for Text Summarization. NAACL-HLT 2024: 8665-8677 - [i37]Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan A. Rossi, Anup B. Rao, Tung Mai, Shuai Li:
Hallucination Diversity-Aware Active Learning for Text Summarization. CoRR abs/2404.01588 (2024) - 2023
- [c43]Tung Mai, Alexander Munteanu, Cameron Musco, Anup Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. AISTATS 2023: 11288-11316 - [c42]Gaurav Gupta, Anup B. Rao, Tung Mai, Ryan A. Rossi, Xiang Chen, Saayan Mitra, Anshumali Shrivastava:
Near Neighbor Search for Constraint Queries. IEEE Big Data 2023: 1707-1715 - [c41]Renzhi Wu, Saayan Mitra, Xiang Chen, Anup B. Rao:
Decentralized Personalized Online Federated Learning. IEEE Big Data 2023: 1873-1882 - [c40]Sudhanshu Chanpuriya, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Zhao Song, Cameron Musco:
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings. NeurIPS 2023 - [c39]Mehrdad Ghadiri, David Arbour, Tung Mai, Cameron Musco, Anup B. Rao:
Finite Population Regression Adjustment and Non-asymptotic Guarantees for Treatment Effect Estimation. NeurIPS 2023 - [c38]Aniket Murhekar, David Arbour, Tung Mai, Anup B. Rao:
Brief Announcement: Dynamic Vector Bin Packing for Online Resource Allocation in the Cloud. SPAA 2023: 307-310 - [i36]Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao, Chris Schwiegelshohn, David P. Woodruff:
Optimal Sketching Bounds for Sparse Linear Regression. CoRR abs/2304.02261 (2023) - [i35]Aniket Murhekar, David Arbour, Tung Mai, Anup Rao:
Dynamic Vector Bin Packing for Online Resource Allocation in the Cloud. CoRR abs/2304.08648 (2023) - [i34]Renzhi Wu, Saayan Mitra, Xiang Chen, Anup Rao:
Decentralized Personalized Online Federated Learning. CoRR abs/2311.04817 (2023) - 2022
- [c37]Nikhil Sheoran, Subrata Mitra, Vibhor Porwal, Siddharth Ghetia, Jatin Varshney, Tung Mai, Anup B. Rao, Vikas Maddukuri:
Conditional Generative Model Based Predicate-Aware Query Approximation. AAAI 2022: 8259-8266 - [c36]David Arbour, Drew Dimmery, Tung Mai, Anup B. Rao:
Online Balanced Experimental Design. ICML 2022: 844-864 - [c35]Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup B. Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen K. Ahmed:
One-Pass Algorithms for MAP Inference of Nonsymmetric Determinantal Point Processes. ICML 2022: 18463-18482 - [c34]Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao:
Sample Constrained Treatment Effect Estimation. NeurIPS 2022 - [c33]Vibhor Porwal, Subrata Mitra, Fan Du, John Anderson, Nikhil Sheoran, Anup B. Rao, Tung Mai, Gautam Kowshik, Sapthotharan Nair, Sameeksha Arora, Saurabh Mahapatra:
Efficient Insights Discovery through Conditional Generative Model based Query Approximation. SIGMOD Conference 2022: 2397-2400 - [i33]Nikhil Sheoran, Subrata Mitra, Vibhor Porwal, Siddharth Ghetia, Jatin Varshney, Tung Mai, Anup B. Rao, Vikas Maddukuri:
Electra: Conditional Generative Model based Predicate-Aware Query Approximation. CoRR abs/2201.12420 (2022) - [i32]Raghavendra Addanki, David Arbour, Tung Mai, Cameron Musco, Anup Rao:
Sample Constrained Treatment Effect Estimation. CoRR abs/2210.06594 (2022) - 2021
- [j2]Ryan A. Rossi, Nesreen K. Ahmed, Aldo G. Carranza, David Arbour, Anup B. Rao, Sungchul Kim, Eunyee Koh:
Heterogeneous Graphlets. ACM Trans. Knowl. Discov. Data 15(1): 9:1-9:43 (2021) - [c32]Jiong Zhu, Ryan A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra:
Graph Neural Networks with Heterophily. AAAI 2021: 11168-11176 - [c31]My Phan, David Arbour, Drew Dimmery, Anup B. Rao:
Designing Transportable Experiments Under S-admissability. AISTATS 2021: 2539-2547 - [c30]David Arbour, Drew Dimmery, Anup B. Rao:
Efficient Balanced Treatment Assignments for Experimentation. AISTATS 2021: 3070-3078 - [c29]Ryan A. Rossi, Anup B. Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed, Gang Wu:
From Closing Triangles to Higher-Order Motif Closures for Better Unsupervised Online Link Prediction. CIKM 2021: 4085-4093 - [c28]Enayat Ullah, Tung Mai, Anup Rao, Ryan A. Rossi, Raman Arora:
Machine Unlearning via Algorithmic Stability. COLT 2021: 4126-4142 - [c27]Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup B. Rao, Tung Mai:
Fundamental Tradeoffs in Distributionally Adversarial Training. ICML 2021: 7544-7554 - [c26]Mojtaba Sahraee-Ardakan, Tung Mai, Anup B. Rao, Ryan A. Rossi, Sundeep Rangan, Alyson K. Fletcher:
Asymptotics of Ridge Regression in Convolutional Models. ICML 2021: 9265-9275 - [c25]Gromit Yeuk-Yin Chan, Tung Mai, Anup B. Rao, Ryan A. Rossi, Fan Du, Cláudio T. Silva, Juliana Freire:
Interactive Audience Expansion On Large Scale Online Visitor Data. KDD 2021: 2621-2631 - [c24]Tung Mai, Cameron Musco, Anup Rao:
Coresets for Classification - Simplified and Strengthened. NeurIPS 2021: 11643-11654 - [i31]Mohammad Mehrabi, Adel Javanmard, Ryan A. Rossi, Anup Rao, Tung Mai:
Fundamental Tradeoffs in Distributionally Adversarial Training. CoRR abs/2101.06309 (2021) - [i30]Enayat Ullah, Tung Mai, Anup Rao, Ryan A. Rossi, Raman Arora:
Machine Unlearning via Algorithmic Stability. CoRR abs/2102.13179 (2021) - [i29]Mojtaba Sahraee-Ardakan, Tung Mai, Anup B. Rao, Ryan A. Rossi, Sundeep Rangan, Alyson K. Fletcher:
Asymptotics of Ridge Regression in Convolutional Models. CoRR abs/2103.04557 (2021) - [i28]Tung Mai, Anup B. Rao, Cameron Musco:
Coresets for Classification - Simplified and Strengthened. CoRR abs/2106.04254 (2021) - [i27]Tung Mai, Anup B. Rao, Ryan A. Rossi, Saeed Seddighin:
Optimal Space and Time for Streaming Pattern Matching. CoRR abs/2107.04660 (2021) - [i26]Sridhar Mahadevan, Anup Rao, Georgios Theocharous, Jennifer Healey:
Multiscale Manifold Warping. CoRR abs/2109.09222 (2021) - [i25]Sudhanshu Chanpuriya, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Zhao Song, Cameron Musco:
An Interpretable Graph Generative Model with Heterophily. CoRR abs/2111.03030 (2021) - [i24]Aravind Reddy, Ryan A. Rossi, Zhao Song, Anup B. Rao, Tung Mai, Nedim Lipka, Gang Wu, Eunyee Koh, Nesreen K. Ahmed:
Online MAP Inference and Learning for Nonsymmetric Determinantal Point Processes. CoRR abs/2111.14674 (2021) - 2020
- [j1]David Durfee, John Peebles, Richard Peng, Anup B. Rao:
Determinant-Preserving Sparsification of SDDM Matrices. SIAM J. Comput. 49(4) (2020) - [c23]Aldo G. Carranza, Ryan A. Rossi, Anup Rao, Eunyee Koh:
Higher-order Clustering in Complex Heterogeneous Networks. KDD 2020: 25-35 - [c22]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. NeurIPS 2020 - [c21]Karan Aggarwal, Georgios Theocharous, Anup B. Rao:
Dynamic Clustering with Discrete Time Event Prediction. SIGIR 2020: 1501-1504 - [c20]Alireza Farhadi, Mohammad Taghi Hajiaghayi, Tung Mai, Anup Rao, Ryan A. Rossi:
Approximate Maximum Matching in Random Streams. SODA 2020: 1773-1785 - [c19]Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, Sungchul Kim, Anup Rao, Yasin Abbasi-Yadkori:
A Structural Graph Representation Learning Framework. WSDM 2020: 483-491 - [c18]Ryan A. Rossi, Anup Rao, Tung Mai, Nesreen K. Ahmed:
Fast and Accurate Estimation of Typed Graphlets. WWW (Companion Volume) 2020: 32-34 - [c17]Ryan A. Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed:
From Closing Triangles to Closing Higher-Order Motifs. WWW (Companion Volume) 2020: 42-43 - [c16]Gromit Yeuk-Yin Chan, Fan Du, Ryan A. Rossi, Anup B. Rao, Eunyee Koh, Cláudio T. Silva, Juliana Freire:
Real-Time Clustering for Large Sparse Online Visitor Data. WWW 2020: 1049-1059 - [i23]Aldo Pacchiano, My Phan, Yasin Abbasi-Yadkori, Anup Rao, Julian Zimmert, Tor Lattimore, Csaba Szepesvári:
Model Selection in Contextual Stochastic Bandit Problems. CoRR abs/2003.01704 (2020) - [i22]Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton:
Sample Efficient Graph-Based Optimization with Noisy Observations. CoRR abs/2006.02672 (2020) - [i21]Jiong Zhu, Ryan A. Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Nesreen K. Ahmed, Danai Koutra:
Graph Neural Networks with Heterophily. CoRR abs/2009.13566 (2020) - [i20]Ryan A. Rossi, Nesreen K. Ahmed, Aldo G. Carranza, David Arbour, Anup B. Rao, Sungchul Kim, Eunyee Koh:
Heterogeneous Graphlets. CoRR abs/2010.14058 (2020)
2010 – 2019
- 2019
- [c15]Thanh Tan Nguyen, Ali Shameli, Yasin Abbasi-Yadkori, Anup Rao, Branislav Kveton:
Sample Efficient Graph-Based Optimization with Noisy Observations. AISTATS 2019: 3333-3341 - [c14]John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao:
Graph Convolutional Networks with Motif-based Attention. CIKM 2019: 499-508 - [c13]Di Jin, Ryan A. Rossi, Eunyee Koh, Sungchul Kim, Anup Rao, Danai Koutra:
Latent Network Summarization: Bridging Network Embedding and Summarization. KDD 2019: 987-997 - [c12]Ekta Gujral, Evangelos E. Papalexakis, Georgios Theocharous, Anup B. Rao:
Hacd: Hierarchical Agglomerative Community Detection In Social Networks. MLSP 2019: 1-6 - [c11]Tung Mai, Anup Rao, Matt Kapilevich, Ryan A. Rossi, Yasin Abbasi-Yadkori, Ritwik Sinha:
On Densification for Minwise Hashing. UAI 2019: 831-840 - [c10]David Durfee, Yu Gao, Anup B. Rao, Sebastian Wild:
Efficient Second-Order Shape-Constrained Function Fitting. WADS 2019: 395-408 - [i19]Ryan A. Rossi, Nesreen K. Ahmed, Aldo G. Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh:
Heterogeneous Network Motifs. CoRR abs/1901.10026 (2019) - [i18]David Durfee, Yu Gao, Anup B. Rao, Sebastian Wild:
Efficient Second-Order Shape-Constrained Function Fitting. CoRR abs/1905.02149 (2019) - [i17]Ryan A. Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K. Ahmed, Gang Wu:
Higher-Order Ranking and Link Prediction: From Closing Triangles to Closing Higher-Order Motifs. CoRR abs/1906.05059 (2019) - [i16]Alireza Farhadi, MohammadTaghi Hajiaghayi, Tung Mai, Anup Rao, Ryan A. Rossi:
Approximate Maximum Matching in Random Streams. CoRR abs/1912.10497 (2019) - 2018
- [c9]Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford:
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. FOCS 2018: 898-909 - [i15]Sharan Vaswani, Branislav Kveton, Zheng Wen, Anup Rao, Mark Schmidt, Yasin Abbasi-Yadkori:
New Insights into Bootstrapping for Bandits. CoRR abs/1805.09793 (2018) - [i14]John Boaz Lee, Ryan A. Rossi, Xiangnan Kong, Sungchul Kim, Eunyee Koh, Anup Rao:
Higher-order Graph Convolutional Networks. CoRR abs/1809.07697 (2018) - [i13]Aldo G. Carranza, Ryan A. Rossi, Anup Rao, Eunyee Koh:
Higher-order Spectral Clustering for Heterogeneous Graphs. CoRR abs/1810.02959 (2018) - [i12]Di Jin, Ryan A. Rossi, Danai Koutra, Eunyee Koh, Sungchul Kim, Anup Rao:
Bridging Network Embedding and Graph Summarization. CoRR abs/1811.04461 (2018) - [i11]Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford:
Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations. CoRR abs/1811.10722 (2018) - 2017
- [c8]David Durfee, John Peebles, Richard Peng, Anup B. Rao:
Determinant-Preserving Sparsification of SDDM Matrices with Applications to Counting and Sampling Spanning Trees. FOCS 2017: 926-937 - [c7]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu:
Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs. STOC 2017: 410-419 - [c6]David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, Sushant Sachdeva:
Sampling random spanning trees faster than matrix multiplication. STOC 2017: 730-742 - [i10]Tung Mai, Richard Peng, Anup B. Rao, Vijay V. Vazirani:
Concave Flow on Small Depth Directed Networks. CoRR abs/1704.07791 (2017) - [i9]David Durfee, John Peebles, Richard Peng, Anup B. Rao:
Determinant-Preserving Sparsification of SDDM Matrices with Applications to Counting and Sampling Spanning Trees. CoRR abs/1705.00985 (2017) - [i8]Branislav Kveton, Csaba Szepesvári, Anup Rao, Zheng Wen, Yasin Abbasi-Yadkori, S. Muthukrishnan:
Stochastic Low-Rank Bandits. CoRR abs/1712.04644 (2017) - 2016
- [c5]Kevin A. Lai, Anup B. Rao, Santosh S. Vempala:
Agnostic Estimation of Mean and Covariance. FOCS 2016: 665-674 - [i7]Kevin A. Lai, Anup B. Rao, Santosh S. Vempala:
Agnostic Estimation of Mean and Covariance. CoRR abs/1604.06968 (2016) - [i6]Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford, Adrian Vladu:
Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs. CoRR abs/1611.00755 (2016) - [i5]David Durfee, Rasmus Kyng, John Peebles, Anup B. Rao, Sushant Sachdeva:
Sampling Random Spanning Trees Faster than Matrix Multiplication. CoRR abs/1611.07451 (2016) - 2015
- [c4]Peter Chin, Anup Rao, Van Vu:
Stochastic Block Model and Community Detection in Sparse Graphs: A spectral algorithm with optimal rate of recovery. COLT 2015: 391-423 - [c3]Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman:
Algorithms for Lipschitz Learning on Graphs. COLT 2015: 1190-1223 - [c2]Rasmus Kyng, Anup Rao, Sushant Sachdeva:
Fast, Provable Algorithms for Isotonic Regression in all L_p-norms. NIPS 2015: 2719-2727 - [i4]Peter Chin, Anup Rao, Van Vu:
Stochastic Block Model and Community Detection in the Sparse Graphs: A spectral algorithm with optimal rate of recovery. CoRR abs/1501.05021 (2015) - [i3]Rasmus Kyng, Anup Rao, Sushant Sachdeva, Daniel A. Spielman:
Algorithms for Lipschitz Learning on Graphs. CoRR abs/1505.00290 (2015) - [i2]Rasmus Kyng, Anup Rao, Sushant Sachdeva:
Fast, Provable Algorithms for Isotonic Regression in all ℓp-norms. CoRR abs/1507.00710 (2015) - 2014
- [c1]Michael B. Cohen, Rasmus Kyng, Gary L. Miller, Jakub W. Pachocki, Richard Peng, Anup B. Rao, Shen Chen Xu:
Solving SDD linear systems in nearly mlog1/2n time. STOC 2014: 343-352 - [i1]Michael B. Cohen, Rasmus Kyng, Jakub W. Pachocki, Richard Peng, Anup B. Rao:
Preconditioning in Expectation. CoRR abs/1401.6236 (2014)
Coauthor Index
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