Devavrat Shah

ORCID

Publication Activity (10 Years)

Years Active: 1999-2024
Publications (10 Years): 113

Top Venues

CoRR

NeurIPS

SIGMETRICS (Abstracts)

NIPS

Publications

Arnab Sarker, Ali Jadbabaie, Devavrat Shah
Unifying Epidemic Models With Mixtures. IEEE Trans. Signal Inf. Process. over Networks 10 (2024)
Jessy Xinyi Han, Andrew Miller, S. Craig Watkins, Christopher Winship, Fotini Christia, Devavrat Shah
A Causal Framework to Evaluate Racial Bias in Law Enforcement Systems. CoRR (2024)
Rohan Alur, Manish Raghavan, Devavrat Shah
Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction. CoRR (2024)
Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis L. Shung
Auditing for Human Expertise. NeurIPS (2023)
Rohan Alur, Loren Laine, Darrick K. Li, Manish Raghavan, Devavrat Shah, Dennis L. Shung
Auditing for Human Expertise. CoRR (2023)
Abdullah Alomar, Munther A. Dahleh, Sean Mann, Devavrat Shah
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise. NeurIPS (2023)
Sarah H. Cen, Aleksander Madry, Devavrat Shah
A User-Driven Framework for Regulating and Auditing Social Media. CoRR (2023)
Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah
Counterfactual Identifiability of Bijective Causal Models. CoRR (2023)
Devavrat Shah, Christina Lee Yu
Robust Max Entrywise Error Bounds for Tensor Estimation From Sparse Observations via Similarity-Based Collaborative Filtering. IEEE Trans. Inf. Theory 69 (5) (2023)
Abhin Shah, Devavrat Shah, Gregory W. Wornell
On Computationally Efficient Learning of Exponential Family Distributions. CoRR (2023)
Abdullah Alomar, Munther A. Dahleh, Sean Mann, Devavrat Shah
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise. CoRR (2023)
Ali Jadbabaie, Anuran Makur, Devavrat Shah
Federated Optimization of Smooth Loss Functions. IEEE Trans. Inf. Theory 69 (12) (2023)
Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dennis Shen
Causal Matrix Completion. COLT (2023)
Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah
Counterfactual Identifiability of Bijective Causal Models. ICML (2023)
Bowen Song, Marco Paolieri, Harper E. Stewart, Leana Golubchik, Jill L. McNitt-Gray, Vishal Misra, Devavrat Shah
Predicting Ground Reaction Force from Inertial Sensors. CoRR (2023)
Sean Mann, Charlotte Park, Devavrat Shah
Exploiting Observation Bias to Improve Matrix Completion. CoRR (2023)
Cindy Y. Zhang, Sarah Huiyi Cen, Devavrat Shah
Matrix Estimation for Individual Fairness. CoRR (2023)
Cindy Y. Zhang, Sarah Huiyi Cen, Devavrat Shah
Matrix Estimation for Individual Fairness. ICML (2023)
Romain Cosson, Ali Jadbabaie, Anuran Makur, Amirhossein Reisizadeh, Devavrat Shah
Gradient Descent with Low-Rank Objective Functions. CDC (2023)
Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah
CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation. NSDI (2023)
Raaz Dwivedi, Susan Murphy, Devavrat Shah
Counterfactual inference for sequential experimental design. CoRR (2022)
Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah
CausalSim: Toward a Causal Data-Driven Simulator for Network Protocols. CoRR (2022)
Chandler Squires, Dennis Shen, Anish Agarwal, Devavrat Shah, Caroline Uhler
Causal Imputation via Synthetic Interventions. CLeaR (2022)
Lavanya Marla, Lav R. Varshney, Devavrat Shah, Nirmal A. Prakash, Michael E. Gale
Short and Wide Network Paths. IEEE Trans. Netw. Sci. Eng. 9 (2) (2022)
Arnab Sarker, Ali Jadbabaie, Devavrat Shah
Unifying Epidemic Models with Mixtures. CoRR (2022)
Romain Cosson, Ali Jadbabaie, Anuran Makur, Amirhossein Reisizadeh, Devavrat Shah
Gradient Descent for Low-Rank Functions. CoRR (2022)
Christian Borgs, Jennifer T. Chayes, Devavrat Shah, Christina Lee Yu
Iterative Collaborative Filtering for Sparse Matrix Estimation. Oper. Res. 70 (6) (2022)
Anish Agarwal, Sarah Cen, Devavrat Shah, Christina Lee Yu
Network Synthetic Interventions: A Framework for Panel Data with Network Interference. CoRR (2022)
Sarah Huiyi Cen, Devavrat Shah
Regret, stability & fairness in matching markets with bandit learners. AISTATS (2022)
Horia Mania, Ali Jadbabaie, Devavrat Shah, Suvrit Sra
Time Varying Regression with Hidden Linear Dynamics. L4DC (2022)
Raaz Dwivedi, Katherine Tian, Sabina Tomkins, Predrag V. Klasnja, Susan Murphy, Devavrat Shah
Doubly robust nearest neighbors in factor models. CoRR (2022)
Ali Jadbabaie, Arnab Sarker, Devavrat Shah
Current Implicit Policies May Not Eradicate COVID-19. CoRR (2022)
Abhin Shah, Raaz Dwivedi, Devavrat Shah, Gregory W. Wornell
On counterfactual inference with unobserved confounding. CoRR (2022)
Ali Jadbabaie, Anuran Makur, Devavrat Shah
Federated Optimization of Smooth Loss Functions. CoRR (2022)
Devavrat Shah, Qiaomin Xie, Zhi Xu
Nonasymptotic Analysis of Monte Carlo Tree Search. Oper. Res. 70 (6) (2022)
Anish Agarwal, Abdullah Alomar, Devavrat Shah
On Multivariate Singular Spectrum Analysis and Its Variants. SIGMETRICS (Abstracts) (2022)
Romain Cosson, Devavrat Shah
Quantifying Variational Approximation for Log-Partition Function. COLT (2021)
Vincent D. Blondel, Kyomin Jung, Pushmeet Kohli, Devavrat Shah, Seungpil Won
Partition-Merge: Distributed Inference and Modularity Optimization. IEEE Access 9 (2021)
Arwa Alanqary, Abdullah Alomar, Devavrat Shah
Change Point Detection via Multivariate Singular Spectrum Analysis. NeurIPS (2021)
Meshal Alharbi, Saud Alghumayjan, Mansour Alsaleh, Devavrat Shah, Ahmad Alabdulkareem
Electricity Non-Technical Loss Detection: Enhanced Cost-Driven Approach Utilizing Synthetic Control. ISGT (2021)
Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dennis Shen
Causal Matrix Completion. CoRR (2021)
Sarah Huiyi Cen, Devavrat Shah
Regret, stability, and fairness in matching markets with bandit learners. CoRR (2021)
Sarah Huiyi Cen, Devavrat Shah
Regulating algorithmic filtering on social media. NeurIPS (2021)
Abhin Shah, Devavrat Shah, Gregory W. Wornell
On Learning Continuous Pairwise Markov Random Fields. AISTATS (2021)
Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators. NeurIPS (2021)
Abhin Shah, Devavrat Shah, Gregory W. Wornell
A Computationally Efficient Method for Learning Exponential Family Distributions. CoRR (2021)
Anish Agarwal, Abdullah Alomar, Varkey Alumootil, Devavrat Shah, Dennis Shen, Zhi Xu, Cindy Yang
PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators. CoRR (2021)
Romain Cosson, Devavrat Shah
Approximating the Log-Partition Function. CoRR (2021)
Michael Fleder, Devavrat Shah
I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem. SIGMETRICS (Abstracts) (2021)
Abhin Shah, Devavrat Shah, Gregory W. Wornell
A Computationally Efficient Method for Learning Exponential Family Distributions. NeurIPS (2021)
Bader Alaskar, Abdullah Alhadlaq, Meshal Alharbi, Saud Alghumayjan, Ahmad Alabdulkareem, Mansour Alsaleh, Devavrat Shah
Next-day Electricity Demand Forecast: A New Ensemble Recommendation System Using Peak and Valley. ISGT (2021)
Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu
Editorial. IEEE J. Sel. Areas Inf. Theory 1 (3) (2020)
Anish Agarwal, Abdullah Alomar, Romain Cosson, Devavrat Shah, Dennis Shen
Synthetic Interventions. CoRR (2020)
Michael Fleder, Devavrat Shah
I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem. Proc. ACM Meas. Anal. Comput. Syst. 4 (3) (2020)
Anish Agarwal, Abdullah Alomar, Devavrat Shah
tspDB: Time Series Predict DB. NeurIPS (Competition and Demos) (2020)
Abhin Shah, Devavrat Shah, Gregory W. Wornell
On Learning Continuous Pairwise Markov Random Fields. CoRR (2020)
Sarah Huiyi Cen, Devavrat Shah
Regulating algorithmic filtering on social media. CoRR (2020)
Anish Agarwal, Abdullah Alomar, Devavrat Shah
On Multivariate Singular Spectrum Analysis. CoRR (2020)
Ali Jadbabaie, Anuran Makur, Devavrat Shah
Estimation of Skill Distribution from a Tournament. NeurIPS (2020)
Anish Agarwal, Abdullah Alomar, Arnab Sarker, Devavrat Shah, Dennis Shen, Cindy Yang
Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave? CoRR (2020)
Yihua Li, Devavrat Shah, Dogyoon Song, Christina Lee Yu
Nearest Neighbors for Matrix Estimation Interpreted as Blind Regression for Latent Variable Model. IEEE Trans. Inf. Theory 66 (3) (2020)
Anish Agarwal, Devavrat Shah, Dennis Shen
On Principal Component Regression in a High-Dimensional Error-in-Variables Setting. CoRR (2020)
Devavrat Shah, Qiaomin Xie, Zhi Xu
Stable Reinforcement Learning with Unbounded State Space. L4DC (2020)
Michael Fleder, Devavrat Shah
Forecasting with Alternative Data. SIGMETRICS (Abstracts) (2020)
Devavrat Shah, Varun Somani, Qiaomin Xie, Zhi Xu
On Reinforcement Learning for Turn-based Zero-sum Markov Games. CoRR (2020)
Devavrat Shah, Qiaomin Xie, Zhi Xu
Stable Reinforcement Learning with Unbounded State Space. CoRR (2020)
Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. NeurIPS (2020)
Asuman E. Ozdaglar, Devavrat Shah, Christina Lee Yu
Asynchronous Approximation of a Single Component of the Solution to a Linear System. IEEE Trans. Netw. Sci. Eng. 7 (3) (2020)
Ali Jadbabaie, Anuran Makur, Devavrat Shah
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression. CoRR (2020)
Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. CoRR (2020)
Devavrat Shah, Varun Somani, Qiaomin Xie, Zhi Xu
On Reinforcement Learning for Turn-based Zero-sum Markov Games. FODS (2020)
Devavrat Shah, Qiaomin Xie, Zhi Xu
Non-Asymptotic Analysis of Monte Carlo Tree Search. SIGMETRICS (Abstracts) (2020)
Ali Jadbabaie, Anuran Makur, Devavrat Shah
Estimation of Skill Distributions. CoRR (2020)
Devavrat Shah, Qiaomin Xie, Zhi Xu
On Reinforcement Learning Using Monte Carlo Tree Search with Supervised Learning: Non-Asymptotic Analysis. CoRR (2019)
Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song
Model Agnostic High-Dimensional Error-in-Variable Regression. CoRR (2019)
Lavanya Marla, Lav R. Varshney, Devavrat Shah, Nirmal A. Prakash, Michael E. Gale
Short and Wide Network Paths. CoRR (2019)
Abdullah Alomar, Muhammad J. Amjad, Robert Lindland, Devavrat Shah
Time Series Predict DB. CoRR (2019)
Michael Fleder, Devavrat Shah
Forecasting with Alternative Data. Proc. ACM Meas. Anal. Comput. Syst. 3 (3) (2019)
Devavrat Shah, Christina Lee Yu
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation. ISIT (2019)
Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song
On Robustness of Principal Component Regression. NeurIPS (2019)
Muhammad Jehangir Amjad, Vishal Misra, Devavrat Shah, Dennis Shen
mRSC: Multidimensional Robust Synthetic Control. SIGMETRICS (Abstracts) (2019)
Kyomin Jung, Yingdong Lu, Devavrat Shah, Mayank Sharma, Mark S. Squillante
Revisiting Stochastic Loss Networks: Structures and Approximations. Math. Oper. Res. 44 (3) (2019)
Devavrat Shah, Christina Lee Yu
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation. CoRR (2019)
Devavrat Shah, Christina Lee Yu
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation. Allerton (2019)
Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
Model Agnostic Time Series Analysis via Matrix Estimation. SIGMETRICS (Abstracts) (2019)
Muhammad J. Amjad, Vishal Misra, Devavrat Shah, Dennis Shen
mRSC: Multi-dimensional Robust Synthetic Control. Proc. ACM Meas. Anal. Comput. Syst. 3 (2) (2019)
Linqi Song, Christina Fragouli, Devavrat Shah
Interactions Between Learning and Broadcasting in Wireless Recommendation Systems. ISIT (2019)
Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dylan Sleeper, Andrew Tsai, Madeline Wong
Zorro: A Model Agnostic System to Price Consumer Data. CoRR (2019)
Devavrat Shah, Sai Burle, Vishal Doshi, Ying-zong Huang, Balaji Rengarajan
Prediction Query Language. Allerton (2018)
George H. Chen, Devavrat Shah
Explaining the Success of Nearest Neighbor Methods in Prediction. Found. Trends Mach. Learn. 10 (5-6) (2018)
Guy Bresler, David Gamarnik, Devavrat Shah
Learning Graphical Models From the Glauber Dynamics. IEEE Trans. Inf. Theory 64 (6) (2018)
Devavrat Shah, Qiaomin Xie
Q-learning with Nearest Neighbors. CoRR (2018)
Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
Time Series Analysis via Matrix Estimation. CoRR (2018)
Muhammad J. Amjad, Devavrat Shah
Censored Demand Estimation in Retail. SIGMETRICS (Abstracts) (2018)
Linqi Song, Christina Fragouli, Devavrat Shah
Regret vs. Bandwidth Trade-off for Recommendation Systems. CoRR (2018)
Devavrat Shah, Dogyoon Song
Learning Mixture Model with Missing Values and its Application to Rankings. CoRR (2018)
Linqi Song, Christina Fragouli, Devavrat Shah
Recommender Systems over Wireless: Challenges and Opportunities. ITW (2018)
Devavrat Shah, Christina E. Lee
Reducing Crowdsourcing to Graphon Estimation, Statistically. AISTATS (2018)
Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen
Model Agnostic Time Series Analysis via Matrix Estimation. Proc. ACM Meas. Anal. Comput. Syst. 2 (3) (2018)
Devavrat Shah, Qiaomin Xie
Q-learning with Nearest Neighbors. NeurIPS (2018)

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Publication Activity (10 Years)

Top Topics

Top Venues

Devavrat Shah