Online Convex Optimization Using Predictions. - Niangjun Chen; Anish Agarwal; Adam Wierman; Siddharth Barman; Lachlan L. H. Andrew (SIGMETRICS, 2015)

Time Series Analysis via Matrix Estimation. - Anish Agarwal; Muhammad Jehangir Amjad; Devavrat Shah; Dennis Shen (CoRR, 2018)

Model Agnostic Time Series Analysis via Matrix Estimation. - Anish Agarwal; Muhammad Jehangir Amjad; Devavrat Shah; Dennis Shen (Proc. ACM Meas. Anal. Comput. Syst., 2018)

A Marketplace for Data: An Algorithmic Solution. - Anish Agarwal; Munther A. Dahleh; Tuhin Sarkar (EC, 2019)

On Robustness of Principal Component Regression. - Anish Agarwal; Devavrat Shah; Dennis Shen; Dogyoon Song (NeurIPS, 2019)

Model Agnostic High-Dimensional Error-in-Variable Regression. - Anish Agarwal; Devavrat Shah; Dennis Shen; Dogyoon Song (CoRR, 2019)

Zorro: A Model Agnostic System to Price Consumer Data. - Anish Agarwal; Munther A. Dahleh; Devavrat Shah; Dylan Sleeper; Andrew Tsai; Madeline Wong (CoRR, 2019)

Single Image Super-Resolution using Residual Channel Attention Network. - Hritam Basak; Rohit Kundu; Anish Agarwal; Shreya Giri (ICIIS, 2020)

tspDB: Time Series Predict DB. - Anish Agarwal; Abdullah Alomar; Devavrat Shah (NeurIPS (Competition and Demos), 2020)

Augmented Q Imitation Learning (AQIL). - Xiao-Lei Zhang; Anish Agarwal (CoRR, 2020)

Two Burning Questions on COVID-19: Did shutting down the economy help? Can we (partially) reopen the economy without risking the second wave? - Anish Agarwal; Abdullah Alomar; Arnab Sarker; Devavrat Shah; Dennis Shen; Cindy Yang (CoRR, 2020)

Synthetic Interventions. - Anish Agarwal; Abdullah Alomar; Romain Cosson; Devavrat Shah; Dennis Shen (CoRR, 2020)

On Multivariate Singular Spectrum Analysis. - Anish Agarwal; Abdullah Alomar; Devavrat Shah (CoRR, 2020)

On Principal Component Regression in a High-Dimensional Error-in-Variables Setting. - Anish Agarwal; Devavrat Shah; Dennis Shen (CoRR, 2020)

On the Inefficiency of Forward Markets in Leader-Follower Competition. - Desmond W. H. Cai; Anish Agarwal; Adam Wierman (Oper. Res., 2020)

PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators. - Anish Agarwal; Abdullah Alomar; Varkey Alumootil; Devavrat Shah; Dennis Shen; Zhi Xu 001; Cindy Yang (NeurIPS, 2021)

Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy. - Anish Agarwal; Rahul Singh (CoRR, 2021)

Causal Imputation via Synthetic Interventions. - Chandler Squires; Dennis Shen; Anish Agarwal; Devavrat Shah; Caroline Uhler (CLeaR, 2022)

On Multivariate Singular Spectrum Analysis and Its Variants. - Anish Agarwal; Abdullah Alomar; Devavrat Shah (SIGMETRICS (Abstracts), 2022)

CausalSim: Toward a Causal Data-Driven Simulator for Network Protocols. - Abdullah Alomar; Pouya Hamadanian; Arash Nasr-Esfahany; Anish Agarwal; Mohammad Alizadeh; Devavrat Shah (CoRR, 2022)

Synthetic Blip Effects: Generalizing Synthetic Controls for the Dynamic Treatment Regime. - Anish Agarwal; Vasilis Syrgkanis (CoRR, 2022)

Network Synthetic Interventions: A Framework for Panel Data with Network Interference. - Anish Agarwal; Sarah H. Cen; Devavrat Shah; Christina Lee Yu (CoRR, 2022)

Causal Inference for Social and Engineering Systems - Anish Agarwal (2022)

Causal Inference for Social and Engineering Systems. - Anish Agarwal (SIGMETRICS Perform. Evaluation Rev., 2022)

Causal Matrix Completion. - Anish Agarwal; Munther A. Dahleh; Devavrat Shah; Dennis Shen (COLT, 2023)

Synthetic Combinations: A Causal Inference Framework for Combinatorial Interventions. - Abhineet Agarwal; Anish Agarwal; Suhas Vijaykumar (NeurIPS, 2023)

Adaptive Principal Component Regression with Applications to Panel Data. - Anish Agarwal; Keegan Harris; Justin Whitehouse; Zhiwei Steven Wu (NeurIPS, 2023)

CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation. - Abdullah Alomar; Pouya Hamadanian; Arash Nasr-Esfahany; Anish Agarwal; Mohammad Alizadeh; Devavrat Shah (NSDI, 2023)

Estimating the Value of Evidence-Based Decision Making. - Alberto Abadie; Anish Agarwal; Guido Imbens; Siwei Jia; James McQueen; Serguei Stepaniants (CoRR, 2023)

Incentive-Aware Synthetic Control: Accurate Counterfactual Estimation via Incentivized Exploration. - Dung Daniel T. Ngo; Keegan Harris; Anish Agarwal; Vasilis Syrgkanis; Zhiwei Steven Wu (CoRR, 2023)

Mutli-Armed Bandits with Network Interference. - Abhineet Agarwal; Anish Agarwal; Lorenzo Masoero; Justin Whitehouse (NeurIPS, 2024)

Strategyproof Decision-Making in Panel Data Settings and Beyond. - Keegan Harris; Anish Agarwal; Chara Podimata; Zhiwei Steven Wu (SIGMETRICS/Performance (Abstracts), 2024)

Doubly Robust Inference in Causal Latent Factor Models. - Alberto Abadie; Anish Agarwal; Raaz Dwivedi; Abhin Shah (CoRR, 2024)

Multi-Armed Bandits with Network Interference. - Abhineet Agarwal; Anish Agarwal; Lorenzo Masoero; Justin Whitehouse (CoRR, 2024)

Distributional Matrix Completion via Nearest Neighbors in the Wasserstein Space. - Jacob Feitelberg; Kyuseong Choi; Anish Agarwal; Raaz Dwivedi (CoRR, 2024)

Learning Counterfactual Distributions via Kernel Nearest Neighbors. - Kyuseong Choi; Jacob Feitelberg; Anish Agarwal; Raaz Dwivedi (CoRR, 2024)

Towards data auctions with externalities. - Anish Agarwal; Munther A. Dahleh; Thibaut Horel; Maryann Rui (Games Econ. Behav., 2024)

A Causal Inference Framework for Data Rich Environments. - Alberto Abadie; Anish Agarwal; Devavrat Shah (CoRR, 2025)

N2: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion. - Caleb Chin; Aashish Khubchandani; Harshvardhan Maskara; Kyuseong Choi; Jacob Feitelberg; Albert Gong; Manit Paul; Tathagata Sadhukhan; Anish Agarwal; Raaz Dwivedi (CoRR, 2025)