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ORCIDDevavrat Shah
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- affiliation: MIT, Cambridge, USA
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2020 – today
- 2023
[i85]Cindy Y. Zhang, Sarah Huiyi Cen, Devavrat Shah:
Matrix Estimation for Individual Fairness. CoRR abs/2302.02096 (2023)- [i84]Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah:
Counterfactual Identifiability of Bijective Causal Models. CoRR abs/2302.02228 (2023) - 2022
- [j63]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): 524-537 (2022) - [c145]Sarah Huiyi Cen, Devavrat Shah:
Regret, stability & fairness in matching markets with bandit learners. AISTATS 2022: 8938-8968 - [c144]Chandler Squires, Dennis Shen, Anish Agarwal, Devavrat Shah, Caroline Uhler:
Causal Imputation via Synthetic Interventions. CLeaR 2022: 688-711 - [c143]Horia Mania, Ali Jadbabaie, Devavrat Shah, Suvrit Sra:
Time Varying Regression with Hidden Linear Dynamics. L4DC 2022: 858-869 - [c142]Anish Agarwal, Abdullah Alomar, Devavrat Shah:
On Multivariate Singular Spectrum Analysis and Its Variants. SIGMETRICS (Abstracts) 2022: 79-80 - [i83]Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany, Anish Agarwal, Mohammad Alizadeh, Devavrat Shah:
CausalSim: Toward a Causal Data-Driven Simulator for Network Protocols. CoRR abs/2201.01811 (2022) - [i82]Ali Jadbabaie, Anuran Makur, Devavrat Shah:
Federated Optimization of Smooth Loss Functions. CoRR abs/2201.01954 (2022) - [i81]Arnab Sarker, Ali Jadbabaie, Devavrat Shah:
Unifying Epidemic Models with Mixtures. CoRR abs/2201.04960 (2022) - [i80]Raaz Dwivedi, Susan Murphy, Devavrat Shah:
Counterfactual inference for sequential experimental design. CoRR abs/2202.06891 (2022) - [i79]Ali Jadbabaie, Arnab Sarker, Devavrat Shah:
Current Implicit Policies May Not Eradicate COVID-19. CoRR abs/2203.15916 (2022) - [i78]Romain Cosson, Ali Jadbabaie, Anuran Makur, Amirhossein Reisizadeh, Devavrat Shah:
Gradient Descent for Low-Rank Functions. CoRR abs/2206.08257 (2022) - [i77]Anish Agarwal, Sarah Cen, Devavrat Shah, Christina Lee Yu:
Network Synthetic Interventions: A Framework for Panel Data with Network Interference. CoRR abs/2210.11355 (2022) - [i76]Abhin Shah, Raaz Dwivedi, Devavrat Shah, Gregory W. Wornell:
On counterfactual inference with unobserved confounding. CoRR abs/2211.08209 (2022) - [i75]Raaz Dwivedi, Katherine Tian, Sabina Tomkins, Predrag V. Klasnja, Susan Murphy, Devavrat Shah:
Doubly robust nearest neighbors in factor models. CoRR abs/2211.14297 (2022) - 2021
- [j62]Vincent D. Blondel, Kyomin Jung, Pushmeet Kohli, Devavrat Shah, Seungpil Won:
Partition-Merge: Distributed Inference and Modularity Optimization. IEEE Access 9: 54032-54055 (2021) - [c141]Abhin Shah, Devavrat Shah, Gregory W. Wornell:
On Learning Continuous Pairwise Markov Random Fields. AISTATS 2021: 1153-1161 - [c140]Romain Cosson, Devavrat Shah:
Quantifying Variational Approximation for Log-Partition Function. COLT 2021: 1330-1357 - [c139]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: 1-5 - [c138]Meshal Alharbi, Saud Alghumayjan, Mansour Alsaleh, Devavrat Shah, Ahmad Alabdulkareem:
Electricity Non-Technical Loss Detection: Enhanced Cost-Driven Approach Utilizing Synthetic Control. ISGT 2021: 1-5 - [c137]Sarah Huiyi Cen, Devavrat Shah:
Regulating algorithmic filtering on social media. NeurIPS 2021: 6997-7011 - [c136]Abhin Shah, Devavrat Shah, Gregory W. Wornell:
A Computationally Efficient Method for Learning Exponential Family Distributions. NeurIPS 2021: 15841-15854 - [c135]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: 18564-18576 - [c134]Arwa Alanqary, Abdullah Alomar, Devavrat Shah:
Change Point Detection via Multivariate Singular Spectrum Analysis. NeurIPS 2021: 23218-23230 - [c133]Michael Fleder, Devavrat Shah:
I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem. SIGMETRICS (Abstracts) 2021: 59-60 - [i74]Sarah Huiyi Cen, Devavrat Shah:
Regret, stability, and fairness in matching markets with bandit learners. CoRR abs/2102.06246 (2021) - [i73]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 abs/2102.06961 (2021) - [i72]Romain Cosson, Devavrat Shah:
Approximating the Log-Partition Function. CoRR abs/2102.10196 (2021) - [i71]Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dennis Shen:
Causal Matrix Completion. CoRR abs/2109.15154 (2021) - [i70]Abhin Shah, Devavrat Shah, Gregory W. Wornell:
A Computationally Efficient Method for Learning Exponential Family Distributions. CoRR abs/2110.15397 (2021) - 2020
- [j61]Devavrat Shah, Guy Bresler, John C. Duchi, Po-Ling Loh, Yihong Wu, Christina Lee Yu:
Editorial. IEEE J. Sel. Areas Inf. Theory 1(3): 612 (2020) - [j60]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): 47:1-47:17 (2020) - [j59]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): 1760-1784 (2020) - [j58]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): 975-986 (2020) - [c132]Devavrat Shah, Varun Somani, Qiaomin Xie, Zhi Xu:
On Reinforcement Learning for Turn-based Zero-sum Markov Games. FODS 2020: 139-148 - [c131]Devavrat Shah, Qiaomin Xie, Zhi Xu:
Stable Reinforcement Learning with Unbounded State Space. L4DC 2020: 581 - [c130]Anish Agarwal, Abdullah Alomar, Devavrat Shah:
tspDB: Time Series Predict DB. NeurIPS (Competition and Demos) 2020: 27-56 - [c129]Ali Jadbabaie, Anuran Makur, Devavrat Shah:
Estimation of Skill Distribution from a Tournament. NeurIPS 2020 - [c128]Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang:
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. NeurIPS 2020 - [c127]Michael Fleder, Devavrat Shah:
Forecasting with Alternative Data. SIGMETRICS (Abstracts) 2020: 23-24 - [c126]Devavrat Shah, Qiaomin Xie, Zhi Xu:
Non-Asymptotic Analysis of Monte Carlo Tree Search. SIGMETRICS (Abstracts) 2020: 31-32 - [i69]Devavrat Shah, Varun Somani, Qiaomin Xie, Zhi Xu:
On Reinforcement Learning for Turn-based Zero-sum Markov Games. CoRR abs/2002.10620 (2020) - [i68]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 abs/2005.00072 (2020) - [i67]Devavrat Shah, Qiaomin Xie, Zhi Xu:
Stable Reinforcement Learning with Unbounded State Space. CoRR abs/2006.04353 (2020) - [i66]Devavrat Shah, Dogyoon Song, Zhi Xu, Yuzhe Yang:
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation. CoRR abs/2006.06135 (2020) - [i65]Anish Agarwal, Abdullah Alomar, Romain Cosson, Devavrat Shah, Dennis Shen:
Synthetic Interventions. CoRR abs/2006.07691 (2020) - [i64]Ali Jadbabaie, Anuran Makur, Devavrat Shah:
Estimation of Skill Distributions. CoRR abs/2006.08189 (2020) - [i63]Sarah Huiyi Cen, Devavrat Shah:
Regulating algorithmic filtering on social media. CoRR abs/2006.09647 (2020) - [i62]Anish Agarwal, Abdullah Alomar, Devavrat Shah:
On Multivariate Singular Spectrum Analysis. CoRR abs/2006.13448 (2020) - [i61]Anish Agarwal, Devavrat Shah, Dennis Shen:
On Principal Component Regression in a High-Dimensional Error-in-Variables Setting. CoRR abs/2010.14449 (2020) - [i60]Abhin Shah, Devavrat Shah, Gregory W. Wornell:
On Learning Continuous Pairwise Markov Random Fields. CoRR abs/2010.15031 (2020) - [i59]Ali Jadbabaie, Anuran Makur, Devavrat Shah:
Gradient-Based Empirical Risk Minimization using Local Polynomial Regression. CoRR abs/2011.02522 (2020)
2010 – 2019
- 2019
- [j57]Kyomin Jung, Yingdong Lu, Devavrat Shah, Mayank Sharma, Mark S. Squillante:
Revisiting Stochastic Loss Networks: Structures and Approximations. Math. Oper. Res. 44(3): 890-918 (2019) - [j56]Muhammad J. Amjad, Vishal Misra, Devavrat Shah, Dennis Shen:
mRSC: Multi-dimensional Robust Synthetic Control. Proc. ACM Meas. Anal. Comput. Syst. 3(2): 37:1-37:27 (2019) - [j55]Michael Fleder, Devavrat Shah:
Forecasting with Alternative Data. Proc. ACM Meas. Anal. Comput. Syst. 3(3): 46:1-46:29 (2019) - [c125]Devavrat Shah, Christina Lee Yu:
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation. Allerton 2019: 904-909 - [c124]Devavrat Shah, Christina Lee Yu:
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation. ISIT 2019: 41-45 - [c123]Linqi Song, Christina Fragouli, Devavrat Shah:
Interactions Between Learning and Broadcasting in Wireless Recommendation Systems. ISIT 2019: 2549-2553 - [c122]Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song:
On Robustness of Principal Component Regression. NeurIPS 2019: 9889-9900 - [c121]Muhammad Jehangir Amjad, Vishal Misra, Devavrat Shah, Dennis Shen:
mRSC: Multidimensional Robust Synthetic Control. SIGMETRICS (Abstracts) 2019: 55-56 - [c120]Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen:
Model Agnostic Time Series Analysis via Matrix Estimation. SIGMETRICS (Abstracts) 2019: 85-86 - [i58]Devavrat Shah, Qiaomin Xie, Zhi Xu:
On Reinforcement Learning Using Monte Carlo Tree Search with Supervised Learning: Non-Asymptotic Analysis. CoRR abs/1902.05213 (2019) - [i57]Anish Agarwal, Devavrat Shah, Dennis Shen, Dogyoon Song:
Model Agnostic High-Dimensional Error-in-Variable Regression. CoRR abs/1902.10920 (2019) - [i56]Abdullah Alomar, Muhammad J. Amjad, Robert Lindland, Devavrat Shah:
Time Series Predict DB. CoRR abs/1903.07097 (2019) - [i55]Anish Agarwal, Munther A. Dahleh, Devavrat Shah, Dylan Sleeper, Andrew Tsai, Madeline Wong:
Zorro: A Model Agnostic System to Price Consumer Data. CoRR abs/1906.02420 (2019) - [i54]Devavrat Shah, Christina Lee Yu:
Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation. CoRR abs/1908.01241 (2019) - [i53]Lavanya Marla, Lav R. Varshney, Devavrat Shah, Nirmal A. Prakash, Michael E. Gale:
Short and Wide Network Paths. CoRR abs/1911.00344 (2019) - 2018
- [j54]George H. Chen, Devavrat Shah:
Explaining the Success of Nearest Neighbor Methods in Prediction. Found. Trends Mach. Learn. 10(5-6): 337-588 (2018) - [j53]Muhammad J. Amjad, Devavrat Shah, Dennis Shen:
Robust Synthetic Control. J. Mach. Learn. Res. 19: 22:1-22:51 (2018) - [j52]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): 40:1-40:39 (2018) - [j51]Guy Bresler, David Gamarnik, Devavrat Shah:
Learning Graphical Models From the Glauber Dynamics. IEEE Trans. Inf. Theory 64(6): 4072-4080 (2018) - [c119]Devavrat Shah, Christina E. Lee:
Reducing Crowdsourcing to Graphon Estimation, Statistically. AISTATS 2018: 1741-1750 - [c118]Devavrat Shah, Sai Burle, Vishal Doshi, Ying-zong Huang, Balaji Rengarajan:
Prediction Query Language. Allerton 2018: 611-616 - [c117]Linqi Song, Christina Fragouli, Devavrat Shah:
Recommender Systems over Wireless: Challenges and Opportunities. ITW 2018: 1-5 - [c116]Devavrat Shah, Qiaomin Xie:
Q-learning with Nearest Neighbors. NeurIPS 2018: 3115-3125 - [c115]Muhammad J. Amjad, Devavrat Shah:
Censored Demand Estimation in Retail. SIGMETRICS (Abstracts) 2018: 17-19 - [i52]Devavrat Shah, Qiaomin Xie:
Q-learning with Nearest Neighbors. CoRR abs/1802.03900 (2018) - [i51]Anish Agarwal, Muhammad Jehangir Amjad, Devavrat Shah, Dennis Shen:
Time Series Analysis via Matrix Estimation. CoRR abs/1802.09064 (2018) - [i50]Linqi Song, Christina Fragouli, Devavrat Shah:
Regret vs. Bandwidth Trade-off for Recommendation Systems. CoRR abs/1810.06313 (2018) - [i49]Devavrat Shah, Dogyoon Song:
Learning Mixture Model with Missing Values and its Application to Rankings. CoRR abs/1812.11917 (2018) - 2017
- [j50]Sahand Negahban, Sewoong Oh, Devavrat Shah:
Rank Centrality: Ranking from Pairwise Comparisons. Oper. Res. 65(1): 266-287 (2017) - [j49]Muhammad J. Amjad, Devavrat Shah:
Censored Demand Estimation in Retail. Proc. ACM Meas. Anal. Comput. Syst. 1(2): 31:1-31:28 (2017) - [j48]Jay Kumar Sundararajan, Devavrat Shah, Muriel Médard, Parastoo Sadeghi:
Feedback-Based Online Network Coding. IEEE Trans. Inf. Theory 63(10): 6628-6649 (2017) - [c114]Devavrat Shah:
Matrix Estimation, Latent Variable Model and Collaborative Filtering. FSTTCS 2017: 4:1-4:8 - [c113]Christian Borgs, Jennifer T. Chayes, Christina E. Lee, Devavrat Shah:
Thy Friend is My Friend: Iterative Collaborative Filtering for Sparse Matrix Estimation. NIPS 2017: 4715-4726 - [c112]Jonathan Perry, Hari Balakrishnan, Devavrat Shah:
Flowtune: Flowlet Control for Datacenter Networks. NSDI 2017: 421-435 - [i48]Devavrat Shah, Qiaomin Xie:
Centralized Congestion Control and Scheduling in a Datacenter. CoRR abs/1710.02548 (2017) - 2016
- [j47]Devavrat Shah, Tauhid Zaman:
Finding Rumor Sources on Random Trees. Oper. Res. 64(3): 736-755 (2016) - [c111]Devavrat Shah:
Compute Choice. ICALP 2016: 1:1-1:1 - [c110]Muhammad J. Amjad, Devavrat Shah:
Trading Bitcoin and Online Time Series Prediction. NIPS Time Series Workshop 2016: 1-15 - [c109]Dogyoon Song, Christina E. Lee, Yihua Li, Devavrat Shah:
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering. NIPS 2016: 2155-2163 - [c108]Guy Bresler, Devavrat Shah, Luis Filipe Voloch:
Collaborative Filtering with Low Regret. SIGMETRICS 2016: 207-220 - 2015
- [c107]George H. Chen, Devavrat Shah, Polina Golland:
A Latent Source Model for Patch-Based Image Segmentation. MICCAI (3) 2015: 140-148 - [e1]Bill Lin, Jun (Jim) Xu, Sudipta Sengupta, Devavrat Shah:
Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, Portland, OR, USA, June 15-19, 2015. ACM 2015, ISBN 978-1-4503-3486-0 [contents] - [i47]Guy Bresler, Devavrat Shah, Luis Filipe Voloch:
Collaborative Filtering with Low Regret. CoRR abs/1507.05371 (2015) - [i46]George H. Chen, Devavrat Shah, Polina Golland:
A Latent Source Model for Patch-Based Image Segmentation. CoRR abs/1510.01648 (2015) - 2014
- [j46]David R. Karger, Sewoong Oh, Devavrat Shah:
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems. Oper. Res. 62(1): 1-24 (2014) - [j45]H. Vincent Poor, Kwang-Cheng Chen, Vikram Krishnamurthy, Devavrat Shah, Patrick J. Wolfe:
Introduction to the Issue on Signal Processing for Social Networks [Guest editorial]. IEEE J. Sel. Top. Signal Process. 8(4): 511-513 (2014) - [c106]Devavrat Shah, Kang Zhang:
Bayesian regression and Bitcoin. Allerton 2014: 409-414 - [c105]Guy Bresler, David Gamarnik, Devavrat Shah:
Learning graphical models from the Glauber dynamics. Allerton 2014: 1148-1155 - [c104]Sewoong Oh, Devavrat Shah:
Learning Mixed Multinomial Logit Model from Ordinal Data. NIPS 2014: 595-603 - [c103]Guy Bresler, David Gamarnik, Devavrat Shah:
Hardness of parameter estimation in graphical models. NIPS 2014: 1062-1070 - [c102]Guy Bresler, David Gamarnik, Devavrat Shah:
Structure learning of antiferromagnetic Ising models. NIPS 2014: 2852-2860 - [c101]Guy Bresler, George H. Chen, Devavrat Shah:
A Latent Source Model for Online Collaborative Filtering. NIPS 2014: 3347-3355 - [c100]Jonathan Perry, Amy Ousterhout, Hari Balakrishnan, Devavrat Shah, Hans Fugal:
Fastpass: a centralized "zero-queue" datacenter network. SIGCOMM 2014: 307-318 - [c99]Ammar Ammar, Sewoong Oh, Devavrat Shah, Luis Filipe Voloch:
What's your choice?: learning the mixed multi-nomial. SIGMETRICS 2014: 565-566 - [i45]Devavrat Shah, John N. Tsitsiklis, Yuan Zhong:
On Queue-Size Scaling for Input-Queued Switches. CoRR abs/1405.4764 (2014) - [i44]Guy Bresler, David Gamarnik, Devavrat Shah:
Hardness of parameter estimation in graphical models. CoRR abs/1409.3836 (2014) - [i43]Angélique Dremeau, Christophe Schülke, Yingying Xu, Devavrat Shah:
Statistical inference with probabilistic graphical models. CoRR abs/1409.4928 (2014) - [i42]Devavrat Shah, Kang Zhang:
Bayesian regression and Bitcoin. CoRR abs/1410.1231 (2014) - [i41]Guy Bresler, David Gamarnik, Devavrat Shah:
Learning graphical models from the Glauber dynamics. CoRR abs/1410.7659 (2014) - [i40]Christina E. Lee, Asuman E. Ozdaglar, Devavrat Shah:
Solving Systems of Linear Equations: Locally and Asynchronously. CoRR abs/1411.2647 (2014) - [i39]Guy Bresler, George H. Chen, Devavrat Shah:
A Latent Source Model for Online Collaborative Filtering. CoRR abs/1411.6591 (2014) - [i38]Guy Bresler, David Gamarnik, Devavrat Shah:
Structure learning of antiferromagnetic Ising models. CoRR abs/1412.1443 (2014) - 2013
- [j44]Vivek F. Farias, Srikanth Jagabathula, Devavrat Shah:
A Nonparametric Approach to Modeling Choice with Limited Data. Manag. Sci. 59(2): 305-322 (2013) - [j43]Masood Qazi, Mehul Tikekar, Lara Dolecek, Devavrat Shah, Anantha P. Chandrakasan:
Technique for Efficient Evaluation of SRAM Timing Failure. IEEE Trans. Very Large Scale Integr. Syst. 21(8): 1558-1562 (2013) - [c98]George H. Chen, Stanislav Nikolov, Devavrat Shah:
A Latent Source Model for Nonparametric Time Series Classification. NIPS 2013: 1088-1096 - [c97]Christina E. Lee, Asuman E. Ozdaglar, Devavrat Shah:
Computing the Stationary Distribution Locally. NIPS 2013: 1376-1384 - [c96]David R. Karger, Sewoong Oh, Devavrat Shah:
Efficient crowdsourcing for multi-class labeling. SIGMETRICS 2013: 81-92 - [i37]George H. Chen, Stanislav Nikolov, Devavrat Shah:
A Latent Source Model for Online Time Series Classification. CoRR abs/1302.3639 (2013) - [i36]Vincent D. Blondel, Kyomin Jung, Pushmeet Kohli, Devavrat Shah:
Partition-Merge: Distributed Inference and Modularity Optimization. CoRR abs/1309.6129 (2013) - [i35]Christina E. Lee, Asuman E. Ozdaglar, Devavrat Shah:
Computing the Stationary Distribution Locally. CoRR abs/1312.1986 (2013) - 2012
- [j42]David Gamarnik, Devavrat Shah, Yehua Wei:
Belief Propagation for Min-Cost Network Flow: Convergence and Correctness. Oper. Res. 60(2): 410-428 (2012) - [j41]Devavrat Shah, Damon Wischik:
Log-weight scheduling in switched networks. Queueing Syst. Theory Appl. 71(1-2): 97-136 (2012) - [j40]Devavrat Shah:
Product-form distributions and network algorithms (abstract only). SIGMETRICS Perform. Evaluation Rev. 39(4): 24 (2012) - [j39]Urs Niesen, Devavrat Shah, Gregory W. Wornell:
Caching in Wireless Networks. IEEE Trans. Inf. Theory 58(10): 6524-6540 (2012) - [c95]Sahand Negahban, Devavrat Shah:
Learning sparse Boolean polynomials. Allerton Conference 2012: 2032-2036 - [c94]Peter Anthony Iannucci, Kermin Elliott Fleming, Jonathan Perry, Hari Balakrishnan, Devavrat Shah:
A hardware spinal decoder. ANCS 2012: 151-162 - [c93]