Research Papers
- Differentially Private Hypothesis Testing for Linear Regression
- with Salil Vadhan
-
Journal on Machine Learning Research (JMLR) 2023
[JMLR]
- NaijaCoder: Participatory Design for Early Algorithms Education in the Global South
- with Atinuke Adegbile, Alida Monaco, Lekan Afuye, and Philip Abel
-
In Proceedings of the ACM Technical Symposium on Computer Science Education (SIGCSE), 2024
[arxiv]
- Saibot: A Differentially Private Data Search Platform
- with Zezhou Huang, Jiaxiang Liu, Raul Castro Fernandez, Eugene Wu
-
Proceedings of the International Conference on Very Large Data Bases (VLDB), 2023
[arxiv]
- Bounded Space Differentially Private Quantiles
- with Omri Ben-Eliezer and Anamay Chaturvedi
-
Transactions on Machine Learning Research (TMLR) 2023
[TMLR]
-
TPDP, 2021
[arxiv]
- Degree Distribution Identifiability of Stochastic Kronecker Multiplication
- with Dimitris Kalimeris
-
2023
[arxiv]
- Privately Estimating a Gaussian: Efficient, Robust and Optimal
- with Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang
-
Proceedings of the ACM Symposium on Theory of Computing (STOC), 2023
[arxiv]
[ACM]
- The Algorithmic Foundations of Private Computational Social Science
-
Doctoral Dissertation, Harvard University Graduate School of Arts and Sciences, 2022
[DASH]
- Private Rank Aggregation in Central and Local Models
- with Badih Ghazi, Ravi Kumar, and Pasin Manurangsi
-
Proceedings of the AAAI Conference on Artificial Intelligence, 2022
[arxiv]
-
PPML, 2021
- Hypothesis Testing for Differentially Private Linear Regression
- with Salil Vadhan
-
Proceedings of the Advances in Neural Information Processing Systems (NeurIPS), 2022
-
IMS (Institute of Mathematical Statistics) 2022 Annual Meeting
[arxiv]
-
SEA (Southern Economic Association) 2022 Annual Meeting
-
TPDP, 2021
- Privacy Budget Tailoring in Private Data Analysis
- with Chris Wiggins
-
Transactions on Machine Learning Research (TMLR) 2023
[TMLR]
- Differentially Private Simple Linear Regression
- with Audra McMillan, Jayshree Sarathy, Adam Smith, and Salil Vadhan
-
Journal on Privacy Enhancing Technologies (PoPETs), 2022
[arxiv]
-
TPDP, 2020
- The Cost of a Reductions Approach to Private Fair Optimization
-
TPDP, 2019
[arxiv]
-
- Learning to Prune: Speeding up Repeated Computations
- with Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik
-
COLT, 2019
[arxiv]
[PMLR]
- Unleashing Linear Optimizers for Group-Fair Learning and Optimization
- with Nicole Immorlica and Adam Tauman Kalai
-
COLT, 2018
[arxiv]
[PMLR]
- Learning Certifiably Optimal Rule Lists for Categorical Data
- with Elaine Angelino, Nicholas Larus-Stone, Margo Seltzer, and Cynthia Rudin
-
JMLR, 2018
[arxiv]
[JMLR]
-
Long Journal Version of KDD 2017, SysML 2018 papers with more analyses/results/experiments/proofs
- Systems Optimizations for Learning Certifiably Optimal Rule Lists
-
with Elaine Angelino, Vassilios Kaxiras, Nicholas Larus-Stone, Aditya Saligrama,
Margo Seltzer, and Cynthia Rudin
-
SysML, 2018
[pdf]
- Learning Certifiably Optimal Rule Lists
- with Elaine Angelino, Nicholas Larus-Stone, Margo Seltzer, and Cynthia Rudin
-
KDD, 2017
[pdf]
- Selected for oral presentation
- PFunk-H: Approximate Query Processing using Perceptual Models
- with Eugene Wu
-
Proceedings of the International Workshop on Human-in-the-Loop Data Analytics (HILDA), 2016
[pdf]
-
Poster at North East Database Day, January, 2016
- Selected for full oral presentation at HILDA 2016
- Exploiting visual perception for sampling-based approximation on aggregate queries
-
Columbia University Technical Report 1613, 2015