Daniel G. Alabi

alabid [at] cs [dot] columbia [dot] edu

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
After climbing a great hill, one only finds that there are many more hills to climb.
Nelson Mandela