Daniel G. Alabi

Research Papers

Privacy Considerations for Data Integration in Data Markets

with Eugene Wu

[arxiv] 

On the Spectra of Graph Embeddings Beyond 2x2 Initiator Matrices

[arxiv] 

Separating Graph Embedding Methods via Degree Distributions

with Dimitris Kalimeris

[arxiv] 

Degree Distribution Identifiability of Stochastic Kronecker Graphs

with Dimitris Kalimeris

[arxiv] 

EmpireDB: Data System to Accelerate Computational Sciences

with Eugene Wu

[arxiv] 

Differentially Private Hypothesis Testing for Linear Regression

with Salil Vadhan

Journal on Machine Learning Research (JMLR) 2023  [JMLR] 

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] 

Lecture Notes from the NaijaCoder Summer Camp

with Joseph Ekpenyong, Alida Monaco

[arxiv] 

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] 

Bounded Space Differentially Private Quantiles

with Omri Ben-Eliezer and Anamay Chaturvedi

Transactions on Machine Learning Research (TMLR) 2023  [TMLR] 

TPDP, 2021  [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