Daniel Alabi

alabid [at] g [dot] harvard [dot] edu

Research Papers

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
IMS (Institute of Mathematical Statistics) 2022 Annual Meeting [arxiv] 
SEA (Southern Economic Association) 2022 Annual Meeting
TPDP, 2021 
Bounded Space Differentially Private Quantiles
with Omri Ben-Eliezer and Anamay Chaturvedi
TPDP, 2021  [arxiv] 
Prioritizing Minority Groups when Applying Differential Privacy
with Julius Adebayo and Chris Wiggins
Efficient Reductions for Differentially Private Multi-Objective Settings
with Julius Adebayo
TPDP, 2020 
Differentially Private Simple Linear Regression
with Audra McMillan, Jayshree Sarathy, Adam Smith, and Salil Vadhan
TPDP, 2020  [arxiv] 
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