Daniel Alabi

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

Research Papers

The Cost of a Reductions Approach to Private Fair Optimization
Full version in submission  [arxiv] 
To appear in TPDP 2019 in November 2019
Poster at the Recent Themes in Resource Tradeoffs: Privacy, Fairness, and Robustness workshop at the University of Minnesota in June 2019 
Learning to Prune: Speeding up Repeated Computations
with Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, and Ellen Vitercik
COLT, June, 2019  [arxiv]  [PMLR] 
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
with Nicole Immorlica and Adam Tauman Kalai
COLT, July, 2018  [arxiv]  [PMLR] 
Learning Certifiably Optimal Rule Lists for Categorical Data
with Elaine Angelino, Nicholas Larus-Stone, Margo Seltzer, and Cynthia Rudin
JMLR, June, 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, February, 2018  [pdf] 
Learning Certifiably Optimal Rule Lists
with Elaine Angelino, Nicholas Larus-Stone, Margo Seltzer, and Cynthia Rudin
KDD, August, 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), June, 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, September, 2015
After climbing a great hill, one only finds that there are many more hills to climb.
Nelson Mandela