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ORCIDDaniel Alabi
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2020 – today
- 2024
[c8]Daniel Alabi
, Atinuke Adegbile, Lekan Afuye, Philip Abel, Alida Monaco:
NaijaCoder: Participatory Design for Early Algorithms Education in the Global South. SIGCSE (1) 2024: 39-45- [i13]Daniel Alabi, Joseph Ekpenyong, Alida Monaco:
Lecture Notes from the NaijaCoder Summer Camp. CoRR abs/2409.01499 (2024) - 2023
- [j6]Daniel Alabi, Salil P. Vadhan:
Differentially Private Hypothesis Testing for Linear Regression. J. Mach. Learn. Res. 24: 361:1-361:50 (2023) - [j5]Zezhou Huang, Jiaxiang Liu, Daniel Alabi, Raul Castro Fernandez, Eugene Wu:
Saibot: A Differentially Private Data Search Platform. Proc. VLDB Endow. 16(11): 3057-3070 (2023) - [j4]Daniel Alabi, Chris Wiggins:
Privacy Budget Tailoring in Private Data Analysis. Trans. Mach. Learn. Res. 2023 (2023) - [j3]Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi:
Bounded Space Differentially Private Quantiles. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang:
Privately Estimating a Gaussian: Efficient, Robust, and Optimal. STOC 2023: 483-496 - [i12]Zezhou Huang, Jiaxiang Liu, Daniel Alabi, Raul Castro Fernandez, Eugene Wu:
Saibot: A Differentially Private Data Search Platform. CoRR abs/2307.00432 (2023) - [i11]Daniel Alabi, Dimitris Kalimeris:
Degree Distribution Identifiability of Stochastic Kronecker Graphs. CoRR abs/2310.00171 (2023) - [i10]Daniel Alabi, Atinuke Adegbile, Lekan Afuye, Philip Abel, Alida Monaco:
NaijaCoder: Participatory Design for Early Algorithms Education in the Global South. CoRR abs/2310.20488 (2023) - 2022
- [j2]Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan:
Differentially Private Simple Linear Regression. Proc. Priv. Enhancing Technol. 2022(2): 184-204 (2022) - [c6]Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Private Rank Aggregation in Central and Local Models. AAAI 2022: 5984-5991 - [c5]Daniel Alabi, Salil P. Vadhan:
Hypothesis Testing for Differentially Private Linear Regression. NeurIPS 2022 - [i9]Daniel Alabi, Omri Ben-Eliezer, Anamay Chaturvedi:
Bounded Space Differentially Private Quantiles. CoRR abs/2201.03380 (2022) - [i8]Daniel Alabi, Salil P. Vadhan:
Hypothesis Testing for Differentially Private Linear Regression. CoRR abs/2206.14449 (2022) - [i7]Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang:
Privately Estimating a Gaussian: Efficient, Robust and Optimal. CoRR abs/2212.08018 (2022) - 2021
- [i6]Daniel Alabi, Badih Ghazi, Ravi Kumar, Pasin Manurangsi:
Private Rank Aggregation in Central and Local Models. CoRR abs/2112.14652 (2021) - 2020
- [i5]Daniel Alabi, Audra McMillan, Jayshree Sarathy, Adam D. Smith, Salil P. Vadhan:
Differentially Private Simple Linear Regression. CoRR abs/2007.05157 (2020)
2010 – 2019
- 2019
- [c4]Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik:
Learning to Prune: Speeding up Repeated Computations. COLT 2019: 30-33 - [i4]Daniel Alabi, Adam Tauman Kalai, Katrina Ligett, Cameron Musco, Christos Tzamos, Ellen Vitercik:
Learning to Prune: Speeding up Repeated Computations. CoRR abs/1904.11875 (2019) - [i3]Daniel Alabi:
The Cost of a Reductions Approach to Private Fair Optimization. CoRR abs/1906.09613 (2019) - 2018
- [c3]Daniel Alabi, Nicole Immorlica, Adam Kalai:
Unleashing Linear Optimizers for Group-Fair Learning and Optimization. COLT 2018: 2043-2066 - [i2]Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai:
When optimizing nonlinear objectives is no harder than linear objectives. CoRR abs/1804.04503 (2018) - 2017
- [j1]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists for Categorical Data. J. Mach. Learn. Res. 18: 234:1-234:78 (2017) - [c2]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists. KDD 2017: 35-44 - [i1]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists for Categorical Data. CoRR abs/1704.01701 (2017) - 2016
- [c1]Daniel Alabi, Eugene Wu:
PFunk-H: approximate query processing using perceptual models. HILDA@SIGMOD 2016: 10
Coauthor Index
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