Learning and Testing Junta Distributions. - Maryam Aliakbarpour; Eric Blais; Ronitt Rubinfeld (COLT, 2016)

Sublinear-Time Algorithms for Counting Star Subgraphs with Applications to Join Selectivity Estimation. - Maryam Aliakbarpour; Amartya Shankha Biswas; Themistoklis Gouleakis; John Peebles; Ronitt Rubinfeld; Anak Yodpinyanee (CoRR, 2016)

Differentially Private Identity and Closeness Testing of Discrete Distributions. - Maryam Aliakbarpour; Ilias Diakonikolas; Ronitt Rubinfeld (CoRR, 2017)

I've Seen "Enough": Incrementally Improving Visualizations to Support Rapid Decision Making. - Sajjadur Rahman; Maryam Aliakbarpour; Hidy Kong; Eric Blais; Karrie Karahalios; Aditya G. Parameswaran; Ronitt Rubinfeld (Proc. VLDB Endow., 2017)

Differentially Private Identity and Equivalence Testing of Discrete Distributions. - Maryam Aliakbarpour; Ilias Diakonikolas; Ronitt Rubinfeld (ICML, 2018)

Sublinear-Time Algorithms for Counting Star Subgraphs via Edge Sampling. - Maryam Aliakbarpour; Amartya Shankha Biswas; Themis Gouleakis; John Peebles; Ronitt Rubinfeld; Anak Yodpinyanee (Algorithmica, 2018)

Testing Mixtures of Discrete Distributions. - Maryam Aliakbarpour; Ravi Kumar 001; Ronitt Rubinfeld (COLT, 2019)

Towards Testing Monotonicity of Distributions Over General Posets. - Maryam Aliakbarpour; Themis Gouleakis; John Peebles; Ronitt Rubinfeld; Anak Yodpinyanee (COLT, 2019)

Private Testing of Distributions via Sample Permutations. - Maryam Aliakbarpour; Ilias Diakonikolas; Daniel Kane 001; Ronitt Rubinfeld (NeurIPS, 2019)

Testing Properties of Multiple Distributions with Few Samples. - Maryam Aliakbarpour; Sandeep Silwal (ITCS, 2020)

Testing Determinantal Point Processes. - Khashayar Gatmiry; Maryam Aliakbarpour; Stefanie Jegelka (NeurIPS, 2020)

Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. - Stephen Macke; Maryam Aliakbarpour; Ilias Diakonikolas; Aditya G. Parameswaran; Ronitt Rubinfeld (ICDE, 2021)

Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence. - Shahab Asoodeh; Maryam Aliakbarpour; Flávio P. Calmon (ISIT, 2021)

Local Differential Privacy Is Equivalent to Contraction of Eγ-Divergence. - Shahab Asoodeh; Maryam Aliakbarpour; Flávio P. Calmon (CoRR, 2021)

Estimation of Entropy in Constant Space with Improved Sample Complexity. - Maryam Aliakbarpour; Andrew McGregor 001; Jelani Nelson; Erik Waingarten (NeurIPS, 2022)

Testing Tail Weight of a Distribution Via Hazard Rate. - Maryam Aliakbarpour; Amartya Shankha Biswas; Kavya Ravichandran; Ronitt Rubinfeld (ALT, 2023)

Hypothesis Selection with Memory Constraints. - Maryam Aliakbarpour; Mark Bun; Adam Smith 006 (NeurIPS, 2023)

Metalearning with Very Few Samples Per Task. - Maryam Aliakbarpour; Konstantina Bairaktari; Gavin Brown 003; Adam Smith 006; Jonathan R. Ullman (CoRR, 2023)

Metalearning with Very Few Samples Per Task. - Maryam Aliakbarpour; Konstantina Bairaktari; Gavin Brown 003; Adam Smith 006; Nathan Srebro; Jonathan R. Ullman (COLT, 2024)

Differentially Private Medians and Interior Points for Non-Pathological Data. - Maryam Aliakbarpour; Rose Silver; Thomas Steinke 002; Jonathan R. Ullman (ITCS, 2024)

Optimal Hypothesis Selection in (Almost) Linear Time. - Maryam Aliakbarpour; Mark Bun; Adam Smith 006 (NeurIPS, 2024)

Optimal Algorithms for Augmented Testing of Discrete Distributions. - Maryam Aliakbarpour; Piotr Indyk; Ronitt Rubinfeld; Sandeep Silwal (NeurIPS, 2024)

Privacy in Metalearning and Multitask Learning: Modeling and Separations. - Maryam Aliakbarpour; Konstantina Bairaktari; Adam Smith 006; Marika Swanberg; Jonathan R. Ullman (AISTATS, 2025)

Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. - Maryam Aliakbarpour; Syomantak Chaudhuri; Thomas A. Courtade; Alireza Fallah 001; Michael I. Jordan (AISTATS, 2025)

Better Private Distribution Testing by Leveraging Unverified Auxiliary Data. - Maryam Aliakbarpour; Arnav Burudgunte; Clément L. Canonne; Ronitt Rubinfeld (COLT, 2025)

Better Private Distribution Testing by Leveraging Unverified Auxiliary Data. - Maryam Aliakbarpour; Arnav Burudgunte; Clément Cannone; Ronitt Rubinfeld (CoRR, 2025)

Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor. - Maryam Aliakbarpour; Zhan Shi; Ria Stevens; Vincent X. Wang (CoRR, 2025)

On the Structure of Replicable Hypothesis Testers. - Anders Aamand; Maryam Aliakbarpour; Justin Y. Chen; Shyam Narayanan; Sandeep Silwal (CoRR, 2025)