Aliakbarpour

Can we estimate balls-in-bins processes from one observation?

Maryam Aliakbarpour, Constantinos Daskalakis, Ronitt Rubinfeld, Manolis Zampetakis

PreprintTesting Tail Weight of a Distribution Via Hazard Rate

Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld

PreprintEstimation of Entropy in Constant Space with Improved Sample Complexity

Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten

To appear in**NeurIPS 2022**Local Differential Privacy Is Equivalent to Contraction of an f-Divergence

Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon

2021 IEEE International Symposium on Information Theory,**ISIT 2021**Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees

Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya Parameswaran, Ronitt Rubinfeld

37th IEEE International Conference on Data Engineering,**ICDE 2021**Testing Determinantal Point Processes

Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka

34-th Conference on Neural Information Processing Systems,**NeurIPS 2020 (spotlight talk)**Testing Properties of Multiple Distributions with Few Samples

Maryam Aliakbarpour, Sandeep Silwal

11th Innovations in Theoretical Computer Science Conference,**ITCS2020**Private Testing of Distributions via Sample Permutations

Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld

33-th Conference on Neural Information Processing Systems,**NeurIPS 2019**Testing Mixtures of Discrete Distributions

Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld

32nd Annual Conference on Learning Theory,**COLT 2019**

Full version, Video of the talk at COLT 2019

Towards Testing Monotonicity of Distributions Over General Posets

Maryam Aliakbarpour, Themistoklis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee

32nd Annual Conference on Learning Theory,**COLT 2019**

Full version Video of the talk at COLT 2019

Differentially Private Identity and Equivalence Testing of Discrete Distributions

Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld

35th International Conference on Machine Learning,**ICML 2018**

Video of the talk

Sublinear-Time Algorithms for Counting Star Subgraphs via Edge Sampling

Maryam Aliakbarpour, Amartya Shankha Biswas, Themistoklis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee

**Algorithmica**80(2), pp 668-697, 2018.

ArXiv versionI've Seen Enough: Incrementally Improving Visualizations to Support Rapid Decision Making.

Sajjadur Rahman, Maryam Aliakbarpour, Ha Kyung Kong, Eric Blais, Karrie Karahalios, Aditya Parameswaran, Ronitt Rubinfeld

43rd International Conference on Very Large Data Bases,**VLDB 2017**

Full version

Learning and Testing Junta Distributions

Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld

29th Annual Conference on Learning Theory,**COLT 2016**

Video of the talk at COLT 2016

Slides (short version), Slides (long version)Join of two graphs admits a nowhere-zero 3-flow

Saieed Akbari, Maryam Aliakbarpour, Niloofar Ghanbari, Emisa Nategh, Hossein Shahmohamad

**Czechoslovak Mathematical Journal**, Volume 64, Issue 2, pp 433-446, June 2014.Minimum flow number of complete multipartite graphs

Saieed Akbari, Maryam Aliakbarpour, Niloofar Ghanbari, Emisa Nategh, Hossein Shahmohamad

**Bulletin of the Institute of Combinatorics and its Applications**, Volume 66, pp 57-64, September 2012.

**PhD Thesis:**Distribution Testing: Classical and New Paradigms

September 2020.**Master Thesis:**Learning and Testing Junta Distributions over Hypercubes

September 2015.