Maryam Aliakbarpour

According to our database, Maryam Aliakbarpour authored at least 20 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Differentially Private Medians and Interior Points for Non-Pathological Data.

Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman

Proceedings of the 15th Innovations in Theoretical Computer Science Conference, 2024

Metalearning with Very Few Samples Per Task.

Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman

Proceedings of the Thirty Seventh Annual Conference on Learning Theory, June 30, 2024

2023

Metalearning with Very Few Samples Per Task.

Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan R. Ullman

CoRR, 2023

Hypothesis Selection with Memory Constraints.

Maryam Aliakbarpour, Mark Bun, Adam Smith

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Testing Tail Weight of a Distribution Via Hazard Rate.

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

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Estimation of Entropy in Constant Space with Improved Sample Complexity.

Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Local Differential Privacy Is Equivalent to Contraction of E_γ-Divergence.

Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon

CoRR, 2021

Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence.

Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon

Proceedings of the IEEE International Symposium on Information Theory, 2021

Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees.

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

Proceedings of the 37th IEEE International Conference on Data Engineering, 2021

2020

Testing Determinantal Point Processes.

Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Testing Properties of Multiple Distributions with Few Samples.

Maryam Aliakbarpour, Sandeep Silwal

Proceedings of the 11th Innovations in Theoretical Computer Science Conference, 2020

2019

Private Testing of Distributions via Sample Permutations.

Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Towards Testing Monotonicity of Distributions Over General Posets.

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

Proceedings of the Conference on Learning Theory, 2019

Testing Mixtures of Discrete Distributions.

Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld

Proceedings of the Conference on Learning Theory, 2019

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

Differentially Private Identity and Equivalence Testing of Discrete Distributions.

Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld

Proceedings of the 35th International Conference on Machine Learning, 2018

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 Closeness Testing of Discrete Distributions.

Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld

CoRR, 2017

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

Learning and Testing Junta Distributions.

Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld

Proceedings of the 29th Conference on Learning Theory, 2016