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ORCIDMaryam Aliakbarpour
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2020 – today
- 2024
[c14]Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. COLT 2024: 46-93- [c13]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS 2024: 3:1-3:21 - 2023
- [c12]Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld:
Testing Tail Weight of a Distribution Via Hazard Rate. ALT 2023: 34-81 - [c11]Maryam Aliakbarpour, Mark Bun, Adam Smith:
Hypothesis Selection with Memory Constraints. NeurIPS 2023 - [i12]Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman:
Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR abs/2305.13440 (2023) - [i11]Maryam Aliakbarpour
, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan R. Ullman:
Metalearning with Very Few Samples Per Task. CoRR abs/2312.13978 (2023) - 2022
- [c10]Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten:
Estimation of Entropy in Constant Space with Improved Sample Complexity. NeurIPS 2022 - [i10]Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten:
Estimation of Entropy in Constant Space with Improved Sample Complexity. CoRR abs/2205.09804 (2022) - 2021
- [c9]Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld:
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. ICDE 2021: 1703-1714 - [c8]Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon:
Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence. ISIT 2021: 545-550 - [i9]Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon:
Local Differential Privacy Is Equivalent to Contraction of Eγ-Divergence. CoRR abs/2102.01258 (2021) - 2020
- [c7]Maryam Aliakbarpour, Sandeep Silwal:
Testing Properties of Multiple Distributions with Few Samples. ITCS 2020: 69:1-69:41 - [c6]Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka:
Testing Determinantal Point Processes. NeurIPS 2020 - [i8]Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka:
Testing Determinantal Point Processes. CoRR abs/2008.03650 (2020) - [i7]Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld:
Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. CoRR abs/2008.03891 (2020) - [i6]Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld:
Testing Tail Weight of a Distribution Via Hazard Rate. CoRR abs/2010.02888 (2020)
2010 – 2019
- 2019
- [c5]Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee:
Towards Testing Monotonicity of Distributions Over General Posets. COLT 2019: 34-82 - [c4]Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld:
Testing Mixtures of Discrete Distributions. COLT 2019: 83-114 - [c3]Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld:
Private Testing of Distributions via Sample Permutations. NeurIPS 2019: 10877-10888 - [i5]Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee:
Towards Testing Monotonicity of Distributions Over General Posets. CoRR abs/1907.03182 (2019) - [i4]Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld:
Testing Mixtures of Discrete Distributions. CoRR abs/1907.03190 (2019) - [i3]Maryam Aliakbarpour, Sandeep Silwal:
Testing Properties of Multiple Distributions with Few Samples. CoRR abs/1911.07324 (2019) - 2018
- [j2]Maryam Aliakbarpour, Amartya Shankha Biswas, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee:
Sublinear-Time Algorithms for Counting Star Subgraphs via Edge Sampling. Algorithmica 80(2): 668-697 (2018) - [c2]Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld:
Differentially Private Identity and Equivalence Testing of Discrete Distributions. ICML 2018: 169-178 - 2017
- [j1]Sajjadur Rahman, Maryam Aliakbarpour, Hidy Kong, Eric Blais, Karrie Karahalios, Aditya G. Parameswaran, Ronitt Rubinfeld:
I've Seen "Enough": Incrementally Improving Visualizations to Support Rapid Decision Making. Proc. VLDB Endow. 10(11): 1262-1273 (2017) - [i2]Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld:
Differentially Private Identity and Closeness Testing of Discrete Distributions. CoRR abs/1707.05497 (2017) - 2016
- [c1]Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld:
Learning and Testing Junta Distributions. COLT 2016: 19-46 - [i1]Maryam Aliakbarpour, Amartya Shankha Biswas, Themistoklis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee:
Sublinear-Time Algorithms for Counting Star Subgraphs with Applications to Join Selectivity Estimation. CoRR abs/1601.04233 (2016)
Coauthor Index
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