Maryam Aliakbarpour

49 papers A* 15A 4B 2Journal 28

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	High-Dimensional Robust Mean Estimation with Untrusted Batches. CoRR Maryam Aliakbarpour, Vladimir Braverman, Yuhan Liu, Junze Yin
2026	A*	`conf`	On the Structure of Replicable Hypothesis Testers. SODA Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Shyam Narayanan, Sandeep Silwal
2026	J	`jnl`	Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions. CoRR Maryam Aliakbarpour, Alireza Azizi, Ria Stevens
2025	A*	`conf`	Adversarially Robust Quantum State Learning and Testing. FOCS Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Yuhan Liu
2025	J	`jnl`	Adversarially robust quantum state learning and testing. CoRR Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Yuhan Liu
2025	A*	`conf`	Better Private Distribution Testing by Leveraging Unverified Auxiliary Data. COLT Maryam Aliakbarpour, Arnav Burudgunte, Clément L. Canonne, Ronitt Rubinfeld
2025	J	`jnl`	Better Private Distribution Testing by Leveraging Unverified Auxiliary Data. CoRR Maryam Aliakbarpour, Arnav Burudgunte, Clément Cannone, Ronitt Rubinfeld
2025	A	`conf`	Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. AISTATS Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan
2025	J	`jnl`	High-Probability Bounds For Heterogeneous Local Differential Privacy. CoRR Maryam Aliakbarpour, Alireza Fallah, Swaha Roy, Ria Stevens
2025	J	`jnl`	Hypothesis Selection: A High Probability Conundrum. CoRR Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Sandeep Silwal
2025	J	`jnl`	Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor. CoRR Maryam Aliakbarpour, Zhan Shi, Ria Stevens, Vincent X. Wang
2025	J	`jnl`	On the Structure of Replicable Hypothesis Testers. CoRR Anders Aamand, Maryam Aliakbarpour, Justin Y. Chen, Shyam Narayanan, Sandeep Silwal
2025	A	`conf`	Privacy in Metalearning and Multitask Learning: Modeling and Separations. AISTATS Maryam Aliakbarpour, Konstantina Bairaktari, Adam Smith, Marika Swanberg, Jonathan R. Ullman
2025	J	`jnl`	Quantifying Information Disclosure During Gradient Descent Using Gradient Uniqueness. CoRR Sleem Mahmoud Abdelghafar, Maryam Aliakbarpour, Chris Jermaine
2025	J	`jnl`	Shadow Tomography Against Adversaries. CoRR Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Chia-Ying Lin, Yuhan Liu, Aadil Oufkir, Yu-Ching Shen
2025	J	`jnl`	Support Basis: Fast Attention Beyond Bounded Entries. CoRR Maryam Aliakbarpour, Vladimir Braverman, Junze Yin, Haochen Zhang
2024	A	`conf`	Differentially Private Medians and Interior Points for Non-Pathological Data. ITCS Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman
2024	J	`jnl`	Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy. CoRR Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan
2024	A*	`conf`	Metalearning with Very Few Samples Per Task. COLT Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Nathan Srebro, Jonathan R. Ullman
2024	A*	`conf`	Optimal Algorithms for Augmented Testing of Discrete Distributions. NeurIPS Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld, Sandeep Silwal
2024	J	`jnl`	Optimal Algorithms for Augmented Testing of Discrete Distributions. CoRR Maryam Aliakbarpour, Piotr Indyk, Ronitt Rubinfeld, Sandeep Silwal
2024	A*	`conf`	Optimal Hypothesis Selection in (Almost) Linear Time. NeurIPS Maryam Aliakbarpour, Mark Bun, Adam Smith
2024	J	`jnl`	Privacy in Metalearning and Multitask Learning: Modeling and Separations. CoRR Maryam Aliakbarpour, Konstantina Bairaktari, Adam D. Smith, Marika Swanberg, Jonathan R. Ullman
2023	J	`jnl`	Differentially Private Medians and Interior Points for Non-Pathological Data. CoRR Maryam Aliakbarpour, Rose Silver, Thomas Steinke, Jonathan R. Ullman
2023	A*	`conf`	Hypothesis Selection with Memory Constraints. NeurIPS Maryam Aliakbarpour, Mark Bun, Adam Smith
2023	J	`jnl`	Metalearning with Very Few Samples Per Task. CoRR Maryam Aliakbarpour, Konstantina Bairaktari, Gavin Brown, Adam Smith, Jonathan R. Ullman
2023	B	`conf`	Testing Tail Weight of a Distribution Via Hazard Rate. ALT Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld
2022	A*	`conf`	Estimation of Entropy in Constant Space with Improved Sample Complexity. NeurIPS Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten
2022	J	`jnl`	Estimation of Entropy in Constant Space with Improved Sample Complexity. CoRR Maryam Aliakbarpour, Andrew McGregor, Jelani Nelson, Erik Waingarten
2021	J	`jnl`	Local Differential Privacy Is Equivalent to Contraction of E CoRR Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon
2021	B	`conf`	Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence. ISIT Shahab Asoodeh, Maryam Aliakbarpour, Flávio P. Calmon
2021	A*	`conf`	Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. ICDE Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld
2020	J	`jnl`	Rapid Approximate Aggregation with Distribution-Sensitive Interval Guarantees. CoRR Stephen Macke, Maryam Aliakbarpour, Ilias Diakonikolas, Aditya G. Parameswaran, Ronitt Rubinfeld
2020	A*	`conf`	Testing Determinantal Point Processes. NeurIPS Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
2020	J	`jnl`	Testing Determinantal Point Processes. CoRR Khashayar Gatmiry, Maryam Aliakbarpour, Stefanie Jegelka
2020	A	`conf`	Testing Properties of Multiple Distributions with Few Samples. ITCS Maryam Aliakbarpour, Sandeep Silwal
2020	J	`jnl`	Testing Tail Weight of a Distribution Via Hazard Rate. CoRR Maryam Aliakbarpour, Amartya Shankha Biswas, Kavya Ravichandran, Ronitt Rubinfeld
2019	A*	`conf`	Private Testing of Distributions via Sample Permutations. NeurIPS Maryam Aliakbarpour, Ilias Diakonikolas, Daniel Kane, Ronitt Rubinfeld
2019	A*	`conf`	Testing Mixtures of Discrete Distributions. COLT Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld
2019	J	`jnl`	Testing Mixtures of Discrete Distributions. CoRR Maryam Aliakbarpour, Ravi Kumar, Ronitt Rubinfeld
2019	J	`jnl`	Testing Properties of Multiple Distributions with Few Samples. CoRR Maryam Aliakbarpour, Sandeep Silwal
2019	A*	`conf`	Towards Testing Monotonicity of Distributions Over General Posets. COLT Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee
2019	J	`jnl`	Towards Testing Monotonicity of Distributions Over General Posets. CoRR Maryam Aliakbarpour, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee
2018	A*	`conf`	Differentially Private Identity and Equivalence Testing of Discrete Distributions. ICML Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
2018	J	`jnl`	Sublinear-Time Algorithms for Counting Star Subgraphs via Edge Sampling. Algorithmica Maryam Aliakbarpour, Amartya Shankha Biswas, Themis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee
2017	J	`jnl`	Differentially Private Identity and Closeness Testing of Discrete Distributions. CoRR Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld
2017	J	`jnl`	I've Seen "Enough": Incrementally Improving Visualizations to Support Rapid Decision Making. Proc. VLDB Endow. Sajjadur Rahman, Maryam Aliakbarpour, Hidy Kong, Eric Blais, Karrie Karahalios, Aditya G. Parameswaran, Ronitt Rubinfeld
2016	A*	`conf`	Learning and Testing Junta Distributions. COLT Maryam Aliakbarpour, Eric Blais, Ronitt Rubinfeld
2016	J	`jnl`	Sublinear-Time Algorithms for Counting Star Subgraphs with Applications to Join Selectivity Estimation. CoRR Maryam Aliakbarpour, Amartya Shankha Biswas, Themistoklis Gouleakis, John Peebles, Ronitt Rubinfeld, Anak Yodpinyanee