10 citations · 17 across the 23 of their papers we have counts for
26 papers
Optimal Prediction-Augmented Algorithms for Testing Independence of Distributions
Maryam Aliakbarpour, Alireza Azizi, Ria Stevens
Independence testing is a fundamental problem in statistical inference: given samples from a joint distribution over multiple random variables, the goal is to determine whether…
High-Dimensional Robust Mean Estimation with Untrusted Batches
Maryam Aliakbarpour, Vladimir Braverman, Yuhan Liu +1
We study high-dimensional mean estimation in a collaborative setting where data is contributed by users in batches of size . In this environment, a learner seeks to recover…
Shadow Tomography Against Adversaries
Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia +4
We study single-copy shadow tomography in the adversarial robust setting, where the goal is to learn the expectation values of observables with …
High-Probability Bounds For Heterogeneous Local Differential Privacy
Maryam Aliakbarpour, Alireza Fallah, Swaha Roy +1
We study statistical estimation under local differential privacy (LDP) when users may hold heterogeneous privacy levels and accuracy must be guaranteed with high probability. Depar…
Auditing Information Disclosure During Large-Scale Gradient-Based Training via Gradient Uniqueness
Sleem Abdelghafar, Maryam Aliakbarpour, Chris Jermaine +1
Auditing information disclosure across every datapoint during the training of LLMs is challenging. We propose a principled, attack-agnostic approach that uses mutual information to…
Support Basis: Fast Attention Beyond Bounded Entries
Maryam Aliakbarpour, Vladimir Braverman, Junze Yin +1
Large language models (LLMs) have demonstrated remarkable performance across a wide range of tasks. However, the quadratic complexity of softmax attention remains a central bottlen…