7 citations · 11 across the 5 of their papers we have counts for
6 papers
Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models
Dariush Wahdany, Jesse Cresswell, Naiqing Guan +3
Tabular foundation models (TFMs) are increasingly deployed through black-box APIs and trained on large collections of real-world tabular datasets. As this training paradigm becomes…
Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Brendan Leigh Ross, Noël Vouitsis, Atiyeh Ashari Ghomi +8
Although large language models (LLMs) are becoming increasingly capable of solving challenging real-world tasks, accurately quantifying their uncertainty remains a critical open pr…
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi +6
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these…
Augment then Smooth: Reconciling Differential Privacy with Certified Robustness
Jiapeng Wu, Atiyeh Ashari Ghomi, David Glukhov +3
Machine learning models are susceptible to a variety of attacks that can erode trust, including attacks against the privacy of training data, and adversarial examples that jeopardi…
Disparate Impact in Differential Privacy from Gradient Misalignment
Maria S. Esipova, Atiyeh Ashari Ghomi, Yaqiao Luo +1
As machine learning becomes more widespread throughout society, aspects including data privacy and fairness must be carefully considered, and are crucial for deployment in highly r…
Seasonal Goods and Spoiled Milk: Pricing for a Limited Shelf-Life
Atiyeh Ashari Ghomi, Allan Borodin, Omer Lev
We examine the case of items with a limited shelf-life where storing an item (before consumption) may carry a cost to a buyer (or distributor). For example, eggs, milk, or Groupon…