← Search

Marco Pistoia

4 accepted papers

2025

A Unified Framework for Provably Efficient Algorithms to Estimate Shapley Values

NeurIPS 2025poster

Shapley values have emerged as a critical tool for explaining which features impact the decisions made by machine learning models. However, computing exact Shapley values is difficult, generally requiring an exponential (in the feature dimension) number of model evaluations. To address this, many mo…

Cited by 0SourceScholar
2025

Fast Zeroth-Order Convex Optimization with Quantum Gradient Methods

NeurIPS 2025poster

We study quantum algorithms based on quantum (sub)gradient estimation using noisy function evaluation oracles, and demonstrate the first dimension-independent query complexities (up to poly-logarithmic factors) for zeroth-order convex optimization in both smooth and nonsmooth settings. Interestingly…

Cited by 0SourceScholar
2024

MaSS: Multi-attribute Selective Suppression for Utility-preserving Data Transformation from an Information-theoretic Perspective

ICML 2024poster

The growing richness of large-scale datasets has been crucial in driving the rapid advancement and wide adoption of machine learning technologies. The massive collection and usage of data, however, pose an increasing risk for people's private and sensitive information due to either inadvertent misha…

2019

More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation

NeurIPS 2019poster

Current state-of-the-art models for video action recognition are mostly based on expensive 3D ConvNets. This results in a need for large GPU clusters to train and evaluate such architectures. To address this problem, we present an lightweight and memory-friendly architecture for action recognition t…