Categories: FAANG

Fingerprinting Codes Meet Geometry: Improved Lower Bounds for Private Query Release and Adaptive Data Analysis

Fingerprinting codes are a crucial tool for proving lower bounds in differential privacy. They have been used to prove tight lower bounds for several fundamental questions, especially in the “low accuracy” regime. Unlike reconstruction/discrepancy approaches however, they are more suited for proving worst-case lower bounds, for query sets that arise naturally from the fingerprinting codes construction. In this work, we propose a general framework for proving fingerprinting type lower bounds, that allows us to tailor the technique to the geometry of the query set.
Our approach allows us to…
AI Generated Robotic Content

Recent Posts

We truly live in dark times

submitted by /u/drgoldenpants [link] [comments]

23 hours ago

The Roadmap for Mastering MLOps in 2025

Organizations increasingly adopt machine learning solutions into their daily operations and long-term strategies, and, as…

23 hours ago

Taking a responsible path to AGI

We’re exploring the frontiers of AGI, prioritizing technical safety, proactive risk assessment, and collaboration with…

23 hours ago

Interpreting and Improving Optimal Control Problems With Directional Corrections

Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control…

23 hours ago

Ray jobs on Amazon SageMaker HyperPod: scalable and resilient distributed AI

Foundation model (FM) training and inference has led to a significant increase in computational needs…

23 hours ago

Beyond generic benchmarks: How Yourbench lets enterprises evaluate AI models against actual data

Hugging Face warned that Yourbench is compute intensive but this might be a price enterprises…

1 day ago