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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… Article...

SLiCK: Exploiting Subsequences...

User-defined keyword spotting on a resource-constrained edge device is challenging. However, keywords are often bounded by a maximum keyword length, which has been...

Privacy-Computation Trade-offs in...

A Private Repetition algorithm takes as input a differentially private algorithm with constant success probability and boosts it to one that succeeds with...

3D Shape Tokenization

We introduce Shape Tokens, a 3D representation that is continuous, compact, and easy to integrate into machine learning models. Shape Tokens serve as...

Accelerating LLM Inference...

Accelerating LLM inference is an important ML research problem, as auto-regressive token generation is computationally expensive and relatively slow, and improving inference efficiency...

ARMADA: Augmented Reality...

Teleoperation for robot imitation learning is bottlenecked by hardware availability. Can high-quality robot data be collected without a physical robot? We present a...

BayesCNS: A Unified...

Information Retrieval (IR) systems used in search and recommendation platforms frequently employ Learning-to-Rank (LTR) models to rank items in response to user queries....

Evaluating Gender Bias...

*Equal Contributors Large language models (LLMs) are increasingly being adapted to achieve task-specificity for deployment in real-world decision systems. Several previous works have investigated...

Momentum Approximation in...

This paper was accepted for presentation at the International Workshop on Federated Foundation Models (FL@FM-NeurIPS'24), held in conjunction with NeurIPS 2024. Asynchronous protocols have...

How Easy is...

The remarkable advancements in Multimodal Large Language Models (MLLMs) have not rendered them immune to challenges, particularly in the context of handling deceptive...