83 citations · 221 across the 19 of their papers we have counts for
11 papers · 1 filter
Monge-Kantorovich Fitting With Sobolev Budgets
Forest Kobayashi, Jonathan Hayase, Young-Heon Kim
Given , we consider the problem of ``best'' approximating an probability measure via an measure such that has bounded to…
PLeaS -- Merging Models with Permutations and Least Squares
Anshul Nasery, Jonathan Hayase, Pang Wei Koh +1
The democratization of machine learning systems has made the process of fine-tuning accessible to practitioners, leading to a wide range of open-source models fine-tuned on special…
Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
Gavin Brown, Jonathan Hayase, Samuel Hopkins +5
We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the con…
Scalable Extraction of Training Data from (Production) Language Models
Milad Nasr, Nicholas Carlini, Jonathan Hayase +7
This paper studies extractable memorization: training data that an adversary can efficiently extract by querying a machine learning model without prior knowledge of the training da…
Label Poisoning is All You Need
Rishi D. Jha, Jonathan Hayase, Sewoong Oh
In a backdoor attack, an adversary injects corrupted data into a model's training dataset in order to gain control over its predictions on images with a specific attacker-defined t…
Zonotope Domains for Lagrangian Neural Network Verification
Matt Jordan, Jonathan Hayase, Alexandros G. Dimakis +1
Neural network verification aims to provide provable bounds for the output of a neural network for a given input range. Notable prior works in this domain have either generated bou…
Few-shot Backdoor Attacks via Neural Tangent Kernels
Jonathan Hayase, Sewoong Oh
In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired t…
Git Re-Basin: Merging Models modulo Permutation Symmetries
Samuel K. Ainsworth, Jonathan Hayase, Siddhartha Srinivasa
The success of deep learning is due in large part to our ability to solve certain massive non-convex optimization problems with relative ease. Though non-convex optimization is NP-…
Towards a Defense Against Federated Backdoor Attacks Under Continuous Training
Shuaiqi Wang, Jonathan Hayase, Giulia Fanti +1
Backdoor attacks are dangerous and difficult to prevent in federated learning (FL), where training data is sourced from untrusted clients over long periods of time. These difficult…
SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics
Jonathan Hayase, Weihao Kong, Raghav Somani +1
Modern machine learning increasingly requires training on a large collection of data from multiple sources, not all of which can be trusted. A particularly concerning scenario is w…
The Futility of Bias-Free Learning and Search
George D. Montanez, Jonathan Hayase, Julius Lauw +3
Building on the view of machine learning as search, we demonstrate the necessity of bias in learning, quantifying the role of bias (measured relative to a collection of possible da…