28 citations · 38 across the 5 of their papers we have counts for
5 papers
Efficient Parametric Approximations of Neural Network Function Space Distance
Nikita Dhawan, Sicong Huang, Juhan Bae +1
It is often useful to compactly summarize important properties of model parameters and training data so that they can be used later without storing and/or iterating over the entire…
Dataset Inference for Self-Supervised Models
Adam Dziedzic, Haonan Duan, Muhammad Ahmad Kaleem +5
Self-supervised models are increasingly prevalent in machine learning (ML) since they reduce the need for expensively labeled data. Because of their versatility in downstream appli…
On the Difficulty of Defending Self-Supervised Learning against Model Extraction
Adam Dziedzic, Nikita Dhawan, Muhammad Ahmad Kaleem +2
Self-Supervised Learning (SSL) is an increasingly popular ML paradigm that trains models to transform complex inputs into representations without relying on explicit labels. These…
Adaptive Risk Minimization: Learning to Adapt to Domain Shift
Marvin Zhang, Henrik Marklund, Nikita Dhawan +3
A fundamental assumption of most machine learning algorithms is that the training and test data are drawn from the same underlying distribution. However, this assumption is violate…
AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
Laura Smith, Nikita Dhawan, Marvin Zhang +2
Robotic reinforcement learning (RL) holds the promise of enabling robots to learn complex behaviors through experience. However, realizing this promise for long-horizon tasks in th…