6 citations · 9 across the 11 of their papers we have counts for
6 papers · 1 filter
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…
Label Differential Privacy via Aggregation
Anand Brahmbhatt, Rishi Saket, Shreyas Havaldar +3
This paper explores the use of linear aggregation to protect the privacy of sensitive training labels through the concept of \emph{label differential privacy} (label-DP) while main…
End-to-End Neural Network Compression via Regularized Latency Surrogates
Anshul Nasery, Hardik Shah, Arun Sai Suggala +1
Neural network (NN) compression via techniques such as pruning, quantization requires setting compression hyperparameters (e.g., number of channels to be pruned, bitwidths for quan…
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu +2
Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that Simplicity Bias (…
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli +1
We consider the problem of OOD generalization, where the goal is to train a model that performs well on test distributions that are different from the training distribution. Deep l…
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
Anshul Nasery, Soumyadeep Thakur, Vihari Piratla +2
In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the…