204 citations · 241 across the 9 of their papers we have counts for
9 papers
New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound
Arushi Gupta, Nikunj Saunshi, Dingli Yu +2
Saliency methods compute heat maps that highlight portions of an input that were most {\em important} for the label assigned to it by a deep net. Evaluations of saliency methods co…
Understanding Influence Functions and Datamodels via Harmonic Analysis
Nikunj Saunshi, Arushi Gupta, Mark Braverman +1
Influence functions estimate effect of individual data points on predictions of the model on test data and were adapted to deep learning in Koh and Liang [2017]. They have been use…
Understanding Contrastive Learning Requires Incorporating Inductive Biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel +5
Contrastive learning is a popular form of self-supervised learning that encourages augmentations (views) of the same input to have more similar representations compared to augmenta…
A Representation Learning Perspective on the Importance of Train-Validation Splitting in Meta-Learning
Nikunj Saunshi, Arushi Gupta, Wei Hu
An effective approach in meta-learning is to utilize multiple "train tasks" to learn a good initialization for model parameters that can help solve unseen "test tasks" with very fe…
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
Nikunj Saunshi, Sadhika Malladi, Sanjeev Arora
Autoregressive language models, pretrained using large text corpora to do well on next word prediction, have been successful at solving many downstream tasks, even with zero-shot u…
A Sample Complexity Separation between Non-Convex and Convex Meta-Learning
Nikunj Saunshi, Yi Zhang, Mikhail Khodak +1
One popular trend in meta-learning is to learn from many training tasks a common initialization for a gradient-based method that can be used to solve a new task with few samples. T…