6 citations · 20 across the 7 of their papers we have counts for
6 papers · 1 filter
RubiConv -- Efficient Boundary-Respecting Convolutions
Linda Friso, Annie Marsden, Xinyi Chen +4
Convolutional architectures have emerged as powerful alternatives to Transformers for sequence modeling. The primary advantage is that they offer improved theoretical sequence leng…
Online Nonstochastic Model-Free Reinforcement Learning
Udaya Ghai, Arushi Gupta, Wenhan Xia +2
We investigate robust model-free reinforcement learning algorithms designed for environments that may be dynamic or even adversarial. Traditional state-based policies often struggl…
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…
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…
Inherent Noise in Gradient Based Methods
Arushi Gupta
Previous work has examined the ability of larger capacity neural networks to generalize better than smaller ones, even without explicit regularizers, by analyzing gradient based al…
A Simple Saliency Method That Passes the Sanity Checks
Arushi Gupta, Sanjeev Arora
There is great interest in "saliency methods" (also called "attribution methods"), which give "explanations" for a deep net's decision, by assigning a "score" to each feature/pixel…