14 citations · 58 across the 43 of their papers we have counts for
4 papers · 1 filter
Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales
Jonathan Kelner, Annie Marsden, Vatsal Sharan +3
We provide new gradient-based methods for efficiently solving a broad class of ill-conditioned optimization problems. We consider the problem of minimizing a function $f : \mathbb{…
Omnipredictors
Parikshit Gopalan, Adam Tauman Kalai, Omer Reingold +2
Loss minimization is a dominant paradigm in machine learning, where a predictor is trained to minimize some loss function that depends on an uncertain event (e.g., "will it rain to…
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks
Atish Agarwala, Abhimanyu Das, Brendan Juba +4
Can deep learning solve multiple tasks simultaneously, even when they are unrelated and very different? We investigate how the representations of the underlying tasks affect the ab…
Multicalibrated Partitions for Importance Weights
Parikshit Gopalan, Omer Reingold, Vatsal Sharan +1
The ratio between the probability that two distributions and give to points are known as importance weights or propensity scores and play a fundamental role in many dif…