146 citations · 254 across the 22 of their papers we have counts for
21 papers · 1 filter
Out of the Ordinary: Spectrally Adapting Regression for Covariate Shift
Benjamin Eyre, Elliot Creager, David Madras +2
Designing deep neural network classifiers that perform robustly on distributions differing from the available training data is an active area of machine learning research. However,…
Weighted Training for Cross-Task Learning
Shuxiao Chen, Koby Crammer, Hangfeng He +2
In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance be…
Finite Sample Analysis Of Dynamic Regression Parameter Learning
Mark Kozdoba, Edward Moroshko, Shie Mannor +1
We consider the dynamic linear regression problem, where the predictor vector may vary with time. This problem can be modeled as a linear dynamical system, with non-constant observ…
Multi Instance Learning For Unbalanced Data
Mark Kozdoba, Edward Moroshko, Lior Shani +4
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective. We show that when the data is unbalanced and the family of classifiers is suffici…
A Better Resource Allocation Algorithm with Semi-Bandit Feedback
Yuval Dagan, Koby Crammer
We study a sequential resource allocation problem between a fixed number of arms. On each iteration the algorithm distributes a resource among the arms in order to maximize the exp…
Efficient Loss-Based Decoding on Graphs For Extreme Classification
Itay Evron, Edward Moroshko, Koby Crammer
In extreme classification problems, learning algorithms are required to map instances to labels from an extremely large label set. We build on a recent extreme classification frame…