5 papers · 1 filter
Cost-Aware Learning
Clara Mohri, Amir Globerson, Haim Kaplan +2
We consider the problem of Cost-Aware Learning, where sampling different components of a finite-sum objective incurs different costs. The objective is to reach a target error while…
Fast Inference via Hierarchical Speculative Decoding
Clara Mohri, Haim Kaplan, Tal Schuster +2
Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency witho…
Optimal Learning from Label Proportions with General Loss Functions
Lorne Applebaum, Travis Dick, Claudio Gentile +2
Motivated by problems in online advertising, we address the task of Learning from Label Proportions (LLP). We introduce a novel and versatile low-variance debiasing methodology to…
Nearly Optimal Sample Complexity for Learning with Label Proportions
Robert Busa-Fekete, Travis Dick, Claudio Gentile +3
We investigate Learning from Label Proportions (LLP), a partial information setting where examples in a training set are grouped into bags, and only aggregate label values in each…
Learning-Augmented Algorithms with Explicit Predictors
Marek Elias, Haim Kaplan, Yishay Mansour +1
Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. These approaches have demonstrated an enhancem…