71 citations · 114 across the 8 of their papers we have counts for
4 papers · 1 filter
FedFetch: Faster Federated Learning with Adaptive Downstream Prefetching
Qifan Yan, Andrew Liu, Shiqi He +2
Federated learning (FL) is a machine learning paradigm that facilitates massively distributed model training with end-user data on edge devices directed by a central server. Howeve…
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design
Ali Behrouz, Mathias Lecuyer, Cynthia Rudin +1
Sparse decision trees are one of the most common forms of interpretable models. While recent advances have produced algorithms that fully optimize sparse decision trees for predict…
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Jinkun Lin, Anqi Zhang, Mathias Lecuyer +3
We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning model, such as a specific prediction it makes. Our…
Sayer: Using Implicit Feedback to Optimize System Policies
Mathias Lécuyer, Sang Hoon Kim, Mihir Nanavati +4
We observe that many system policies that make threshold decisions involving a resource (e.g., time, memory, cores) naturally reveal additional, or implicit feedback. For example,…