275 citations · 454 across the 4 of their papers we have counts for
4 papers
Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings
Harbin Hong, Sebastian Caldas, Liu Leqi
As Large Language Models (LLMs) increasingly appear in social science research (e.g., economics and marketing), it becomes crucial to assess how well these models replicate human b…
Differentially Private Meta-Learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas +1
Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning. However,…
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Sebastian Caldas, Jakub Konečny, H. Brendan McMahan +1
Communication on heterogeneous edge networks is a fundamental bottleneck in Federated Learning (FL), restricting both model capacity and user participation. To address this issue,…
LEAF: A Benchmark for Federated Settings
Sebastian Caldas, Sai Meher Karthik Duddu, Peter Wu +5
Modern federated networks, such as those comprised of wearable devices, mobile phones, or autonomous vehicles, generate massive amounts of data each day. This wealth of data can he…