Search Results for author: Tatsuki Koga

Found 4 papers, 1 papers with code

Differentially Private Multi-Site Treatment Effect Estimation

no code implementations10 Oct 2023 Tatsuki Koga, Kamalika Chaudhuri, David Page

In this work, we take a fresh look at federated learning with a focus on causal inference; specifically, we look at estimating the average treatment effect (ATE), an important task in causal inference for healthcare applications, and provide a federated analytics approach to enable ATE estimation across multiple sites along with differential privacy (DP) guarantees at each site.

Causal Inference Federated Learning

Population Expansion for Training Language Models with Private Federated Learning

no code implementations14 Jul 2023 Tatsuki Koga, Congzheng Song, Martin Pelikan, Mona Chitnis

Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee.

Domain Adaptation Federated Learning +1

Privacy Amplification by Subsampling in Time Domain

no code implementations13 Jan 2022 Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri

When this is the case, we observe that the influence of a single participant (sensitivity) can be reduced by subsampling and/or filtering in time, while still meeting privacy requirements.

Time Series Time Series Analysis

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