57 citations · 289 across the 59 of their papers we have counts for
7 papers · 1 filter
Modulated learning for private and distributed regression with just a single sample per client device
Praneeth Vepakomma, Amirhossein Reisizadeh, Samuel Horváth +1
This work focuses on the question of learning from a large number of devices with each device holding only a single sample of data. Several real-world applications exist to this on…
VITA: Variational Pretraining of Transformers for Climate-Robust Crop Yield Forecasting
Adib Hasan, Mardavij Roozbehani, Munther Dahleh
Accurate crop yield forecasting is essential for global food security. However, current AI models systematically underperform when yields deviate from historical trends. We attribu…
Sample Efficient Reinforcement Learning with Partial Dynamics Knowledge
Meshal Alharbi, Mardavij Roozbehani, Munther Dahleh
The problem of sample complexity of online reinforcement learning is often studied in the literature without taking into account any partial knowledge about the system dynamics tha…
Estimation of Models with Limited Data by Leveraging Shared Structure
Maryann Rui, Thibaut Horel, Munther Dahleh
Modern data sets, such as those in healthcare and e-commerce, are often derived from many individuals or systems but have insufficient data from each source alone to separately est…
SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise
Abdullah Alomar, Munther Dahleh, Sean Mann +1
The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochasti…
Nonstochastic Bandits with Infinitely Many Experts
X. Flora Meng, Tuhin Sarkar, Munther A. Dahleh
We study the problem of nonstochastic bandits with expert advice, extending the setting from finitely many experts to any countably infinite set: A learner aims to maximize the tot…