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Found 8 papers, 2 papers with code
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Decision-making pipelines are generally characterized by tradeoffs among various risk functions.
On the design-dependent suboptimality of the Lasso
This paper investigates the effect of the design matrix on the ability (or inability) to estimate a sparse parameter in linear regression.
Transformers can optimally learn regression mixture models
In this work, we investigate the hypothesis that transformers can learn an optimal predictor for mixtures of regressions.
Optimally tackling covariate shift in RKHS-based nonparametric regression
We study the covariate shift problem in the context of nonparametric regression over a reproducing kernel Hilbert space (RKHS).
A new similarity measure for covariate shift with applications to nonparametric regression
We study covariate shift in the context of nonparametric regression.
Cluster-and-Conquer: A Framework For Time-Series Forecasting
Our framework -- which we refer to as "cluster-and-conquer" -- is highly general, allowing for any time-series forecasting and clustering method to be used in each step.
FedSplit: An algorithmic framework for fast federated optimization
Motivated by federated learning, we consider the hub-and-spoke model of distributed optimization in which a central authority coordinates the computation of a solution among many agents while limiting communication.
Identifying and Correcting Bias from Time- and Severity- Dependent Reporting Rates in the Estimation of the COVID-19 Case Fatality Rate
As we are in the middle of an active outbreak, estimating this measure will necessarily involve correcting for time- and severity- dependent reporting of cases, and time-lags in observed patient outcomes.
