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Found 6 papers, 2 papers with code
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting
A common network inference problem, arising from real-world data constraints, is how to infer a dynamic network from its time-aggregated adjacency matrix and time-varying marginals (i. e., row and column sums).
On Sinkhorn's Algorithm and Choice Modeling
For a broad class of choice and ranking models based on Luce's choice axiom, including the Bradley--Terry--Luce and Plackett--Luce models, we show that the associated maximum likelihood estimation problems are equivalent to a classic matrix balancing problem with target row and column sums.
Optimal Diagonal Preconditioning
Moreover, our implementation of customized solvers, combined with a random row/column sampling step, can find near-optimal diagonal preconditioners for matrices up to size 200, 000 in reasonable time, demonstrating their practical appeal.
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg
For strongly convex and convex problems, we also characterize the corresponding convergence rates for the Nesterov accelerated FedAvg algorithm, which are the first linear speedup guarantees for momentum variants of FedAvg in convex settings.
Interpretable Personalization via Policy Learning with Linear Decision Boundaries
Our findings suggest that our proposed policy learning framework using tools from causal inference and Bayesian optimization provides a promising practical approach to interpretable personalization across a wide range of applications.
Ensemble Methods for Causal Effects in Panel Data Settings
This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment.
