Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation
Confidence Sets for Causal Orderings
Causal discovery procedures aim to deduce causal relationships among variables in a multivariate dataset. While various methods have been proposed for estimating a single causal …
y.-samuel-wang
AdAdaGrad: Adaptive Batch Size Schemes for Adaptive Gradient Methods
The choice of batch sizes in minibatch stochastic gradient optimizers is critical in large-scale model training for both optimization and generalization performance. Although …
Statistical Inference for Networks of High-Dimensional Point Processes
Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional …
xu-wang
Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
In federated learning (FL) problems, client sampling plays a key role in the convergence speed of training algorithm. However, while being an important problem in FL, client …
Latent Multimodal Functional Graphical Model Estimation
Joint multimodal functional data acquisition, where functional data from multiple modes are measured simultaneously from the same subject, has emerged as an exciting modern …
katherine-tsai
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
In offline reinforcement learning (RL) an optimal policy is learnt solely from a priori collected observational data. However, in observational data, actions are often confounded …
High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
Undirected graphical models have been widely used to model the conditional independence structure of high-dimensional random vector data for years. In many modern applications such …
Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problems
We propose a trust-region stochastic sequential quadratic programming algorithm (TR-StoSQP) to solve nonlinear optimization problems with stochastic objectives and deterministic …
