Statistical Learning
Implicit Regularization of Decentralized Gradient Descent for Decentralized Sparse Regression
We consider learning a sparse model from linear measurements taken by a network of agents.
4+3 Phases of Compute-Optimal Neural Scaling Laws Elliot Paquette
We consider the solvable neural scaling model with three parameters: data complexity, target complexity, and model-parameter-count.
AR-Pro: Counterfactual Explanations for Anomaly Repair with Formal Properties
Anomaly detection is widely used for identifying critical errors and suspicious behaviors, but current methods lack interpretability.
Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
ML or targeted minimum loss estimation.
FasMe: Fast and Sample-efficient Meta Estimator for Precision Matrix Learning in Small Sample Settings Xiao T an
For instance, researchers are examining the gene network derived from the gene expression data of patients to investigate a rare disease.
Collaborative Learning via Bilevel Optimization
Identifying helpful clients, however, presents challenging and often introduces significant overhead.
FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection Austin Coursey
In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection.