Statistical Learning
Self-Supervised Learning of Representations for Space Generates Multi-Modular Grid Cells
To solve the spatial problems of mapping, localization and navigation, the mammalian lineage has developed striking spatial representations.
Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency
Our method requires only linear time in input size, and still matches the information theoretical optimal sample complexity up to a data distribution dependent condition number factor.
Near-Optimal k-Clustering in the Sliding Window Model
Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store.
Iteratively Learn Diverse Strategies with State Distance Information
In addition, we examine two common computation frameworks for this problem, i.e., population-based training (PBT) and iterative learning
Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation
Extensive experiments and analyses have been conducted to characterize and demonstrate the effectiveness of the proposed DivOE.
Convergence of Alternating Gradient Descent for Matrix Factorization
To the authors' knowledge, this improves on the state-of-art convergence result in the
