Tensor data are multi-dimension arrays. Low-rank decomposition-based
reg...
A fundamental fact about bounded-degree graph expanders is that three no...
Building a universal video-language model for solving various video
unde...
Given a bipartite graph G, the graphical matrix space 𝒮_G
consists of ma...
We propose the AdaPtive Noise Augmentation (PANDA) procedure to regulari...
We propose Noise-Augmented Privacy-Preserving Empirical Risk Minimizatio...
We extend upper bounds on the quantum independence number and the quantu...
We develop a computationally-efficient PAC active learning algorithm for...
Matrix scaling and matrix balancing are two basic linear-algebraic probl...
We provide a condition-based analysis of two interior-point methods for
...
Corporations today collect data at an unprecedented and accelerating sca...
In this paper, we propose a model-free reinforcement learning method to
...
Non-negative Matrix Factorization (NMF) asks to decompose a (entry-wise)...
We propose CROPS, a fast Converging and Robust Optimal Path Selection
al...
Recent work on "learned indexes" has revolutionized the way we look at t...
In this paper we combine many of the standard and more recent algebraic
...
Contact-based decision and planning methods are becoming increasingly
im...
We extend the data augmentation technique (PANDA) by Li et al. (2018) fo...
We propose PANDA, an AdaPtive Noise Augmentation technique to regularize...
We study several quantum versions of the Shannon capacity of graphs and
...
This paper describes a quantum programming environment, named Q|SI〉.
It ...