A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
DP-REC combines privacy and communication efficiency in federated learning.
The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on th…
The success of graph embeddings or node representation learning in a variety of downstream tasks, such as node classification, link prediction, and recommendation systems, has led to their popularity in recent years. Representation learning algorithms aim to preserve local and global network structure by identifying no…
Optimal penalties for RECs balance environmental and revenue impacts.
A faster method for visualization recommendations on large datasets.
A new method, REC, compresses images by encoding their latent representations efficiently.
In this paper, we discuss the statistical properties of the optimization methods , including the minimization method and the regularization method, for estimating a sparse parameter from noisy observations in high-dimensional linear regression with either a deterministic or rando…
New algorithm reconstructs genealogies from genetic data.
Smoothly recovers manifold from noisy data.
Deep learning solves complex PA mean field games with market-clearing conditions.
A new method to measure neural network expressiveness using tighter upper bounds.
Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-deman…
In many recommendation applications such as news recommendation, the items that can be rec- ommended come and go at a very fast pace. This is a challenge for recommender systems (RS) to face this setting. Online learning algorithms seem to be the most straight forward solution. The contextual bandit framework was intro…