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.
PAMS is a Python-based platform for simulating artificial markets.
Paper analyzes convergence of PAM method for low-rank factorization models.
Improved k-medoids clustering with faster algorithms for big data.
Recent advances in topic models have explored complicated structured distributions to represent topic correlation. For example, the pachinko allocation model (PAM) captures arbitrary, nested, and possibly sparse correlations between topics using a directed acyclic graph (DAG). While PAM provides more flexibility and gr…
The Pachinko Allocation Machine (PAM) is a deep topic model that allows representing rich correlation structures among topics by a directed acyclic graph over topics. Because of the flexibility of the model, however, approximate inference is very difficult. Perhaps for this reason, only a small number of potential PAM …
Clustering non-Euclidean data is difficult, and one of the most used algorithms besides hierarchical clustering is the popular algorithm Partitioning Around Medoids (PAM), also simply referred to as k-medoids. In Euclidean geometry the mean-as used in k-means-is a good estimator for the cluster center, but this does no…
The article examines different thresholding methods for improving PAM algorithm in cancer classification.
PAM models generate dependent random distributions across groups with overlapping clusters.
Efficient medoid-based Silhouette method speeds up clustering evaluation.
BanditPAM clusters data faster than traditional methods.
A new medoid-based Silhouette method selects optimal cluster numbers efficiently.
Deep learning model predicts traffic flows across entire network for multiple steps ahead.
We study clustering methods for binary data, first defining aggregation criteria that measure the compactness of clusters. Five new and original methods are introduced, using neighborhoods and population behavior combinatorial optimization metaheuristics: first ones are simulated annealing, threshold accepting and tabu…
Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions instead of a human? How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex? These are t…
Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventua…
A new method clusters mixed-type data efficiently.
Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the -means algorithm, a new class of recursive stochastic gradient algorithms designed for the -medians loss cri…
Paper proposes iLPA for solving DC composite optimization problems, with applications to matrix completion with outliers.
ProtoBandit uses bandits to find prototypes efficiently.
Bayesian deep learning counts crowds robustly despite occlusions and scale variations.
DBS uses swarm intelligence to cluster data without needing a global objective function.