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.
Paper uses MBO data for high-frequency price forecasting.
The MBO scheme for data clustering is analyzed in the large data limit, proving convergence to optimal partition problems.
Efficient algorithm for clustering and classification using MBO scheme.
This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare…
Conformal Candidate Certification advances offline MBO by certifying candidate designs with statistical guarantees.
Study validates metrics for offline MBO using diffusion models.
New method optimizes complex models with minimal data, proving global optimality.
Project forecasts liquidity withdrawal using machine learning models.
We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based …
The random forest algorithm (RF) has several hyperparameters that have to be set by the user, e.g., the number of observations drawn randomly for each tree and whether they are drawn with or without replacement, the number of variables drawn randomly for each split, the splitting rule, the minimum number of samples tha…
Networks capture pairwise interactions between entities and are frequently used in applications such as social networks, food networks, and protein interaction networks, to name a few. Communities, cohesive groups of nodes, often form in these applications, and identifying them gives insight into the overall organizati…
ClusterLOB clusters market events to identify different trading behaviors.
Theoretical proof shows COMs are a type of contrastive divergence model with improved sampling.
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model. It is designed for both single- and multi…
We present two graph-based algorithms for multiclass segmentation of high-dimensional data. The algorithms use a diffuse interface model based on the Ginzburg-Landau functional, related to total variation compressed sensing and image processing. A multiclass extension is introduced using the Gibbs simplex, with the fun…
Poisson learning improves graph-based semi-supervised learning at very low label rates.
Paper proposes COM-QEL to avoid overoptimistic solutions in offline optimization.
A new method for incorporating preferences in multi-objective Bayesian optimization.
JaxMARL-HFT accelerates MARL for HFT with 240x speedup.