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.
A new graph kernel uses LCS and Wasserstein distance for better graph comparisons.
This paper reverses a construction by merging boundary critical points into an interior one.
Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art technique for training large-scale kernelized support vector machines. The budget constraint is maintained incrementally by merging two points whenever the pre-defined budget is exceeded. The process of finding suitable merge partners is costly; it can a…
A critical decision point when training predictors using multiple studies is whether studies should be combined or treated separately. We compare two multi-study prediction approaches in the presence of potential heterogeneity in predictor-outcome relationships across datasets: 1) merging all of the datasets and traini…
Efficiently estimates longitudinal networks by merging sparse networks.
Boosting strategies for merging vs. ensembling studies analyzed.
Spectral algorithm recovers community structure in sparse hypergraphs.
Finding the optimal -means clustering is NP-hard in general and many heuristics have been designed for minimizing monotonically the -means objective. We first show how to extend Lloyd's batched relocation heuristic and Hartigan's single-point relocation heuristic to take into account empty-cluster and single-poin…
A scalable GPVAE method using local adjacencies to approximate GP inference.
LEWIS merges LLMs without training, improving performance on specific tasks.
We consider the initial situation where a dataset has been over-partitioned into clusters and seek a domain independent way to merge those initial clusters. We identify the total variation distance (TVD) as suitable for this goal. By exploiting the relation of the TVD to the Bayes accuracy we show how neural networ…
Enhanced survival trees improve computational efficiency and inference.
A regular -gon inscribing a knot is a sequence of points on a knot, such that the distances between adjacent points are all the same. It is shown that any smooth knot is inscribed by a regular -gon for any .
Study shows training duration impacts model merging quality, suggesting joint selection of duration and method.
Study shows training duration affects model merging quality, suggesting joint selection of duration and method.
EpiMer merges models by solving Fréchet mean on a Riemannian manifold.
New Bayesian method improves Pareto front estimation in multitask finetuning.
Deep reinforcement learning (DRL) on Markov decision processes (MDPs) with continuous action spaces is often approached by directly training parametric policies along the direction of estimated policy gradients (PGs). Previous research revealed that the performance of these PG algorithms depends heavily on the bias-var…
Study on merging predictors in causal and anticausal directions using CMAXENT.
Symmetric critical points lead to symmetry breaking in neural networks.
In this paper, a similarity-driven cluster merging method is proposed for unsuper-vised fuzzy clustering. The cluster merging method is used to resolve the problem of cluster validation. Starting with an overspecified number of clusters in the data, pairs of similar clusters are merged based on the proposed similarity-…
New method merges MCMC samples without distributional assumptions.
Hierarchical clustering is a popular method for analyzing data which associates a tree to a dataset. Hartigan consistency has been used extensively as a framework to analyze such clustering algorithms from a statistical point of view. Still, as we show in the paper, a tree which is Hartigan consistent with a given dens…
A knot K is called Gordian adjacent to a knot L if there exists an unknotting sequence for L containing K. We provide a sufficient condition for Gordian adjacency of torus knots via the study of knots in the thickened torus. We also completely describe Gordian adjacency for torus knots of index 2 and 3 using Levine-Tri…
Paper determines 2-adjacent knots up to 12 crossings.
Adjacency defined for three-manifolds, linking them to the 3-sphere.
Securely evaluates the benefits of merging datasets for causal estimation.
A new method merges neural networks using CCA to improve model performance.
The problem of clustering noisy and incompletely observed high-dimensional data points into a union of low-dimensional subspaces and a set of outliers is considered. The number of subspaces, their dimensions, and their orientations are assumed unknown. We propose a simple low-complexity subspace clustering algorithm, w…
NAMEx merges experts using Nash bargaining for improved performance.
Proposes CAL to learn causal adjacency for better spatiotemporal prediction.
The paper introduces an adjacency constraint to improve goal-conditioned HRL.
A new method reduces task interference in model merging.
Discrete knot theory models use lattice-filtered graphs to detect merging knot components.
Method constructs finance LLMs without instruction data using pretraining and model merging.
Radar sensors provide a unique method for executing environmental perception tasks towards autonomous driving. Especially their capability to perform well in adverse weather conditions often makes them superior to other sensors such as cameras or lidar. Nevertheless, the high sparsity and low dimensionality of the comm…
Clustering evaluation measures are frequently used to evaluate the performance of algorithms. However, most measures are not properly normalized and ignore some information in the inherent structure of clusterings. We model the relation between two clusterings as a bipartite graph and propose a general component-based …
Graphs represent knot adjacency for n crossings.
Deep learning algorithms for connectomics rely upon localized classification, rather than overall morphology. This leads to a high incidence of erroneously merged objects. Humans, by contrast, can easily detect such errors by acquiring intuition for the correct morphology of objects. Biological neurons have complicated…
Single global merging boosts decentralized learning performance.
Motivation: With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes at single cell resolution and is instrumental to understanding many biological processes. While state-of-the-art clustering methods have been…
Reconstruct spacetime from order and number of points.
Gradient boosted decision trees (GBDT) is the leading algorithm for many commercial and academic data applications. We give a deep analysis of this algorithm, especially the histogram technique, which is a basis for the regulized distribution with compact support. We present three new modifications. 1) Share memory tec…
A knot K is called n-adjacent to the unknot, if K admits a projection containing n generalized crossings such that changing any m (no larger than n) of them yields a projection of the unknot. We show that a non-trivial satellite knot K is n-adjacent to the unknot, for some n>0, if and only if it is n-adjacent to the un…
DPSM clusters nodes in data and graph spaces via density propagation and subcluster merging.
For graphs generated from stochastic blockmodels, adjacency spectral embedding is asymptotically consistent. Further, adjacency spectral embedding composed with universally consistent classifiers is universally consistent to achieve the Bayes error. However when the graph contains private or sensitive information, trea…
Bayesian Federated Inference improves survival model analysis without merging data.
In order to drive safely and efficiently under merging scenarios, autonomous vehicles should be aware of their surroundings and make decisions by interacting with other road participants. Moreover, different strategies should be made when the autonomous vehicle is interacting with drivers having different level of coop…