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.
KD-Net transfers knowledge from multi-modal to mono-modal segmentation networks.
Mixtures of neural operators reduce active complexity in operator learning.
In this paper we study BSE Index financial time series for fractal and multifractal behaviour. We show that Bombay stock Exchange (BSE)Index time series is mono-fractal and can be represented by a fractional Brownian motion.
Characterizes super-replication prices in a financial market model.
The performance of the multifractal detrended analysis on short time series is evaluated for synthetic samples of several mono- and multifractal models. The reconstruction of the generalized Hurst exponents is used to determine the range of applicability of the method and the precision of its results as a function of t…
We review ideas on temporal dependences and recurrences in discrete time series from several areas of natural and social sciences. We revisit existing studies and redefine the relevant observables in the language of copulas (joint laws of the ranks). We propose that copulas provide an appropriate mathematical framework…
A new method for incorporating preferences in multi-objective Bayesian optimization.
This paper presents a spermwhale' localization architecture using jointly a bag-of-features (BoF) approach and machine learning framework. BoF methods are known, especially in computer vision, to produce from a collection of local features a global representation invariant to principal signal transformations. Our idea …
In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from [Chen2018Online] and [chen2018projection] to 1, and achieves a -regret bound of . The second one, Bandit-F…
fMRI is a unique non-invasive approach for understanding the functional organization of the human brain, and task-based fMRI promotes identification of functionally relevant brain regions associated with a given task. Here, we use fMRI (using the Poffenberger Paradigm) data collected in mono- and dizygotic twin pairs t…
Rough volatility is a well-established statistical stylised fact of financial assets. This property has lead to the design and analysis of various new rough stochastic volatility models. However, most of these developments have been carried out in the mono-asset case. In this work, we show that some specific multivaria…
Multifractal time series analysis is a approach that shows the possible complexity of the system. Nowadays, one of the most popular and the best methods for determining multifractal characteristics is Multifractal Detrended Fluctuation Analysis (MFDFA). However, it has some drawback. One of its core elements is detrend…
The paper explores centroids and static equilibrium points in non-Euclidean geometries.
A financial system contains many elements networked by their relationships. Extensive works show that topological structure of the network stores rich information on evolutionary behaviors of the system such as early warning signals of collapses and/or crises. Existing works focus mainly on the network structure within…
A scalable portfolio approach speeds up Bayesian optimization for noisy functions.
Study examines bias in language models across multiple languages.
A deep probabilistic model analyzes DNA-encoded library data for efficient screening.
The paper proposes a new method for density estimation using spline quasi-interpolation for clustering.
MBORE optimizes multi-objective problems using density-ratio estimation.
Develops monotone tree-based GAMI models using XGBoost.
New algorithm for decomposing multidimensional, non-stationary signals.
CHILI datasets tackle inorganic nanomaterials, advancing graph machine learning.