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.
MAOP learns object dynamics from raw visual data.
Designs a framework to transfer causal models between similar environments.
SCALOR learns scalable object representations for crowded scenes.
Agents use object-oriented reasoning to solve problems more quickly.
OGRePy simplifies tensor calculations in general relativity.
OGRe simplifies tensor calculations in general relativity.
The Dynamic Chain Event Graph (DCEG) is able to depict many classes of discrete random processes exhibiting asymmetries in their developments and context-specific conditional probabilities structures. However, paradoxically, this very generality has so far frustrated its wide application. So in this paper we develop an…
SPACE models complex scenes by decomposing objects and backgrounds.
Over the past decade, contextual bandit algorithms have been gaining in popularity due to their effectiveness and flexibility in solving sequential decision problems---from online advertising and finance to clinical trial design and personalized medicine. At the same time, there are, as of yet, surprisingly few options…
We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with ti…
DoubleML is a Python library for causal inference using machine learning.
CoDA augments data with counterfactuals from local causal structures.
In this paper, we focus on developing efficient sensitivity analysis methods for a computationally expensive objective function in the case that the minimization of it has just been performed. Here "computationally expensive" means that each of its evaluation takes significant amount of time, and therefore our m…
Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory signals that are difficult to obtain in practice. We present a paradigm for learning object-centric representations for physical scene understan…
DoubleML implements machine learning for causal inference in R.
We study the qualitative and quantitative appearance of stylized facts in several agent-based computational economic market (ABCEM) models. We perform our simulations with the SABCEMM (Simulator for Agent-Based Computational Economic Market Models) tool recently introduced by the authors (Trimborn et al. 2019). Further…
BARMPy offers a Python package for Bayesian Additive Regression Models.
modAL is a modular active learning framework for Python, aimed to make active learning research and practice simpler. Its distinguishing features are (i) clear and modular object oriented design (ii) full compatibility with scikit-learn models and workflows. These features make fast prototyping and easy extensibility p…
Braidlab is a Matlab package for analyzing data using braids. It was designed to be fast, so it can be used on relatively large problems. It uses the object-oriented features of Matlab to provide a class for braids on punctured disks and a class for equivalence classes of simple closed loops. The growth of loops under …
This paper introduces an agent-based artificial financial market in which heterogeneous agents trade one single asset through a realistic trading mechanism for price formation. Agents are initially endowed with a finite amount of cash and a given finite portfolio of assets. There is no money-creation process; the total…
Pipeline for comparing trading algorithms in finance and crypto.
Improved model error correction online with neural networks in 4D-Var.
Hybrid model improves weather forecasting accuracy.
Python tool assesses European agricultural production resilience.
MRCpy implements minimax risk classifiers with performance guarantees and distribution shift adaptability.
This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of w…
Geomstats introduces shape module for analyzing shapes of objects.
This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed wi…
We introduce the simulation tool SABCEMM (Simulator for Agent-Based Computational Economic Market Models) for agent-based computational economic market (ABCEM) models. Our simulation tool is implemented in C++ and we can easily run ABCEM models with several million agents. The object-oriented software design enables th…
Training 3D object detectors for autonomous driving has been limited to small datasets due to the effort required to generate annotations. Reducing both task complexity and the amount of task switching done by annotators is key to reducing the effort and time required to generate 3D bounding box annotations. This paper…
PyHHMM is a Python library for HHMMs with advanced features.
GP+ is a Python library for Gaussian process learning.
ODTLearn learns optimal decision trees for predictive and prescriptive tasks.
While developing their software, professional object-oriented (OO) software developers keep in their minds an image of the subtyping relation between types in their software. The goal of this paper is to present an observation about the graph of the subtyping relation in Java, namely the observation that, after the add…
StepMix estimates mixture models with covariates for social science applications.
Software automates metabolomics data analysis for reproducible results.
tf_geometric simplifies graph deep learning in TensorFlow.
Human visual object recognition is typically rapid and seemingly effortless, as well as largely independent of viewpoint and object orientation. Until very recently, animate visual systems were the only ones capable of this remarkable computational feat. This has changed with the rise of a class of computer vision algo…
Perceptual manifolds arise when a neural population responds to an ensemble of sensory signals associated with different physical features (e.g., orientation, pose, scale, location, and intensity) of the same perceptual object. Object recognition and discrimination requires classifying the manifolds in a manner that is…
FSN model improves few-shot learning by generalizing to new tasks.
This work develops fast and accurate ROMs for AM models using OL methods.
Paper shows leafwise cohomological expression for dynamical zeta functions.
Study on 2-valued dynamics on complex plane, showing some dynamics can't be group actions.
The paper studies dynamic star-shaped risk measures and their representation.
Study circles to understand dynamics and rigidity in homogeneous spaces.
Paper connects dynamics of mechanical systems to Reeb dynamics.
Two heuristics solve dynamic multiple travelling salesmen problems.
In this paper we present a theoretical framework for studying coherent acceptability indices in a dynamic setup. We study dynamic coherent acceptability indices and dynamic coherent risk measures, and we establish a duality between them. We derive a representation theorem for dynamic coherent risk measures in terms of …