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.
FinGPT is an open-source financial LLM for democratizing financial data.
In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solutio…
Study shows non-wandering, partially hyperbolic systems are ergodic.
OpenML-Python API simplifies access to OpenML for Python users.
The abstract discusses open data resources for studying and controlling the spread of COVID-19.
Study creates open-access wildfire dataset for Russia.
Hierarchical NMF organizes COVID-19 literature into a searchable tree.
FinRobot opens-source AI for financial tasks, breaking down complex problems.
We consider the problem of online planning in a Markov Decision Process when given only access to a generative model, restricted to open-loop policies - i.e. sequences of actions - and under budget constraint. In this setting, the Open-Loop Optimistic Planning (OLOP) algorithm enjoys good theoretical guarantees but is …
Open markets are a subset of equity markets with fixed top stocks, changing over time.
Survey examines challenges and tools for integrating multiple types of omics data.
ModHiFi identifies critical components for model modification without gradients or loss function.
In earlier studies, the estimation of the volatility of a stock using information on the daily opening, closing, high and low prices has been developed; the additional information in the high and low prices can be incorporated to produce unbiased (or near-unbiased) estimators with substantially lower variance than the …
Let be a topological space, -- opened subset of . We will say that point is {\it accessible} from if there exists continuous injective mapping $φ: I \to \Cl D$ such that , $φ([0,1)) \subset \Int U$. We proove the next main theorem. The following conditions are neccesary and suf…
Explains biharmonic and biconservative submanifolds for beginners.
Generative models accelerate molecular dynamics by four orders of magnitude.
We study exotic smoothings of open 4-manifolds using the minimal genus function and its analog for end homology. While traditional techniques in open 4-manifold smoothing theory give no control of minimal genera, we make progress by using the adjunction inequality for Stein surfaces. Smoothings can be constructed with …
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes it challenging for non-technical collaborators and endpoint users (e.g. physicians) to easily provide feedback on model devel…
Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature is increasing, the importance and role of the PL field becomes central within machine learning research and practice. This paper introduces an open source, scalable…
Paper discusses ethical norms for machine learning to prevent misuse.
Proposes using probabilistic models for privacy-preserving synthetic data.
Robust machine learning relies on access to data that can be used with standardized frameworks in important tasks and the ability to develop models whose performance can be reasonably reproduced. In machine learning for healthcare, the community faces reproducibility challenges due to a lack of publicly accessible data…
Study parallel waves in spacetimes, focusing on causality and open questions.
Gittins indices provide an optimal solution to the classical multi-armed bandit problem. An obstacle to their use has been the common perception that their computation is very difficult. This paper demonstrates an accessible general methodology for the calculating Gittins indices for the multi-armed bandit with a detai…
3D dataset for intracranial aneurysms aids deep learning applications.
We study the optimal trading policies for a wind energy producer who aims to sell the future production in the open forward, spot, intraday and adjustment markets, and who has access to imperfect dynamically updated forecasts of the future production. We construct a stochastic model for the forecast evolution and deter…
Deep learning applied to biological data mining.
The explosion in workload complexity and the recent slow-down in Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use recent advances in machine learning in software optimizations, augmenting or replacing traditional heuristics and data structures. However, the s…
Query access significantly speeds up learning Multi-Index Models under Gaussian distribution.
PyPOTS simplifies machine learning on time series with missing data.
Book introduces principles of LCK geometry for complex manifold students.
Study benchmarks embedding-based entity alignment methods for KGs.
Survey of methods for learning from observation without requiring expert actions.
Cockpit provides tools for debugging deep neural networks during training.
Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov rand…
Myanmar is languishing at the bottom of key international indexes. United Nations considers the country as a structurally weak and vulnerable economy. Yet, from 2011 when Myanmar ended decades of military rule and isolationism and transited towards democracy, its breakneck development has led to many considering the co…
New sampling and identity-testing methods for mixtures of distributions that don't satisfy approximate tensorization of entropy.
TrueLearn Python library for personalized educational recommendations.
Open-source Vizier optimizes complex systems for Google and beyond.
Generative model learns functional vector fields for pharmacokinetics.
PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without movi…
Optimal DP model training with public data improves privacy and accuracy.
GPI uses GenAI models to infer causal and predictive effects from unstructured data.
PyOD is an open-source Python toolbox for performing scalable outlier detection on multivariate data. Uniquely, it provides access to a wide range of outlier detection algorithms, including established outlier ensembles and more recent neural network-based approaches, under a single, well-documented API designed for us…
We explicitly test if the reliability of credit ratings depends on the total number of admissible states. We analyse open access credit rating data and show that the effect of the number of states in the dynamical properties of ratings change with time, thus giving supportive evidence that the ideal number of admissibl…
Success stories of applied machine learning can be traced back to the datasets and environments that were put forward as challenges for the community. The challenge that the community sets as a benchmark is usually the challenge that the community eventually solves. The ultimate challenge of reinforcement learning rese…
We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody's and show that the validity of these assumptions - existence of a homogeneous genera…
Automated machine learning (AutoML) aims to find optimal machine learning solutions automatically given a machine learning problem. It could release the burden of data scientists from the multifarious manual tuning process and enable the access of domain experts to the off-the-shelf machine learning solutions without e…