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.
Tool uses text mining to define innovative tech fields from abstracts.
The Gaussian kernel is never positive-definite on Riemannian symmetric spaces.
Defines Learning Analytics' foundational structure and scope.
Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better per…
SCOPE iteratively optimizes sparsity-constrained problems without tuning hyperparameters.
Convolutional neural network improves assertion detection in multi-label clinical text.
A standard model of (conditional) heteroscedasticity, i.e., the phenomenon that the variance of a process changes over time, is the Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, which is especially important for economics and finance. GARCH models are typically estimated by the Quasi-Maximum …
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities. Derivations of general classes of our control-based RL methods are presented, together with form…
This paper develops a general theoretical framework to analyze structured sparse recovery problems using the notation of dual certificate. Although certain aspects of the dual certificate idea have already been used in some previous work, due to the lack of a general and coherent theory, the analysis has so far only be…
Scoping review of EO-ML methods for causal inference in poverty geography.
Scoping review and benchmarking of synthetic EHR data generation methods.
Study finds carbon emissions affect stock value, but not bought emissions.
Two new estimators reduce costs and improve accuracy for EHR outcome prediction.
We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding coefficients exactly equal. This is achieved using the minimax concave penalty on differences between the order statistics of the coefficie…
Discovering novel materials can be greatly accelerated by iterative machine learning-informed proposal of candidates---active learning. However, standard \emph{global-scope error} metrics for model quality are not predictive of discovery performance, and can be misleading. We introduce the notion of \emph{Pareto shell-…
The coeffective differential complex on a symplectic manifold is extended both in length and in scope, unifying the constructions of various other authors.
Study reduces emissions in portfolios with error-prone emissions data.
Clarifies the scope of 'reproducibility' in AI and ML.
New conditions ensure deep neural networks can approximate any function on non-Euclidean spaces.
The small-ball method was introduced as a way of obtaining a high probability, isomorphic lower bound on the quadratic empirical process, under weak assumptions on the indexing class. The key assumption was that class members satisfy a uniform small-ball estimate: that for given const…
In probabilistic approaches to classification and information extraction, one typically builds a statistical model of words under the assumption that future data will exhibit the same regularities as the training data. In many data sets, however, there are scope-limited features whose predictive power is only applicabl…
Mutual independence is a key concept in statistics that characterizes the structural relationships between variables. Existing methods to investigate mutual independence rely on the definition of two competing models, one being nested into the other and used to generate a null distribution for a statistic of interest, …
We study the relationship between firms' performance and their technological portfolios using tools borrowed from the complexity science. In particular, we ask whether the accumulation of knowledge and capabilities related to a coherent set of technologies leads firms to experience advantages in terms of productive eff…
SCOPE estimator improves covariance and precision matrix estimation.
Introduces a new triple coproduct for knots on surfaces, preserving local crossing patterns.
The concept of SCN offers a fast framework with universal approximation guarantee for lifelong learning of non-stationary data streams. Its adaptive scope selection property enables for proper random generation of hidden unit parameters advancing conventional randomized approaches constrained with a fixed scope of rand…
In this paper we characterise the propensity of big capital investments to systematically deliver poor outcomes as "fragility," a notion suggested by Nassim Taleb. A thing or system that is easily harmed by randomness is fragile. We argue that, contrary to their appearance, big capital investments break easily - i.e. d…
ManifoldFlow relaxes fixed-spectrum Stiefel layers to learn a positive spectrum.
SaML guides ML models to avoid survey biases.
BAxUS optimizes high-dimensional functions adaptively, avoiding performance degradation and failure.
We introduce a measure to quantify ambiguity in deep learning models, improving their reliability.
This summarizes the study of the financial and economic crisis in Europe. The starting questions were: 1) Why do we have a crisis? Unde venis? 2) What will be the outcome? Quo vadis? Here is the reasoning which touches many areas, ranging from financial to politics and from psychology and economy.
Method regularizes Cholesky factors to detect nonstationarity in longitudinal data.
This review explores ChatGPT in accounting and finance.
Model estimates non-reported GHG emissions for companies using machine learning.
A new method for efficient causal structure learning at scale.
New method tests risk measures for various distortions.
Distributed sparse learning with a cluster of multiple machines has attracted much attention in machine learning, especially for large-scale applications with high-dimensional data. One popular way to implement sparse learning is to use regularization. In this paper, we propose a novel method, called proximal \mb…
We introduce a Bernstein-type inequality which serves to uniformly control quadratic forms of gaussian variables. The latter can for example be used to derive sharp model selection criteria for linear estimation in linear regression and linear inverse problems via penalization, and we do not exclude that its scope of a…
New results on risk estimation for SVM and related methods.
Let R be an o-minimal expansion of the real field. We introduce a class of Hausdorff limits, the T-infinity limits over R, that do not in general fall under the scope of Marker and Steinhorn's definability-of-types theorem. We prove that if R admits analytic cell decomposition, then every T-infinity limit over R is def…
The appeal of metric evaluation of research impact has attracted considerable interest in recent times. Although the public at large and administrative bodies are much interested in the idea, scientists and other researchers are much more cautious, insisting that metrics are but an auxiliary instrument to the qualitati…
ForesightFlow detects informed trading on prediction markets using an information leakage score.
Developing efficient and scalable algorithms for Latent Dirichlet Allocation (LDA) is of wide interest for many applications. Previous work has developed an O(1) Metropolis-Hastings sampling method for each token. However, the performance is far from being optimal due to random accesses to the parameter matrices and fr…
The paper extends RDPG model to handle weighted graphs, enabling better analysis of network data.
New PCA method for derivatives problems.