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.
Study identifies biomarkers for lung cancer in female non-smokers.
In this paper is proposed a kind of model theory for our axiomatic differential geometry. It is claimed that smooth manifolds, which have occupied the center stage in differential geometry, should be replaced by functors on the category of Weil algebras. Our model theory is geometrically natural and conceptually motiva…
In the monitoring of a complex electric grid, it is of paramount importance to provide operators with early warnings of anomalies detected on the network, along with a precise classification and diagnosis of the specific fault type. In this paper, we propose a novel multi-stage early warning system prototype for electr…
Market-GAN adds context control to financial market data generation.
Algorithmic decision making process now affects many aspects of our lives. Standard tools for machine learning, such as classification and regression, are subject to the bias in data, and thus direct application of such off-the-shelf tools could lead to a specific group being unfairly discriminated. Removing sensitive …
Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is hig…
Learning to classify unseen class samples at test time is popularly referred to as zero-shot learning (ZSL). If test samples can be from training (seen) as well as unseen classes, it is a more challenging problem due to the existence of strong bias towards seen classes. This problem is generally known as \emph{generali…
Study robustness of split conformal prediction under adversarial attacks.
Dynamic treatment strategies on networks amplify policy impact through spillovers.
EarnHFT tackles HFT challenges with hierarchical RL, significantly outperforming existing methods.
Formulates Index III lemma and Rauch III theorem with applications.
Optimal CATE estimation with structured contrast functions using KRR.
Study re-evaluates MIMIC-III codes, finding many are under-coded.
This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the correspondin…
Calculates lower bounds for type III Reidemeister moves in link diagrams.
For later use in subsequent upcoming arxiv.org prepublications, basic foundational material on local, smooth or real analytic, CR-generic submanifolds of complex Euclidean spaces is developed from scratch, with strong emphasis on the interplay between extrinsic and intrinsic aspects, a constructive option that commands…
The connection between differential geometry of curves and the (2+1)-dimensional integrable spin system - the M-III equation is established. Using the presented geometrical formalism the L-equivalent counterpart of the M-III equation is found.
This paper investigates teacher hacking during language model distillation and proposes methods to mitigate it.
This research analyzes deep PDE solvers for option pricing accuracy.
Improves classifier performance in multi-stage selection processes.
A new conformal prediction framework for two-stage models identifies stage-wise uncertainty.
The six nondegeneracy conditions of geometric nature that are satisfied by the only six possibly existing nondegenerate general classes I, II, III-1, III-2, IV-1, IV-2 of 5-dimensional CR manifolds are shown to be readable instantaneously from their elementarily normalized respective defining graphed equations, without…
We study the number of Reidemeister type III moves using Fox n-colorings of knot diagrams.
To integrate strategic, tactical and operational decisions, the two-stage optimization has been widely used to guide dynamic decision making. In this paper, we study the two-stage stochastic programming for complex systems with unknown response estimated by simulation. We introduce the global-local metamodel assisted t…
Dual-stage sEMG classification improves gesture recognition accuracy.
Improves classifier performance in multi-stage processes with adversarial autoencoders and multi-task learning.
Although stochastic gradient descent (SGD) method and its variants (e.g., stochastic momentum methods, AdaGrad) are the choice of algorithms for solving non-convex problems (especially deep learning), there still remain big gaps between the theory and the practice with many questions unresolved. For example, there is s…
New algorithm efficiently learns sparse staged trees.
MARCD uses generative scenarios to improve portfolio decisions during regime shifts.
Cross-validation is the de facto standard for predictive model evaluation and selection. In proper use, it provides an unbiased estimate of a model's predictive performance. However, data sets often undergo various forms of data-dependent preprocessing, such as mean-centering, rescaling, dimensionality reduction, and o…
SAMoSSA combines mSSA and AR for accurate time series analysis.
This paper explores the following question: what kind of statistical guarantees can be given when doing variable selection in high-dimensional models? In particular, we look at the error rates and power of some multi-stage regression methods. In the first stage we fit a set of candidate models. In the second stage we s…
Motivation: Radiomics refers to the high-throughput mining of quantitative features from radiographic images. It is a promising field in that it may provide a non-invasive solution for screening and classification. Standard machine learning classification and feature selection techniques, however, tend to display infer…
Proposes methods for learning optimal dynamic treatment regimes robust to unconfoundedness violations.
In this paper, we consider multi-stage stochastic optimization problems with convex objectives and conic constraints at each stage. We present a new stochastic first-order method, namely the dynamic stochastic approximation (DSA) algorithm, for solving these types of stochastic optimization problems. We show that DSA c…
Objectives: Most cancer data sources lack information on metastatic recurrence. Electronic medical records (EMRs) and population-based cancer registries contain complementary information on cancer treatment and outcomes, yet are rarely used synergistically. To enable detection of metastatic breast cancer (MBC), we appl…
New algorithms learn staged trees from incomplete data.
We consider ruled and quadric surfaces in the 3-dimensional Euclidean space which are of coordinate finite type with respect to the third fundamental form , i.e., their position vector satisfies the relation where is a square matrix o…
In many classification systems, sensing modalities have different acquisition costs. It is often {\it unnecessary} to use every modality to classify a majority of examples. We study a multi-stage system in a prediction time cost reduction setting, where the full data is available for training, but for a test example, m…
CRUMB: Efficient Prior Fitted Network Inference via Distributionally Matched Context Batching
Machine learning can improve 2SLS first stage predictions, but nonlinear methods often introduce bias.
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional i…
We consider two stage estimation with a non-parametric first stage and a generalized method of moments second stage, in a simpler setting than (Chernozhukov et al. 2016). We give an alternative proof of the theorem given in (Chernozhukov et al. 2016) that orthogonal second stage moments, sample splitting and -…
CycleFQI tackles offline reinforcement learning for cyclic MDPs, mitigating state distribution mismatch.
Two-stage framework detects multi-modal outliers.
The rise of algorithmic decision making led to active researches on how to define and guarantee fairness, mostly focusing on one-shot decision making. In several important applications such as hiring, however, decisions are made in multiple stage with additional information at each stage. In such cases, fairness issues…
New sequences prove some link diagrams can't be transformed by specific moves.
The main purpose of the paper is to investigate Killing vector field on the tangent bundle T(M_{n}) of the Riemannian manifold with respect to the Levi-Civita connection of the metric II+III .