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.
CSL selects best model from library based on covariates.
Method predicts RMST from censored data using pseudo-observations and super learner.
Super learner uses diverse screeners to improve prediction performance.
A new method improves super learner validation efficiency.
We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learners: the maximum likelihood estimator for the mean of a Gaussian, and the large margin classifier in 1D. For general learners, we provide a …
CVTMLE improves statistical inference in settings of positivity or Donsker class violations.
A new method combines conformal prediction with Super Learner for interval predictions.
Develops a new density ratio estimator for causal inference.
Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, …
Super Learner combines dynamic predictions from various models to improve survival estimates.
Proposes a multi-resolution model for prostate cancer classification using mpMRI.
SLEM uses machine learning to improve causal inference from observational data.
Super learner with Huber loss improves cost prediction and causal effect estimation in healthcare expenditure data.
Proposes a new estimator for weak instrumental variables in panel data models.
POSL predicts dynamic convection volumes in hemodiafiltration patients.
Artificial neural networks have been successfully applied to a variety of machine learning tasks, including image recognition, semantic segmentation, and machine translation. However, few studies fully investigated ensembles of artificial neural networks. In this work, we investigated multiple widely used ensemble meth…
In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organizing distributed teachers to jointly select a compact while informative training subset from data hosted by the teachers to make a learner l…
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a "library" of candidate prediction models. The SL is not restricted to a single prediction model, but uses …
Modern Automatic Speech Recognition (ASR) systems rely on distributed deep learning to for quick training completion. To enable efficient distributed training, it is imperative that the training algorithms can converge with a large mini-batch size. In this work, we discovered that Asynchronous Decentralized Parallel St…
Defines semi-symmetric metric connection on super warped products.
Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here we propose sup…
Solves super-hedging for financial models with uncertain prices.
The paper studies special warped products with a specific connection on super Riemannian manifolds.
Background and objective: Stacking is an ensemble machine learning method that averages predictions from multiple other algorithms, such as generalized linear models and regression trees. An implementation of stacking, called super learning, has been developed as a general approach to supervised learning and has seen f…
Paper introduces GDR-learners for estimating potential outcomes from observational data.
Article proves Liouville theorem for heat equation in super Ricci flow.
VISTA learns causal structures by integrating local subgraphs, improving accuracy and efficiency.
Study Liouville theorems for harmonic maps along ancient super Ricci flows.
Paper develops a continuous-time framework for financial markets without stochastic calculus.
New method for pricing financial products without no-arbitrage condition.
Paper finds solutions to a complex equation on surfaces with boundary conditions.
New formulations for Ricci flows without smoothness.
A super Lie group is a group whose operations are mappings in the sense of Rogers. Thus the underlying supermanifold possesses an atlas whose transition functions are functions. Moreover the images of our charts are open subsets of a graded infinite-dimensional Banach space since our space of …
Investigates VaR behavior for sums of one-sided random variables, showing impossibilities and conditions for super-additivity.
We investigate the concept of projective equivalence of connections in supergeometry. To this aim, we propose a definition for (super) geodesics on a supermanifold in which, as in the classical case, they are the projections of the integral curves of a vector field on the tangent bundle: the geodesic vector field assoc…
We consider a financial market with one riskless and one risky asset. The super-replication theorem states that there is no duality gap in the problem of super-replicating a contingent claim under transaction costs and the associated dual problem. We give two versions of this theorem. The first theorem relates a numéra…
We propose a Fundamental Theorem of Asset Pricing and a Super-Replication Theorem in a model-independent framework. We prove these theorems in the setting of finite, discrete time and a market consisting of a risky asset S as well as options written on this risky asset. As a technical condition, we assume the existence…
Study shows zero-shot super-resolution in neural operators is impossible in many cases.
POSL is an online learning algorithm for personalized predictions.
In the paper, the martingales and super-martingales relative to a convex set of equivalent measures are systematically studied. The notion of local regular super-martingale relative to a convex set of equivalent measures is introduced and the necessary and sufficient conditions of the local regularity of it in the disc…
To generalize the notion of recurrent manifold, there are various recurrent like conditions in the literature. In this paper we present a recurrent like structure, namely, \textit{super generalized recurrent manifold}, which generalizes both the hyper generalized recurrent manifold and weakly generalized recurrent mani…
In an incomplete market the price of a claim f in general cannot be uniquely identified by no arbitrage arguments. However, the ``classical'' super replication price is a sensible indicator of the (maximum selling) value of the claim. When f satisfies certain pointwise conditions (e.g., f is bounded from below), the su…
Theorem ensures superior learning outcomes for authorized learners with quantum label encoding.
We introduce and study a superversion of Dubrovin's notion of semisimple Frobenius manifolds. We establish a correspondence between semisimple Frobenius (super)manifolds and special solutions to the (supersymmetric) Schlesinger equations. Finally, we calculate the Schlesinger initial conditions for solutions describing…
Single-image super-resolution (SISR) is a canonical problem with diverse applications. Leading methods like SRGAN produce images that contain various artifacts, such as high-frequency noise, hallucinated colours and shape distortions, which adversely affect the realism of the result. In this paper, we propose an altern…
EP-learning framework improves causal contrast estimation efficiency.
The object of the present paper is to obtain the characterization of a warped product semi-Riemannian manifold with a special type of recurrent like structure, called super generalized recurrent. As consequence of this result we also find out the necessary and sufficient conditions for a warped product manifold to sati…
In this paper, we prove the characterization of the -super Perelman Ricci flows by various functional inequalities and gradient estimate for the heat semigroup generated by the Witten Laplacian on manifolds equipped with time dependent metrics and potentials. As a byproduct, we derive the Hamilton type dim…