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.
Develops method to train classifiers on incomplete feature datasets.
Paper extends RUMs with features to handle incomplete preferences and proves identifiability.
Paper proposes a tensor data model for incomplete imaging data.
This paper improves conditional multidimensional scaling for incomplete data.
New method discovers causal structures from incomplete data.
GapNet uses incomplete datasets to train neural networks, improving disease detection.
A new method uses RBMs to handle incomplete multi-output data.
MGMC method handles missing data in medical datasets for accurate disease classification.
We study the problem of learning influence functions under incomplete observations of node activations. Incomplete observations are a major concern as most (online and real-world) social networks are not fully observable. We establish both proper and improper PAC learnability of influence functions under randomly missi…
Learning with feature evolution studies the scenario where the features of the data streams can evolve, i.e., old features vanish and new features emerge. Its goal is to keep the model always performing well even when the features happen to evolve. To tackle this problem, canonical methods assume that the old features …
We construct kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data i…
Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensiona…
Clinical diagnostic decision making and population-based studies often rely on multi-modal data which is noisy and incomplete. Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal …
Nowadays, multi-view clustering has attracted more and more attention. To date, almost all the previous studies assume that views are complete. However, in reality, it is often the case that each view may contain some missing instances. Such incompleteness makes it impossible to directly use traditional multi-view clus…
Proposes CRG_IMSC for better clustering of multi-view data.
Paper compares hard and soft EM for BN learning from incomplete data.
A federated model predicts failures using multi-stream incomplete data.
Latent diffusion improves robustness in missing data imputation.
Recently, incomplete-market techniques have been used to develop a model applicable to credit default swaps (CDSs) with results obtained that are quite different from those obtained using the market-standard model. This article makes use of the new incomplete-market model to further study CDS hedging and extends the mo…
It is always demanding to learn robust visual representation for various learning problems; however, this learning and maintenance process usually suffers from noise, incompleteness or knowledge domain mismatch. Thus, robust representation learning by removing noisy features or samples, complementing incomplete data, a…
We prove existence and uniqueness of stochastic equilibria in a class of incomplete continuous-time financial environments where the market participants are exponential utility maximizers with heterogeneous risk-aversion coefficients and general Markovian random endowments. The incompleteness featured in our setting - …
New method clusters strong and weak views effectively, improving performance by up to 40%.
Framework for imputing missing heart data to simulate brain-heart interactions.
The noncompact Yamabe flow can lead to incomplete metrics over infinite time.
Deep model predicts wildfire data in incomplete regions.
New flow preserves singularities on incomplete manifolds.
Study optimal control strategy for hedge funds managers with PSAHARA utility family.
The possibility of statistical evaluation of the market completeness and incompleteness is investigated for continuous time diffusion stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one.…
We adress the maximization problem of expected utility from terminal wealth. The special feature of this paper is that we consider a financial market where the price process of risky assets can have a default time. Using dynamic programming, we characterize the value function with a backward stochastic differential equ…
Develops GNNs for incomplete graphs, improving learning from missing node attributes.
In order to find a way of measuring the degree of incompleteness of an incomplete financial market, the rank of the vector price process of the traded assets and the dimension of the associated acceptance set are introduced. We show that they are equal and state a variety of consequences.
Survey of analytic and geometric results on fibred cusp spaces.
This paper solves hedging in incomplete markets using neural networks.
Efficiently clusters incomplete data without imputation or full EM, faster and more accurate.
We investigate the possibility of statistical evaluation of the market completeness for discrete time stock market models. It is known that the market completeness is not a robust property: small random deviations of the coefficients convert a complete market model into a incomplete one. The paper shows that market inc…
We consider the problem of optimal consumption of multiple goods in incomplete semimartingale markets. We formulate the dual problem and identify conditions that allow for existence and uniqueness of the solution and give a characterization of the optimal consumption strategy in terms of the dual optimizer. We illustra…
In the setting of exponential investors and uncertainty governed by Brownian motions we first prove the existence of an incomplete equilibrium for a general class of models. We then introduce a tractable class of exponential-quadratic models and prove that the corresponding incomplete equilibrium is characterized by a …
Study Dirac operators on incomplete cusp edge spaces, proving self-adjointness and Fredholm properties.
Algorithm recovers sparse PCA support from incomplete data.
New ML method detects incomplete bid-rigging cartels.
Framework reconstructs missing spatio-temporal data for extreme value prediction.
We show that when the price process represents a fully incomplete market, the optimal super-replication of any Markovian claim with being nonnegative and lower semicontinuous is of buy-and-hold type. Since both (unbounded) stochastic volatility models and rough volatility models are examples of …
The paper extends cost-efficiency analysis to incomplete markets.
New estimator for symmetric kernel expectations, robust to missing data.
We develop a technique based on Malliavin-Bismut calculus ideas, for asymptotic expansion of dual control problems arising in connection with exponential indifference valuation of claims, and with minimisation of relative entropy, in incomplete markets. The problems involve optimisation of a functional of Brownian path…
Study optimal investment and consumption in incomplete markets with nonlinear expectations.
Improved VAE estimation from incomplete data using variational mixtures.
Motivated by recent interest in the spectrum of the Laplacian of incomplete surfaces with isolated conical singularities, we consider more general incomplete m-dimensional manifolds with singularities on sets of codimension at least 2. With certain restrictions on the metric, we establish that the spectrum is discrete …