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.
Proposes adaptive method for classifying interval-valued time series.
DeepIFSAC uses attention mechanisms and contrastive learning to impute missing values in tabular data.
New method calculates Shapley values for uncertain functions.
IFGAN uses feature-specific GANs for missing value imputation.
This work optimizes bid strategies for online auctions using measure-valued optimization.
Value selection reduces model size while maintaining accuracy.
Proposes a normalization technique for manifold valued data.
Proposes a method to create fair ITRs that balance value and fairness.
Developing an explainable outlier detection method for interval-valued data using Shapley value-based approach.
The paper introduces localized conformal p-values for conditional testing problems.
This paper proposes an efficient method for calculating Shapley values in Naive Bayes classifiers.
We propose a differentiable sigmoid function for efficient p-value calculation in clustering.
Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points based on some similarity criterion. A c…
The true probability of a European call option to achieve positive return is investigated under the Black-Scholes model. It is found that the probability is determined by those market factors appearing in the BS formula, besides the growth rate of stock price. Our numerical investigations indicate that the biases of BS…
Value iteration is a fixed point iteration technique utilized to obtain the optimal value function and policy in a discounted reward Markov Decision Process (MDP). Here, a contraction operator is constructed and applied repeatedly to arrive at the optimal solution. Value iteration is a first order method and therefore …
Policy evaluation is a key process in reinforcement learning. It assesses a given policy using estimation of the corresponding value function. When using a parameterized function to approximate the value, it is common to optimize the set of parameters by minimizing the sum of squared Bellman Temporal Differences errors…
Enhances data valuation by integrating global and local statistical properties.
Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper is to construct confidence intervals…
A novel model combines deep learning and extreme value theory for multivariate cyber risk prediction.
In this study, we propose a new definition of multivariate conditional value-at-risk (MCVaR) as a set of vectors for discrete probability spaces. We explore the properties of the vector-valued MCVaR (VMCVaR) and show the advantages of VMCVaR over the existing definitions given for continuous random variables when adapt…
Proposes a new method to avoid model extrapolation in Shapley values.
This paper proposes a new approach to RL by focusing on the value-improvement path.
New kernels capture both local and non-local interactions efficiently.
In a discounted reward Markov Decision Process (MDP), the objective is to find the optimal value function, i.e., the value function corresponding to an optimal policy. This problem reduces to solving a functional equation known as the Bellman equation and a fixed point iteration scheme known as the value iteration is u…
We study the dynamics of exchange value in a system composed of many interacting agents. The simple model we propose exhibits cooperative emergence and collapse of global value for individual goods. We demonstrate that the demand that drives the value exhibits non Gaussian "fat tails" and typical fluctuations which gro…
In medical domain, data features often contain missing values. This can create serious bias in the predictive modeling. Typical standard data mining methods often produce poor performance measures. In this paper, we propose a new method to simultaneously classify large datasets and reduce the effects of missing values.…
Proposes a low-cost method to set hyperparameters using optimized default values.
Quantum SVT reduces credit risk analysis costs.
Reinforcement learning algorithms such as the deep deterministic policy gradient algorithm (DDPG) has been widely used in continuous control tasks. However, the model-free DDPG algorithm suffers from high sample complexity. In this paper we consider the deterministic value gradients to improve the sample efficiency of …
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
This paper studies a recent proposal to use randomized value functions to drive exploration in reinforcement learning. These randomized value functions are generated by injecting random noise into the training data, making the approach compatible with many popular methods for estimating parameterized value functions. B…
Impact of chosen behavioural factors on imprecision of present value is discussed here. The formal model of behavioural present value is offered as a result of this discussion. Behavioural present value is described here by fuzzy set. These considerations were illustrated by means of extensive numerical case study. Fin…
New bandit algorithms focus on extreme values, outperforming existing methods.
The paper studies OPE with missing data, showing bias under nonignorable missingness and proposing a solution.
Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several benefits. However, it does not satisfy the non-expansion property, and its direct use may fail to converge even in value iteration. In this pape…
Bayesian approach improves Shapley value estimation efficiency.
Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with…
We introduce a new method to explain Gaussian processes using Shapley values.
New kernels allow learning from non-separable data.
Paper develops a new test for high-dimensional matrix-valued data.
Develops intrinsic Gaussian process regression for manifold-valued data.
The problem of explaining the behavior of deep neural networks has recently gained a lot of attention. While several attribution methods have been proposed, most come without strong theoretical foundations, which raises questions about their reliability. On the other hand, the literature on cooperative game theory sugg…
Proposes a value-based method for continuous control without an actor.
The uncertainty or the variability of the data may be treated by considering, rather than a single value for each data, the interval of values in which it may fall. This paper studies the derivation of basic description statistics for interval-valued datasets. We propose a geometrical approach in the determination of s…
Q-Distribution Guided Q-Learning corrects overestimation of uncertain OOD actions in offline RL.
PolarBM models complex-valued audio signals in polar coordinates, improving over conventional methods.
"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the…
Paper improves conformal prediction for imprecise training data.