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.
The paper examines utility maximization in markets with hidden Gaussian drift, finding restrictions on model parameters.
The maximum entropy principle can be used to assign utility values when only partial information is available about the decision maker's preferences. In order to obtain such utility values it is necessary to establish an analogy between probability and utility through the notion of a utility density function. According…
Random utility theory models an agent's preferences on alternatives by drawing a real-valued score on each alternative (typically independently) from a parameterized distribution, and then ranking the alternatives according to scores. A special case that has received significant attention is the Plackett-Luce model, fo…
GBC methods compute expected utility without needing the model's density.
Optimizes portfolios with utility theory, diversification, and leverage.
Paper finds a new principle for optimizing consumption and wealth using Tsallis entropy.
Directly proves Li-Yau estimates on manifolds with negative Ricci curvature.
We apply the maximum entropy principle to economic systems in equilibrium and find the density function for the market's wealth. This is the same as price density which is used for insurance pricing. The risk aversion parameter of the agent then it's utility function with respect to this density is derived.
Data-driven anomaly detection methods suffer from the drawback of detecting all instances that are statistically rare, irrespective of whether the detected instances have real-world significance or not. In this paper, we are interested in the problem of specifically detecting anomalous instances that are known to have …
The paper calculates the value of information in high-dimensional decision making.
Theoretical limits on verifying self-improving systems without risking unbounded utility.
The paper analyzes optimal consumption with past spending maximum as a reference.
Develops deep learning methods for solving S-shaped utility maximisation problems.
We consider market players with tail-risk-seeking behaviour as exemplified by the S-shaped utility introduced by Kahneman and Tversky. We argue that risk measures such as value at risk (VaR) and expected shortfall (ES) are ineffective in constraining such players. We show that, in many standard market models, product d…
Model captures decision-making under bounded rationality with prior beliefs and market feedback.
Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.
Study optimal consumption for loss-averse agents considering past spending peaks.
In this paper we present a proof of a Neumann type maximum principle for the Laplace operator on compact Riemannian manifolds. A key p oint is the simple geometric nature of the constant in the a priori estimate of this maximum principle. In particular, this maximum principle can be applied to manifolds with Ricci curv…
The paper tackles optimal policy learning with asymmetric counterfactual utilities in healthcare decisions.
We consider a financial market model with a single risky asset whose price process evolves according to a general jump-diffusion with locally bounded coefficients and where market participants have only access to a partial information flow. For any utility function, we prove that the partial information financial marke…
Paper extends RUMs with features to handle incomplete preferences and proves identifiability.
In this paper we derive the maximum entropy characteristics of a particular rank order distribution, namely the discrete generalized beta distribution, which has recently been observed to be extremely useful in modelling many several rank-size distributions from different context in Arts and Sciences, as a two-paramete…
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents. Existing models apply the likelihood-based estimation. In this paper, we present a general framework of max-margin supervised topic models for both continuous and categorical response var…
We consider expected utility maximisation problem for exponential Levy models and HARA utilities in presence of illiquid asset in portfolio. This illiquid asset is modelled by an option of European type on another risky asset which is correlated with the first one. Under some hypothesis on Levy processes, we give the e…
Unified meta algorithms estimate various distribution functionals in infinite-armed bandits.
The maximum number of maximum cliques in a graph is determined for graphs with at least 15 vertices.
New method for sequential probability assignment reduces regret using contextual Shtarkov sums.
In this paper, we look for properties of gradient Yamabe solitons on top of warped product manifolds. Utilizing the maximum principle, we find lower bound estimates for both the potential function of the soliton and the scalar curvature of the warped product. By slightly modifying Li-Yau's technique so that we can hand…
Efficiently estimates GEV distribution parameters using neural networks.
New defense method inspired by encryption improves visual classification accuracy.
We study how to communicate findings of Bayesian inference to third parties, while preserving the strong guarantee of differential privacy. Our main contributions are four different algorithms for private Bayesian inference on proba-bilistic graphical models. These include two mechanisms for adding noise to the Bayesia…
The paper calculates genus bounds for multibranched surfaces.
A framework is introduced for actively and adaptively solving a sequence of machine learning problems, which are changing in bounded manner from one time step to the next. An algorithm is developed that actively queries the labels of the most informative samples from an unlabeled data pool, and that adapts to the chang…
Chow and Liu (1968) studied the problem of learning a maximumlikelihood Markov tree. We generalize their work to more complexMarkov networks by considering the problem of learning a maximumlikelihood Markov network of bounded complexity. We discuss howtree-width is in many ways the appropriate measure of complexity and…
We prove non-asymptotic lower bounds on the expectation of the maximum of independent Gaussian variables and the expectation of the maximum of independent symmetric random walks. Both lower bounds recover the optimal leading constant in the limit. A simple application of the lower bound for random walks is an (…
Paper proposes methods to localize sources in WSNs without knowing sensor parameters.
We introduce the concept of singular recursive utility. This leads to a kind of singular BSDE which, to the best of our knowledge, has not been studied before. We show conditions for existence and uniqueness of a solution for this kind of singular BSDE. Furthermore, we analyze the problem of maximizing the singular rec…
The study bounds the utility of empirically optimal portfolios using stock return data.
In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. The fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the…
Clustering stocks reduces estimation error in global minimum variance portfolio.
Lower bounds for eigenvalues on Bakry-Emery manifolds proven.
Study Epstein-Zin preferences in mean field portfolio games, proving unique equilibria.
Paper proposes a new UCB approach for estimating maximum mean.
Proposes a max-utility arm selection strategy for reducing cumulative regret in sequential query recommendations.
New bound on Jones polynomial for specific positive links.
Paper quantifies label shift robustly.
Study shows how over-parameterized classifiers can still perform well on noisy data.
New algorithm solves utility maximization with deep learning for constrained problems.