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 reduces redundant information in multi-modal datasets.
BALLAST optimizes Lagrangian observer placement for ocean vector fields.
We simplify information measure computation using learned features.
An asymmetric information model is introduced for the situation in which there is a small agent who is more susceptible to the flow of information in the market than the general market participant, and who tries to implement strategies based on the additional information. In this model market participants have access t…
We study a simple model of an asset market with informed and non-informed agents. In the absence of non-informed agents, the market becomes information efficient when the number of traders with different private information is large enough. Upon introducing non-informed agents, we find that the latter contribute signif…
New method quantifies redundant information using information bottleneck.
We propose a new framework for reasoning about information in complex systems. Our foundation is based on a variational extension of Shannon's information theory that takes into account the modeling power and computational constraints of the observer. The resulting \emph{predictive -information} encompasse…
This paper reviews information theory in open-world machine learning.
Review of information plane analyses in neural networks, highlighting mixed results and methodological challenges.
Information geometry offers new tools for statistical analysis.
Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome's plausibility. Information measures based on Shannon's concept of entropy include realization information, Kullback-Leibler divergenc…
In this paper, we present a new approach to interpret deep learning models. By coupling mutual information with network science, we explore how information flows through feedforward networks. We show that efficiently approximating mutual information allows us to create an information measure that quantifies how much in…
Before the massive spread of computer technology, information was far from complex. The development of technology shifted the paradigm: from individuals who faced scarce and costly information to individuals who face massive amounts of information accessible at low costs. Nowadays we are living in the era of big data a…
In financial markets valuable information is rarely circulated homogeneously, because of time required for information to spread. However, advances in communication technology means that the 'lifetime' of important information is typically short. Hence, viewed as a tradable asset, information shares the characteristics…
Introduces relative information gain for improving Gaussian process regression rates.
This paper uses information theory to improve risk modeling in big data.
In information theory, Fisher information and Shannon information (entropy) are respectively used to quantify the uncertainty associated with the distribution modeling and the uncertainty in specifying the outcome of given variables. These two quantities are complementary and are jointly applied to information behavior…
Investors pay for additional asset information based on utility maximization.
New method detects information leakage using approximate Bayes predictor.
Market strategies minimize Fisher information to minimize risk.
New approach quantifies overfitting in high-dimensional regression.
Unified notation simplifies information-theoretic concepts in machine learning.
Study optimal portfolios for traders with asymmetric information and delay.
Strategic brokers exploit private information in broker-mediated markets, affecting informed traders' performance.
Proposes a new method to enhance neural learning by maximizing information gain.
Mirror descent linked to information ratio via Bayesian regret bounds.
Study shows changes in information sharing between Bitcoin markets during 2017 crash.
Study on optimal information acquisition in Kyle model with entropy cost.
Study on information evolution in interactive decision making using multi-armed bandits.
Study reveals mutual information is crucial for understanding algorithm performance in stochastic convex optimization.
We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders' private signals. We consider the design of markets that are always informative, markets that are …
The paper proposes methods to extract and analyze individual variable information from complex dependencies.
There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it. The second, Shannon information…
We model continuous-time information flows generated by a number of information sources that switch on and off at random times. By modulating a multi-dimensional Lévy random bridge over a random point field, our framework relates the discovery of relevant new information sources to jumps in conditional expectation mart…
Identifying features that leak information about sensitive attributes is a key challenge in the design of information obfuscation mechanisms. In this paper, we propose a framework to identify information-leaking features via information density estimation. Here, features whose information densities exceed a pre-defined…
Proposes indifference pricing to estimate weak information value.
The background for the general mathematical link between utility and information theory investigated in this paper is a simple financial market model with two kinds of small traders: less informed traders and insiders, whose extra information is represented by an enlargement of the other agents' filtration. The expecte…
The paper introduces submodular information measures for machine learning applications.
In modern recommender systems, both users and items are associated with rich side information, which can help understand users and items. Such information is typically heterogeneous and can be roughly categorized into flat and hierarchical side information. While side information has been proved to be valuable, the maj…
This study analyzes information flow networks in Chinese stock sectors using transfer entropy.
In this communication, we describe some interrelations between generalized -entropies and a generalized version of Fisher information. In information theory, the de Bruijn identity links the Fisher information and the derivative of the entropy. We show that this identity can be extended to generalized versions of en…
We present results on simulations of a stock market with heterogeneous, cumulative information setup. We find a non-monotonic behaviour of traders' returns as a function of their information level. Particularly, the average informed agents underperform random traders; only the most informed agents are able to beat the …
In this survey, we describe the fundamental differential-geometric structures of information manifolds, state the fundamental theorem of information geometry, and illustrate some use cases of these information manifolds in information sciences. The exposition is self-contained by concisely introducing the necessary con…
Improved method for encoding contingency tables reduces mutual information bias.
Whatever information a deep neural network has gleaned from training data is encoded in its weights. How this information affects the response of the network to future data remains largely an open question. Indeed, even defining and measuring information entails some subtleties, since a trained network is a determinist…
Estimates how much samples inform neural network training and function.
Meta learning with information theory and Gaussian processes.
Study derives new equation for reserves in non-monotone information scenarios.