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.
Bayes factors and relative belief ratios are compared as measures of statistical evidence.
Meta-learning improves relative density-ratio estimation from limited data.
Study Nash equilibrium in market with relative wealth concerns under partial information and heterogeneous priors.
Adversarial inference on tree models is possible with limited corruption, improving on Kesten-Stigum threshold.
Divergence estimators based on direct approximation of density-ratios without going through separate approximation of numerator and denominator densities have been successfully applied to machine learning tasks that involve distribution comparison such as outlier detection, transfer learning, and two-sample homogeneity…
This note will extend the research presented in Brown & Rogers (2009) to the case of CRRA agents. We consider the model outlined in that paper in which agents had diverse beliefs about the dividends produced by a risky asset. We now assume that the agents all have CRRA utility, with some integer coefficient of relative…
Study ratio-limit boundaries for random walks on hyperbolic groups.
This paper addresses privacy concerns in ratio statistics using differential privacy.
Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use an F-measure optimization method to improve the performance of logis…
Unified kernel for prediction markets reduces belief variance forecast error.
Paper optimizes DC pension fund management with VaR and relative performance constraints.
The paper gives a simple algebraic description, and background justification, for the Bowley Ratio, the relative returns to labour and capital, in a simple economy.
The belief propagation (BP) algorithm is widely applied to perform approximate inference on arbitrary graphical models, in part due to its excellent empirical properties and performance. However, little is known theoretically about when this algorithm will perform well. Using recent analysis of convergence and stabilit…
Study market-to-book ratios using Stochastic Portfolio Theory.
Applying traditional collaborative filtering to digital publishing is challenging because user data is very sparse due to the high volume of documents relative to the number of users. Content based approaches, on the other hand, is attractive because textual content is often very informative. In this paper we describe …
Stable and consistent model alignment for language models without assuming human preference models.
Bitcoin reacts negatively to inflation surprises, contrary to belief.
The study evaluates forecast risk-adjusted performance using various metrics.
This paper employs the extrinsic information transfer (EXIT) method, a technique imported from the analysis of the iterative decoding of error control codes, to study the performance of belief propagation in community detection in the presence of side information. We consider both the detection of a single (hidden) com…
REGS samples from unnormalized distributions using gradient flow and neural networks.
Generative adversarial networks (GANs) are successful deep generative models. GANs are based on a two-player minimax game. However, the objective function derived in the original motivation is changed to obtain stronger gradients when learning the generator. We propose a novel algorithm that repeats the density ratio e…
The objective of change-point detection is to discover abrupt property changes lying behind time-series data. In this paper, we present a novel statistical change-point detection algorithm based on non-parametric divergence estimation between time-series samples from two retrospective segments. Our method uses the rela…
The cost of belief changes with precision and is a hyperbolic geometry.
We study the effect of the quality and quantity of side information on the recovery of a hidden community of size in a graph of size . Side information for each node in the graph is modeled by a random vector with the following features: either the dimension of the vector is allowed to vary with , while …
Bayesian optimization improves efficiency with semi-supervised learning.
We consider the weighted belief-propagation (WBP) decoder recently proposed by Nachmani et al. where different weights are introduced for each Tanner graph edge and optimized using machine learning techniques. Our focus is on simple-scaling models that use the same weights across certain edges to reduce the storage and…
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, where optimality improves with increased computational time. This is because the resulting planning task takes the form of a dynamic programmi…
The study finds the minimum average area ratio on hyperbolic manifolds and its relation to scalar curvature.
Approximations of loopy belief propagation, including expectation propagation and approximate message passing, have attracted considerable attention for probabilistic inference problems. This paper proposes and analyzes a generalization of Opper and Winther's expectation consistent (EC) approximate inference method. Th…
Study analyzes prediction market convergence and pricing mechanisms.
We present a simple dynamical model of stock index returns which is grounded on the ability of the Cyclically Adjusted Price Earning (CAPE) valuation ratio devised by Robert Shiller to predict long-horizon performances of the market. More precisely, we discuss a discrete time dynamics in which the return growth depends…
In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP …
Generalized belief propagation converges to optimal solutions on graphs with motifs.
Study risk sharing with Lambda VaR under diverse beliefs.
FORE evaluates occupancy ratios without requiring Bellman completeness.
Belief propagation recovers backpropagation results.
Study on future-dependent value functions for off-policy evaluation in complex environments.
Paper develops estimators for unbounded density ratios with applications in error control.
Paper tackles unbounded density ratio estimation for covariate shift adaptation.
Upper bounds for Steklov eigenvalues on manifolds with boundary.
New framework analyzes belief evolution in social networks.
We consider the problem of imitation learning from expert demonstrations in partially observable Markov decision processes (POMDPs). Belief representations, which characterize the distribution over the latent states in a POMDP, have been modeled using recurrent neural networks and probabilistic latent variable models, …
In an incomplete market, including liquidly-traded European options in an investment portfolio could potentially improve the expected terminal utility for a risk-averse investor. However, unlike the Sharpe ratio, which provides a concise measure of the relative investment attractiveness of different underlying risky as…
Study asset pricing with reference-dependent preferences, finding matching equity premia.
Community detection is considered for a stochastic block model graph of n vertices, with K vertices in the planted community, edge probability p for pairs of vertices both in the community, and edge probability q for other pairs of vertices. The main focus of the paper is on weak recovery of the community based on the …
We analyze and quantify, in a financial market with parameter uncertainty and for a Constant Relative Risk Aversion investor, the utility effects of two different boundedly rational (i.e., sub-optimal) investment strategies (namely, myopic and unconditional strategies) and compare them between each other and with the u…
A new metric evaluates generative models by comparing real and generated samples.
The exploration-exploitation trade-off is among the central challenges of reinforcement learning. The optimal Bayesian solution is intractable in general. This paper studies to what extent analytic statements about optimal learning are possible if all beliefs are Gaussian processes. A first order approximation of learn…