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.
Analyzes new economic paradigm for non-independent consumer choices.
Choice models, which capture popular preferences over objects of interest, play a key role in making decisions whose eventual outcome is impacted by human choice behavior. In most scenarios, the choice model, which can effectively be viewed as a distribution over permutations, must be learned from observed data. The ob…
Examines how algorithms affect user autonomy and information choice.
Randomly initialized sentence encoders perform well on tasks, suggesting learning is key.
Enhances preference learning by incorporating response times into binary choices.
The paper introduces V(I) to guide algorithm choice and parameter tuning in financial forecasting.
Simple algorithms identify best items or full rankings from choice-based feedback.
Proposes new methods for Markov chain choice models with panel data.
New method automates asymmetric choice for better skill transfer in reinforcement learning.
Information-theoretic Bayesian optimisation techniques have demonstrated state-of-the-art performance in tackling important global optimisation problems. However, current information-theoretic approaches require many approximations in implementation, introduce often-prohibitive computational overhead and limit the choi…
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
Individual choices are either based on personal experience or on information provided by peers. The latter case, causes individuals to conform to the majority in their neighborhood. Such herding behavior may be very efficient in aggregating disperse private information, thereby revealing the optimal choice. However if …
The theory of rational choice assumes that when people make decisions they do so in order to maximize their utility. In order to achieve this goal they ought to use all the information available and consider all the choices available to choose an optimal choice. This paper investigates what happens when decisions are m…
Revealed preference theory studies the possibility of modeling an agent's revealed preferences and the construction of a consistent utility function. However, modeling agent's choices over preference orderings is not always practical and demands strong assumptions on human rationality and data-acquisition abilities. Th…
We introduce a semi-supervised discrete choice model to calibrate discrete choice models when relatively few requests have both choice sets and stated preferences but the majority only have the choice sets. Two classic semi-supervised learning algorithms, the expectation maximization algorithm and the cluster-and-label…
Binary choice forests model customer choices in retailing.
The paper tackles interpreting DCM with image data by addressing data isomorphism.
We introduce a fully probabilistic framework of consumer product choice based on quality assessment. It allows us to capture many aspects of marketing such as partial information asymmetry, quality differentiation, and product placement in a supermarket.
Generative model captures how individuals process travel information under uncertainty.
Bayesian methods detect significant IIA violations in similarity choice data.
Algorithm maximizes revenue from user choices with contextual information.
Observation of the workings of productive organizations shows that the characteristics of a trade, backed by nature given to a technological environment, determine the productive combination implemented by the decision maker, and the structure of the operating cycle which is related. The choice of the production functi…
aMCL uses annealing to improve hypothesis diversity in ambiguous tasks.
Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…
We develop a probabilistic consumer choice framework based on information asymmetry between consumers and firms. This framework makes it possible to study market competition of several firms by both quality and price of their products. We find Nash market equilibria and other optimal strategies in various situations ra…
Estimates optimal transport maps with known cost functions.
Node2vec embeddings are unstable and unstable with parameter choices.
New framework explains neural network bias in solving differential equations.
Study dynamic portfolio choice under rotating drivers, revealing a new geometric structure.
Active learning recovers choice model from noisy data.
New mutual information framework improves contrastive learning for vision tasks.
Dynamic assortment problem on two-sided platform with unknown parameters
Optimal portfolio choice with cross-impact propagators, solving complex equations.
In this paper, we study the dynamic assortment optimization problem under a finite selling season of length . At each time period, the seller offers an arriving customer an assortment of substitutable products under a cardinality constraint, and the customer makes the purchase among offered products according to a d…
A distributed method for Bayesian model choice using marginal likelihood and Monte Carlo sampling.
Subjective expected utility theory assumes that decision-makers possess unlimited computational resources to reason about their choices; however, virtually all decisions in everyday life are made under resource constraints - i.e. decision-makers are bounded in their rationality. Here we experimentally tested the predic…
In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making. It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Ma…
Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We dem…
Bayesian DL model improves DCMs for better predictive and inferential performance.
In this paper we consider an information theoretic approach for the accounting classification process. We propose a matrix formalism and an algorithm for calculations of information theoretic measures associated to accounting classification. The formalism may be useful for further generalizations and computer-based imp…
Additive Bayesian networks are types of graphical models that extend the usual Bayesian generalized linear model to multiple dependent variables through the factorisation of the joint probability distribution of the underlying variables. When fitting an ABN model, the choice of the prior of the parameters is of crucial…
While a user's preference is directly reflected in the interactive choice process between her and the recommender, this wealth of information was not fully exploited for learning recommender models. In particular, existing collaborative filtering (CF) approaches take into account only the binary events of user actions …
New neural network approach using mutual information.
New balanced metrics introduced for SPD matrices, improving metric choice.
Estimators of information theoretic measures such as entropy and mutual information are a basic workhorse for many downstream applications in modern data science. State of the art approaches have been either geometric (nearest neighbor (NN) based) or kernel based (with a globally chosen bandwidth). In this paper, we co…
Proposes fair classification method using Wasserstein-1 distances.
There is a consensus that human and non-human subjects experience temporal distortions in many stages of their perceptual and decision-making systems. Similarly, intertemporal choice research has shown that decision-makers undervalue future outcomes relative to immediate ones. Here we combine techniques from informatio…
Proposes a new model for context-dependent decision-making.