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.
New analysis shows interpretability doesn't guarantee steering utility in LLMs.
Proposes a decision-theoretic approach for enhancing model interpretability in Bayesian frameworks.
RUMBoost combines RUMs and deep learning for better choice modelling.
Solves asset allocation for investors with utility functions and limits.
To any utility maximization problem under transaction costs one can assign a frictionless model with a price process , lying in the bid/ask price interval . Such process is called a \emph{shadow price} if it provides the same optimal utility value as in the original model with bid-as…
We investigate optimal consumption problems for a Black-Scholes market under uniform restrictions on Value-at-Risk and Expected Shortfall for logarithmic utility functions. We find the solutions in terms of a dynamic strategy in explicit form, which can be compared and interpreted. This paper continues our previous wor…
This paper introduces a dual problem to study a continuous-time consumption and investment problem with incomplete markets and stochastic differential utility. For Epstein-Zin utility, duality between the primal and dual problems is established. Consequently the optimal strategy of the consumption and investment proble…
A novel approach combines interpretability and performance in machine learning models.
BL learns interpretable optimization structures from data.
We provide an economic interpretation of the practice consisting in incorporating risk measures as constraints in a classic expected return maximization problem. For what we call the infimum of expectations class of risk measures, we show that if the decision maker (DM) maximizes the expectation of a random return unde…
A new framework for private Bayesian tests maintains interpretability and computational efficiency.
In this note, we explicitly solve the problem of maximizing utility of consumption (until the minimum of bankruptcy and the time of death) with a constraint on the probability of lifetime ruin, which can be interpreted as a risk measure on the whole path of the wealth process.
This article is devoted to the maximisation of HARA utilities of L{é}vy switching process on finite time interval via dual method. We give the description of all f-divergence minimal martingale measures in initially enlarged filtration, the expression of their Radon-Nikodym densities involving Hellinger and Kulback-Lei…
The paper proposes a method for interpretable mixture density estimation using a tree structure.
New method interprets complex models for music and urban simulations.
WISCA generates consensus explanations from conflicting model-agnostic interpretability methods.
The last decade has seen huge progress in the development of advanced machine learning models; however, those models are powerless unless human users can interpret them. Here we show how the mind's construction of concepts and meaning can be used to create more interpretable machine learning models. By proposing a nove…
This paper proposes a systematic framework to design a classification model that yields a classifier which optimizes a utility function based on prior knowledge. Specifically, as the data size grows, we prove that the produced classifier asymptotically converges to the optimal classifier, an extended version of the Bay…
A novel algorithm uses Gaussian process regression to interpret non-intrusive ROMs.
The purpose of this paper relies on the study of long term yield curves modeling. Inspired by the economic litterature, it provides a financial interpretation of the Ramsey rule that links discount rate and marginal utility of aggregate optimal consumption. For such a long maturity modelization, the possibility of adju…
This paper reviews feature selection in KGs for improved ML model performance.
New framework for evaluating multiclass classifier calibration.
Connections between integration along hypersufaces, Radon transforms, and neural networks are exploited to highlight an integral geometric mathematical interpretation of neural networks. By analyzing the properties of neural networks as operators on probability distributions for observed data, we show that the distribu…
Neural Decomposition breaks down VAE latent structure for better interpretability.
Unified approach to learn interpretable concepts from data.
Proposes a new framework for optimizing utility with state-dependent benchmarks.
A new model uses neural networks for consistent discrete choice analysis.
The paper improves dropout's utility by reducing interactions in deep neural networks.
LI-ITR combines flexible ML with interpretable approximations for personalized treatment rules.
Novel framework for portfolio selection considering utility and risk.
Understanding black-box machine learning models is crucial for their widespread adoption. Learning globally interpretable models is one approach, but achieving high performance with them is challenging. An alternative approach is to explain individual predictions using locally interpretable models. For locally interpre…
Local Interpretable Model-Agnostic Explanations (LIME) is a popular technique used to increase the interpretability and explainability of black box Machine Learning (ML) algorithms. LIME typically generates an explanation for a single prediction by any ML model by learning a simpler interpretable model (e.g. linear cla…
Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability and predictive power, and to identify effective regularization methods for specific tasks. This study designs a pa…
SVGP KAN integrates sparse variational GP with KANs for scalable probabilistic inference.
Improved 3D ECG feature attributions for clinical interpretation.
The paper characterizes optimal dynamic portfolios for a modified mean-variance utility.
Proposes MIP, a privacy notion that requires less randomness than DP, leading to better utility.
Current methodologies in machine learning analyze the effects of various statistical parity notions of fairness primarily in light of their impacts on predictive accuracy and vendor utility loss. In this paper, we propose a new framework for interpreting the effects of fairness criteria by converting the constrained lo…
Paper tackles interpretability issues in deep learning models.
New method visualizes tabular feature semantics for better model understanding.
We discuss utility based pricing and hedging of jump diffusion processes with emphasis on the practical applicability of the framework. We point out two difficulties that seem to limit this applicability, namely drift dependence and essential risk aversion independence. We suggest to solve these by a re-interpretation …
The paper proposes a method to calibrate healthcare AI models for reliability and interpretability.
Diversification represents the idea of choosing variety over uniformity. Within the theory of choice, desirability of diversification is axiomatized as preference for a convex combination of choices that are equivalently ranked. This corresponds to the notion of risk aversion when one assumes the von-Neumann-Morgenster…
Improved DSSMs for easier interpretable latent variables.
Investors choose between bonds and savings accounts based on utility maximization.
The paper introduces staged event trees for transparent treatment effect estimation.
This paper simplifies deep ReLU networks into local linear models for better interpretability.
State-of-the-art clustering algorithms use heuristics to partition the feature space and provide little insight into the rationale for cluster membership, limiting their interpretability. In healthcare applications, the latter poses a barrier to the adoption of these methods since medical researchers are required to pr…