arXiv research

Improves federated learning robustness against data corruption.

Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque models. Model agnostic methods such as LIME, SHAP, or Break Down promise instance-level interpretability for any complex machine learning model. …

Large-scale computational experiments, often running over weeks and over large datasets, are used extensively in fields such as epidemiology, meteorology, computational biology, and healthcare to understand phenomena, and design high-stakes policies affecting everyday health and economy. For instance, the OpenMalaria f…

New report on machine learning visualization techniques and trends.

The estimation of multivariate GARCH time series models is a difficult task mainly due to the significant overparameterization exhibited by the problem and usually referred to as the "curse of dimensionality". For example, in the case of the VEC family, the number of parameters involved in the model grows as a polynomi…

Paper tackles robust training with noisy labels using a trusted set and pseudo labels.

Improved conformal prediction for better conditional coverage of classifier predictions.

A CBR approach helps fraud analysts trust machine learning predictions.

Proposes a game-theoretic framework for ML trust regulation.

New trust matrix quantifies breakdowns in deep neural networks.

Two multifidelity trust-region methods use low-fidelity models for efficient optimization.

Trust-aware MAB improves learning performance by accounting for human deviation.

Study finds transparency and model performance metrics increase trust in AutoML systems.

Study explores fairness in financial deep learning through multi-scale trust quantification.

Paper proposes OpenAPI to interpret PLM models without access to parameters.

DeFi doesn't fully remove trust, showing run risk and personal character's importance.

Trust is a collective, self-fulfilling phenomenon that suggests analogies with phase transitions. We introduce a stylized model for the build-up and collapse of trust in networks, which generically displays a first order transition. The basic assumption of our model is that whereas trust begets trust, panic also begets…

Human-in-the-loop data analysis applications necessitate greater transparency in machine learning models for experts to understand and trust their decisions. To this end, we propose a visual analytics workflow to help data scientists and domain experts explore, diagnose, and understand the decisions made by a binary cl…

TRSVR combines SVRG with trust-region for faster optimization.

Proposes a Quasi-Newton trust region method for policy optimization in reinforcement learning.

PRCD-MAP learns to trust imperfect priors in causal discovery, improving accuracy and robustness.

Knowing when a classifier's prediction can be trusted is useful in many applications and critical for safely using AI. While the bulk of the effort in machine learning research has been towards improving classifier performance, understanding when a classifier's predictions should and should not be trusted has received …

Two new algorithms solve nonconvex-strongly concave problems efficiently.

Study examines how uncertainty visualization affects analyst trust in automated classification systems.

AdaScale-TuRBO improves high-dimensional Bayesian optimization by dynamically scaling the GP lengthscale.

Study finds significant price declines and capital reallocation from centralized to decentralized exchanges after FTX collapse.

Study shows trust and trustworthiness emerge through reinforcement learning.

Interactions such as double negation in sentences and scene interactions in images are common forms of complex dependencies captured by state-of-the-art machine learning models. We propose Mahé, a novel approach to provide Model-agnostic hierarchical éxplanations of how powerful machine learning models, such as deep ne…

LoRMIkA finds k-optimal association rules for local model interpretability.

Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML…

New methods improve local model explanations in machine learning.

Proposes a trust model for SIoT nodes using Hellinger distance and matrix factorization.

TRUST improves tree models' accuracy while maintaining interpretability.

Proposes a new algorithm for solving optimization problems with stochastic objectives and equality constraints.

Study identifies key aspects of explainable ML for clinical trust.

TROLL improves RL for LLMs by replacing clipping with a trust region projection.

Bayesian optimization tackles constrained high-dimensional problems with penalties and trust regions.

Simplified trust region method reduces representation change during fine-tuning.

New algorithm for federated learning without a central server.

The three-state agent-based 2D model of financial markets in the version proposed by Giulia Iori in 2002 has been herein extended. We have introduced the increase of herding behaviour by modelling the altering trust of an agent in his nearest neighbours. The trust increases if the neighbour has foreseen the price chang…

Examining ESG scoring method for reliability.

The traditional social recommendation algorithm ignores the following fact: the preferences of users with trust relationships are not necessarily similar, and the consideration of user preference similarity should be limited to specific areas. A social trust and preference segmentation-based matrix factorization (SPMF)…

We propose to improve trust region policy search with normalizing flows policy. We illustrate that when the trust region is constructed by KL divergence constraints, normalizing flows policy generates samples far from the 'center' of the previous policy iterate, which potentially enables better exploration and helps av…

A macroeconomic model based on the economic variables (i) assets, (ii) leverage (defined as debt over asset) and (iii) trust (defined as the maximum sustainable leverage) is proposed to investigate the role of credit in the dynamics of economic growth, and how credit may be associated with both economic performance and…

TREGO improves EGO for global optimization of high-dimensional problems.

Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, which is very time consuming. Can we train interpretable and accurate mod…

Outlier detection plays an essential role in many data-driven applications to identify isolated instances that are different from the majority. While many statistical learning and data mining techniques have been used for developing more effective outlier detection algorithms, the interpretation of detected outliers do…

New method detects out-of-distribution samples in regression tasks.