arXiv research

New trust matrix quantifies breakdowns in deep neural networks.

Training set bugs are flaws in the data that adversely affect machine learning. The training set is usually too large for man- ual inspection, but one may have the resources to verify a few trusted items. The set of trusted items may not by itself be adequate for learning, so we propose an algorithm that uses these ite…

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

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

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

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 …

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

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

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

New algorithm for federated learning without a central server.

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.

Report on enhancing trust in ML models with visualizations.

In data-limited settings, stochastic policies can outperform deterministic ones in bandit problems.

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

The Social Internet of Things (SIoT), integration of the Internet of Things and Social Networks paradigms, has been introduced to build a network of smart nodes that are capable of establishing social links. In order to deal with misbehaving service provider nodes, service requestor nodes must evaluate their trustworth…

The paper presents a method to compute trusted confidence bounds for LECs in CPS.

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

Translating machine learning (ML) models effectively to clinical practice requires establishing clinicians' trust. Explainability, or the ability of an ML model to justify its outcomes and assist clinicians in rationalizing the model prediction, has been generally understood to be critical to establishing trust. Howeve…

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…

This paper improves privacy in federated learning without a trusted server.

TRSVR combines SVRG with trust-region for faster optimization.

New method solves optimization problems with stochastic objectives and constraints.

Building upon the recent success of deep reinforcement learning methods, we investigate the possibility of on-policy reinforcement learning improvement by reusing the data from several consecutive policies. On-policy methods bring many benefits, such as ability to evaluate each resulting policy. However, they usually d…

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

Two new algorithms solve nonconvex-strongly concave problems efficiently.

New metrics CWSA and CWSA+ improve model evaluation under confidence thresholds.

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.

TrustNet robustly learns noise patterns from trusted data to improve weakly-supervised classification.

Improved conformal prediction for better conditional coverage of classifier predictions.

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…

Adaptive replication improves stochastic function optimization.

TRUST improves tree models' accuracy while maintaining interpretability.

MF-TRPO optimizes MFGs with finite sample guarantees.

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

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.

Trust region policy optimization (TRPO) is a popular and empirically successful policy search algorithm in Reinforcement Learning (RL) in which a surrogate problem, that restricts consecutive policies to be 'close' to one another, is iteratively solved. Nevertheless, TRPO has been considered a heuristic algorithm inspi…

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…

System uses conformal prediction to help experts make accurate decisions without understanding when to trust it.

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

Trust lies at the crux of most economic transactions, with credit markets being a notable example. Drawing on insights from the literature on coordination games and network growth, we develop a simple model to clarify how trust breaks down in financial systems. We show how the arrival of bad news about a financial agen…

Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the net…