Develops scenario theory for multi-criteria decision making.
problem Need for robustness assessment with multiple criteria and datasets.
method Collectively treats risks associated with individual criteria for multi-criteria decision problems.
result More accurate robustness certificates and sharper quantification of simultaneous criterion satisfaction.
This work forecasts electricity prices using Bayesian regime detection and conditional neural processes.
problem Forecasting electricity prices with optimal operational outcomes.
method Bayesian regime detection with conditional neural processes, integrating multi-criteria decision support.
result R-NP model outperformed other models in comprehensive operational utility assessments.
Framework selects real estate redevelopment uses by integrating value, risk, complexity, and irreversibility.
problem Persistent underperformance of real estate assets due to structural misalignment.
method Integrates real-options logic and multi-criteria decision analysis.
result Reduces over-complexification and misalignment in strategic use selection.
Multi-criteria recommender systems have been increasingly valuable for helping consumers identify the most relevant items based on different dimensions of user experiences. However, previously proposed multi-criteria models did not take into account latent embeddings generated from user reviews, which capture latent se…
A two-stage decision support system optimizes long-short portfolios under ESG considerations.
problem Optimizing long-short portfolios under environmental, social, and governance (ESG) considerations.
method First stage: Multi-criteria evaluation using TODIMSort and MEREC. Second stage: Non-convex portfolio optimization with Omega ratio.
result ESG-enhanced long-short portfolios outperform non-ESG and market-value-weighted benchmarks.
A new method for multi-criteria recommender systems using graph attention networks.
problem Lack of nuanced relationships between users and items based on specific criteria.
method MDGAT, a multi-edge bipartite graph with dual attention networks and contrastive learning.
result MDGAT achieves higher accuracy in predicting item ratings compared to baseline methods.
A game-theoretic approach to multi-criteria ranking from ordinal data.
problem Ranking objects from ordinal data with multiple criteria.
method Generalizing von Neumann winner to multi-criteria setting using Blackwell's approachability.
result The Blackwell winner can be computed as a convex optimization problem and achieves near-optimal sample complexity.
The diversification (generating slightly varying separating discriminators) of Support Vector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning nature of SVMs. Based on the insight that perturbing the SVM kernel may help in diversifying SVMs, we propose two kernel perturbation based b…
We solve the multi-criteria benchmarking problem by formalizing it as a social choice problem and identifying conditions for meaningful rankings.
problem Aggregating multiple metrics into a single ranking for models in benchmarking problems.
method Formalizing multi-criteria benchmarking as a social choice problem and identifying sufficient conditions for meaningful rankings.
result We prove that meaningful multi-criteria benchmarking becomes possible under certain preference conditions (single-peaked, group-separable, distance-restricted).
Proposes a new framework for predicting stock market movements using sparse neural architectures.
problem Challenging problem of predicting stock market movements using technical indicators.
method Multi-criteria optimization approach to evolve sparse neural architectures.
result Evolved parsimonious networks with better generalization capabilities.
Benchmarking deep time series models for equity portfolios
problem Selecting the best deep time series model for equity portfolios
method Using a CRSP benchmark and multi-criteria acceptability analysis
result No architecture dominates the benchmark, with TransEnc-8 having the highest rank-1 acceptability
New method finds optimal hyperparameters for multiple tasks and criteria.
problem Finding optimal hyperparameters for multiple tasks and criteria.
method Multi-Task Multi Criteria (MTMC) method that provides Pareto-optimal solutions.
result The method selects optimal hyperparameters based on given criteria significance coefficients.
Study enhances financial forecasting with machine learning and fuzzy MCDM.
problem Increasing financial uncertainty and market complexity.
method Integrates machine learning (XGBoost, LSTM, GNN) and intuitionistic fuzzy MCDM.
result High forecasting accuracy with low MAPE and narrow confidence intervals.
HRTPP improves TPP interpretability and accuracy in medical event modeling.
problem Lack of interpretability in TPPs for medical event sequences.
method Hybrid-Rule Temporal Point Processes (HRTPP) integrating temporal logic rules and numerical features.
result HRTPP outperforms state-of-the-art interpretable TPPs in predictive performance and clinical interpretability.
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist …
MOSAIC selects few informative exemplars from high-dimensional data with non-linear structures.
problem Representative selection from high-dimensional data with non-linear structures.
method MOSAIC uses a multi-criteria approach with a quadratic formulation to maximize global representation power, diversity, and outlier detection.
result MOSAIC maximizes data coverage in a transformed space and achieves robustness to various outlier types.
Paper examines how income support affects retirement decisions for low-income individuals.
problem Retirement decisions of low-income individuals affected by income disaster.
method Modeling consumption/savings, investment, and retirement choices with income support.
result Optimal retirement decision depends on the level of income support provided.
Random forests are among the most popular classification and regression methods used in industrial applications. To be effective, the parameters of random forests must be carefully tuned. This is usually done by choosing values that minimize the prediction error on a held out dataset. We argue that error reduction is o…
DAT-CGAN improves time series generation for better decision support.
problem Generating accurate time series data for decision support.
method DAT-CGAN uses multi-Wasserstein loss and overlapped block-sampling for improved sample efficiency.
result DAT-CGAN outperforms GAN-based baselines in generating data relevant to decision processes.
This research assesses uncertainty quantification and sensitivity analysis for DTs in nuclear fuel performance.
problem Understanding the reliability and performance of advanced nuclear fuels using DTs.
method Introduces ML-based uncertainty quantification and sensitivity analysis methods applied to BISON fuel performance code.
result Demonstrates the effectiveness of DTs in multi-criteria decision-making for nuclear fuel performance.
AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate g…
Integrating causal machine learning with inherently interpretable models for decision support.
problem Providing causal insights and decision support through machine learning models.
method Proposing an approach that integrates causal machine learning with inherently interpretable models.
result The proposed approach achieves competitive performance in prediction and what-if analysis while offering transparency on the system structure, causal relationships among variables, and functional forms connecting them.
New fairness notion helps identify fair auditors for evaluating decision-support systems.
problem Identifying fair auditors to evaluate decision-support systems for bias.
method Introducing a non-comparative fairness notion based on desired system properties.
result The proposed fairness notion provides guarantees in terms of comparative fairness.
Many recent works on knowledge distillation have provided ways to transfer the knowledge of a trained network for improving the learning process of a new one, but finding a good technique for knowledge distillation is still an open problem. In this paper, we provide a new perspective based on a decision boundary, which…
Algorithm minimizes regret in multi-criteria bandits with constraints.
problem Optimize primary attribute while respecting secondary constraints.
method Con-LCB algorithm that guarantees logarithmic regret and feasibility identification.
result Logarithmic regret and feasibility identification with high probability.
We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g.~as measured by nearest neighbor Euclidean distances between a test sam…
AI system narrows human decision options for better outcomes.
problem Improving human decision-making in sequential tasks.
method Developed a decision support system using a pre-trained AI agent to limit human action choices.
result Participants outperformed AI and solo play in a wildfire mitigation game.
Conformal prediction helps quantify uncertainty but its use by humans is unclear.
problem Uncertainty quantification in predictions for human decision making.
method Decision theoretic framework for evaluating predictive uncertainty.
result Conformal prediction sets and human decision making goals are in tension.
Decision tool helps manage biofouling risks for ships in the Baltic Sea.
problem Biofouling of ships causes environmental and economic issues.
method Bayesian networks to identify biofouling management strategies.
result Optimal biofouling management includes biocidal-free coating and in-water cleaning.
DECE visualizes machine learning decisions with counterfactual explanations.
problem Making machine learning models transparent and explainable.
method Interactive visualization system supporting counterfactual explanations at instance- and subgroup-levels.
result DECE enables users to explore and understand machine learning model decisions.
Study predicts academic achievement using students' support networks.
problem Predicting academic achievement in college students.
method Decision tree and random forest algorithms applied to Ties data.
result Different types of support are important for different demographics and genders.
Bayesian method helps decision-makers find preferred solutions in multi-objective optimization.
problem Identifying preferred solutions from the Pareto set in multi-objective optimization problems.
method Bayesian model to estimate decision-maker's utility function based on pairwise comparisons, guided by a principled elicitation strategy.
result Superior performance in finding high-utility solutions with a small number of queries.
PolicySynth improves synthetic data alignment with real data for better campaign decisions.
problem Synthetic data used in decision support systems often leads to incorrect decisions.
method PolicySynth framework that conditions synthetic data on churn scorer to align with real data decisions.
result PolicySynth achieves high strategy simulation fidelity (0.923-0.960) on churn and acquisition datasets.
Proposes a new stability measure for model fitting on similar feature data sets.
problem Model fitting on data sets with similar features is challenging.
method Tuning hyperparameters in a multi-criteria fashion with predictive accuracy and feature selection stability.
result Our approach achieves similar or better predictive performance than single-criteria and stability selection approaches.
New decision support systems use prediction sets to help experts update their predictions, improving performance.
problem Challenges in explaining and updating predictions from decision support systems.
method Developed a methodology leveraging nested structure of prediction sets and counterfactual monotonicity to improve performance.
result Limiting experts' agency leads to better performance in decision support systems based on prediction sets.
Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care. We focus on the deep reinforcement learning (DRL) models in this paper. DRL models have demonstrated human-le…
Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. For instance, if I choose not to treat this patient, are they likely to die? Practitioners commonly use supervised learning algorithms to fit predictive models that help decision-makers reason about likely futu…
Online learning with one-sided feedback aims to maximize accuracy while ensuring fairness.
problem Maximizing accuracy in online learning with limited feedback and ensuring fairness.
method Extending the framework of Bechavod et al. (2020) to incorporate dynamic panels of auditors, reducing the problem to a contextual combinatorial semi-bandit, and leveraging Exp2 and Context-Semi-Bandit-FTPL algorithms.
result Multi-criteria no regret guarantees for accuracy and fairness are provided.
Summarizes financial news for better investment decisions.
problem Information overload from financial news hinders timely investment decisions.
method Personalized Chain-of-Thought summarization framework integrating user-specified keywords.
result Personalized summaries highlight relevant market signals, improving investment narratives.
MCU-Net combines U-Net and Monte Carlo Dropout for uncertainty in medical image segmentation.
problem Lack of uncertainty representation in deep learning methods for patient-centered healthcare decisions.
method MCU-Net framework using U-Net and Monte Carlo Dropout with four uncertainty metrics.
result MCU-Net maximizes automated performance and refers truly uncertain cases.
Discriminative classifiers improve decision-making in SHM systems.
problem Lack of descriptive labels for SHM data.
method Risk-based active learning with discriminative classifiers.
result Discriminative classifiers offer improved robustness and reduced inspection costs.
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy. Here we propose a framework to directly support human decision-making, in which the role of machines is to reframe pr…
Develops a support-aware framework for reserve-policy selection in advertising markets.
problem Log-based reserve-price evaluation risks weak support and subgroup harm.
method Support-aware offline decision framework converting logged evidence into certified policies.
result Preserves the best gate-passing policy while eliminating only policies with certified regret.
Optimal cutoff interval for risk scores improves binary classification accuracy.
problem Improving binary classification accuracy with abstention.
method Determines optimal cutoff interval for risk scores, refraining from decisions outside this interval.
result Minimizes classification margin and maximizes accuracy within the interval.
Develops a new criterion for subgroup fairness in algorithmic decision support.
problem Identifying fair recommendations in algorithms despite group-level differences.
method IJDI criterion and IJDI-Scan approach to detect and mitigate disparities.
result Identifies significant disparities in recommendations across subpopulations.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
problem Challenges in deploying LLMs for high-stakes decisions, including data security and accountability.
method Interactive human involvement through multiple iterations, decentralized technologies, and automated auditing.
result Enhanced security and accountability in LLM deployment for financial decisions.
In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over t…
Dealing with sparse rewards is a longstanding challenge in reinforcement learning. The recent use of hindsight methods have achieved success on a variety of sparse-reward tasks, but they fail on complex tasks such as stacking multiple blocks with a robot arm in simulation. Curiosity-driven exploration using the predict…