ACDC algorithm creates a chain of decisions for risk stratification in healthcare.
problem Effective risk stratification in healthcare settings for resource allocation.
method Sequentially carves out 'pure' subsets of majority class examples to yield a pure subset of minority class examples.
result ACDC algorithm provides an interactive interpretation and visual performance metrics.
Deep neural networks optimize inventory decisions in complex supply chains.
problem Optimizing inventory decisions in stochastic multi-echelon supply chains.
method Pairwise modeling and DNN agents for order-up-to levels.
result The method performs better than alternate methods in general supply chain networks.
A new approach integrates inventory prediction and routing optimization for better supply chain management.
problem Optimizing efficient route selection in supply chain management with uncertain inventory demand.
method Decision-focused learning approach using neural networks to directly integrate inventory prediction and routing optimization.
result Direct integration of inventory prediction and routing optimization leads to better supply chain decisions.
Develops an LLM-based agent for superior cryptocurrency trading.
problem Lack of LLMs in cryptocurrency trading due to its unique data types.
method Combines on-chain and off-chain data analysis with a reflective mechanism.
result Demonstrates superior performance in maximizing returns compared to traditional strategies.
The Chain-of-Decision approach improves forecasting of financial professionals' trading decisions.
problem Challenges in forecasting professionals' behaviors, especially in trading decisions.
method Integrates an opinion-generator-in-the-loop to provide subjective analysis based on news items.
result Promising improvements in the proposed tasks' performance.
This paper introduces a new method for optimizing large-scale problems using Markov chain block updates.
problem Optimizing large-scale problems with efficient and natural block selection.
method Markov chain block coordinate descent (BCD) for optimization.
result The method converges for minimizing Lipschitz differentiable functions, with sublinear and linear convergence rates for convex and strongly convex functions, respectively.
The paper calculates the value of information in high-dimensional decision making.
problem Determining the value of acquiring new information in high-dimensional decision problems.
method Using tools from sub-Gaussian processes and generic chaining for asymptotic analysis.
result Asymptotic results on the expected value of information as dimensionality increases.
Blockchain and AI improve invoice financing for supply chains.
problem Challenges in invoice financing for upstream suppliers in complex supply chains.
method Combining blockchain and AI technologies to solve financing issues.
result Atomic crosschain functionality enables informed decisions under uncertainty.
This paper solves the open problem of computing Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.
problem Computing the Bayes optimal prediction for decision trees is infeasible due to an infeasible summation over all division patterns of a feature space.
method Solved the open problem using a Markov chain Monte Carlo method with adaptively tuned step size.
result Computed the Bayes optimal prediction for decision trees using a Markov chain Monte Carlo method.
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.
Two strategies extend multi-label chaining for imprecise probability estimates.
problem Handling imprecise probability estimates in multi-label classification.
method Adapting multi-label chaining to use convex sets of distributions (credal sets).
result Adapted approaches produce relevant cautiousness on hard-to-predict instances.
AI systems that explain their decisions can be monitored for harmful intentions.
problem Monitoring AI systems' decision-making processes for harmful intentions is imperfect and can miss some misbehavior.
method Monitoring the chain of thought (CoT) of AI systems that communicate in human language.
result CoT monitoring is a promising but fragile approach to AI safety.
Algorithm learns mixtures of Markov chains and MDPs from short trajectories.
problem Learning mixtures of Markov chains and MDPs from short unlabeled trajectories.
method Subspace estimation, spectral clustering, EM algorithm, model estimation, classification.
result 96.6% average accuracy on a mixture of two MDPs in gridworld, outperforming EM algorithm with random initialization.
Investment decision in commodity reserves is modeled with uncertainty and learning.
problem Uncertainty in commodity reserve levels and price uncertainty.
method Continuous-time Markov chain model to value investment option.
result Learning about reserve levels improves investment decision.
This paper improves Bayesian decision tree learning using HMC.
problem Bayesian decision tree learning is challenging due to a large parameter space.
method Develops and compares HMC-based algorithms for exploring Bayesian decision tree posteriors.
result HMC-based methods outperform existing methods in predictive accuracy and tree complexity.
The paper proposes a new method to estimate optimal policies using MCMC.
problem Estimating the optimal policy for systems with unknown dynamics and reward functions.
method Using Markov Chain Monte Carlo to generate samples from the posterior distribution of parameters conditioned on optimality.
result The method provably converges to the globally optimal stochastic policy with similar variance to policy gradient methods.
Approach for assessing supply chain cyber risks using expert judgment and forecasting.
problem Supply chain managers face challenges in assessing cyber risks affecting business factors.
method Structured expert judgment and forecasting models to assess various attack techniques and impacts.
result Facilitates implementation of risk management activities and decision-making processes.
DeFi TrustBoost uses blockchain and AI to assess small business loans.
problem Assessing small business loans from low-wealth households.
method Combines blockchain and Explainable AI to ensure confidentiality, compliance, and security.
result Tamper-proof auditing and on-chain/off-chain data storage for financial organizations.
Proposes DVC for better variable selection in non-grid data.
problem Challenges of identifying important variables in non-grid data.
method Imposes chain structure on blocks of variables using step-wise greedy search.
result Outperforms other generic DNNs and classifiers.
Study examines pricing strategies in competitive supply chains with discrete prices.
problem Inaccurate assumptions in traditional SC models for pricing decisions.
method Examines a SC model with one supplier and two manufacturers, considering customer demand segmentation and discrete price setting.
result Nash equilibria among manufacturers are not unique, and low denomination factors can lead to instability.
New data set and method for BTO supply chain demand forecasting.
problem Lack of demand forecasting methods for BTO supply chains.
method Proposes a novel data transformation technique for BTO products.
result Approach compares well to state-of-the-art methods and is easy to implement.
A new Bayesian Decision Tree algorithm for regression and classification.
problem Costly construction of Bayesian Decision Trees.
method General Bayesian Decision Tree algorithm without Markov Chain Monte Carlo or pruning.
result Greedy-modal tree explains most of the information in numerical examples.
Model predicts time evolution of supply chain networks under varying costs.
problem Regulating downstream relationships for sustainable SMEs.
method Time varying SCN model based on Lagrangian mechanics, incorporating EDES cost kernels.
result Model predicts bankruptcy and break-even states under different cost scenarios.
Blackwell's theorems influence modern AI through information compression and decision making.
problem Information compression and decision making under uncertainty.
method Theorems developed in the 1940s and 1950s, applied to modern AI.
result Blackwell theorems remain relevant and influence modern AI subfields.
New algorithm identifies best policy in MDPs faster.
problem Identifying the best policy in Markov Decision Processes.
method Problem-dependent lower bound and first algorithm with instance-specific sample complexity.
result First algorithm with reduced exploration rate for faster convergence.
FS-GCLSTM predicts stock returns by leveraging value-chain relationships.
problem Traditional time series models fail to capture complex interdependencies in modern markets.
method FS-GCLSTM integrates value-chain networks and graph convolutions to predict stock returns.
result FS-GCLSTM consistently delivers superior portfolio performance compared to traditional models.
Paper improves Bayesian neural learning efficiency and uncertainty quantification.
problem Challenges in convergence and scalability of MCMC techniques for Bayesian neural learning.
method Parallel tempering and Langevin-gradient information in Metropolis-Hastings proposals.
result Improves computational time and prediction/decision-making capabilities.
Abstracted Policy Graphs explain reinforcement learning decisions in context.
problem Inability to verify correctness of reinforcement learning systems.
method Markov chains of abstract states to summarize policies.
result Method generates Abstracted Policy Graphs for deterministic policies.
TransCORALNet uses transformer and CORAL for supply chain credit assessment with cold start.
problem Supply chain credit assessment for new borrowers with limited data.
method Two-stream transformer CORAL networks with domain adaptation and LIME.
result TransCORALNet outperforms state-of-the-art models in accuracy.
Supplier learns to price contracts against a learning retailer.
problem Designing data-driven pricing policies for a supplier facing a learning retailer.
method Connecting to non-stationary online learning, proposing dynamic pricing policies for discrete and continuous demand.
result Supplier's pricing policies lead to sublinear regret bounds under various retailer learning policies.
We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heurist…
Study compares forecasting methods for logistics time series.
problem Improving forecasting accuracy in logistics.
method Compared statistical and machine learning methods on simulated time series.
result Statistical methods outperformed machine learning in one-step forecasts.
Optimizes control of hybrid systems with multiple switching processes.
problem Optimal control of hybrid systems with multiple Markov switching processes.
method Combines two separate Markov chains into one synthetic chain, derives HJB equations, and solves the portfolio choice problem.
result Derives explicit solutions and value functions for the optimal control problem.
Survey shows firms still underutilize collaboration in procurement relationships.
problem Limited collaboration between purchasing firms and suppliers despite benefits.
method Survey of procurement professionals, structural equation modeling.
result Firms are not fully utilizing collaborative relationships.
Paper applies RL to optimize inventory management across multiple products and nodes.
problem Optimizing inventory management for a large number of products with shared capacity in a multi-node supply chain.
method Novel multi-agent hierarchical reinforcement learning framework with A2C algorithm and quantised action spaces.
result The approach optimizes for maximizing product sales and minimizing wastage of perishable products.
The study optimizes supply chain management through a dice-based model to predict cleaner production.
problem Uncertainty in supply chain management and economic predictions.
method A 4-component SC module (environmental, demand, economic, social uncertainties) ranked by weight, using Analytical Hierarchical Process and optimization of a weighted cost function.
result Identifies conditions validating the sustainability of a business venture and optimizes market uncertainty.
This paper considers the problem of consumption and investment in a financial market within a continuous time stochastic economy. The investor exhibits a change in the discount rate. The investment opportunities are a stock and a riskless account. The market coefficients and discount factor switch according to a finite…
The paper explores how LLMs with CoT improve performance on complex tasks.
problem Understanding the mechanisms behind LLMs' improved performance with CoT.
method Using circuit complexity theory, the paper examines LLMs' expressivity in solving mathematical and decision-making problems.
result LLMs with CoT can generate correct solutions step-by-step, even for complex tasks.
The paper learns policies for MDPs from data, even when only some features are relevant.
problem Learning a policy for MDPs from state-action samples with unknown relevant features.
method Uses ℓ 1 \ell_1 ℓ 1 -regularized logistic regression to recover policy parameters. result Establishes bounds on regret in terms of generalization error and Markov chain ergodic coefficient.
Optimizes loan recovery timing by forecasting cash flows.
problem Minimizing overall credit loss in loan portfolios.
method Forecast future cash flows using probabilistic and Markov chain models.
result Empirical illustration of loss-optimal recovery timing.
Simple algorithm gives optimal regret bounds for reinforcement learning.
problem Optimizing regret in reinforcement learning for Markov decision processes.
method Optimistic algorithm with regret bound analysis based on mixing time.
result First optimal regret bounds with i l d e O ( t m m i x S A T ) ilde{O}(\sqrt{t_{
m mix} SAT}) i l d e O ( t mmi x S A T ) after T T T steps. The paper uses AI to analyze on-chain parameters and identify risky cryptocurrencies.
problem Identifying risky cryptocurrencies and understanding their price factors.
method Historical data analysis, AI algorithms, clustering, classification.
result A significant negative correlation between cryptocurrency price and maximum and total supply, and a weak positive correlation with 24-hour trading volume.
Two fundamental objects in knot theory are the minimal genus surface and the least area surface bounded by a knot in a 3-dimensional manifold. When the knot is embedded in a general 3-manifold, the problems of finding these surfaces were shown to be NP-complete and NP-hard respectively. However, there is evidence that …
Optimal sequential testing for Markovian data with lower and upper bounds.
problem Sequential hypothesis testing for Markovian data.
method Non-asymptotic lower bounds and optimal test design.
result Optimal test matches lower bound asymptotically.
SNAPO optimizes policies for complex sequential decisions using differentiable simulation.
problem Optimizing policies for high-dimensional, sequential decisions under uncertainty.
method Embeds neural policy in a differentiable simulator, computes gradients efficiently.
result Produces sensitivities at a cost proportional to one reverse pass, regardless of sensitivity count.
Decision tree learning is a popular approach for classification and regression in machine learning and statistics, and Bayesian formulations---which introduce a prior distribution over decision trees, and formulate learning as posterior inference given data---have been shown to produce competitive performance. Unlike c…
We create consistent option surfaces without arbitrage.
problem Constructing consistent option surfaces free of arbitrage across different maturities.
method Combining PCA-Smolyak approximation with chain-consistent diffusion and c-EMOT bridge.
result Computable certificates for strong convexity, solver correctness, and Dupire/Greeks stability.
Develops a new risk measure for Markov chains' asymptotic behavior.
problem Lack of risk measures for asymptotic regimes of Markov chains.
method Simulation-based approach using large deviations theory, density estimation, and stochastic approximation.
result Developed Asymptotic CVaR (ACVaR) for Markov chains.