This article presents results from the first statistically significant study of causes of cost escalation in transport infrastructure projects. The study is based on a sample of 258 rail, bridge, tunnel and road projects worth US$90 billion. The focus is on the dependence of cost escalation on (1) length of project imp…
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48 results for “escalation model”
Bayesian model assesses criminal threat escalation.
problem Evaluate threats posed by potential violent criminals.
method Customised three-level Bayesian hierarchical model.
result Model calibrates to expert judgments and updates in real time.
Kernel-Based Learning of Chest X-ray Images for Predicting ICU Escalation among COVID-19 Patientscs.LG
Kernel-based learning predicts ICU escalation from COVID-19 chest X-rays.
problem Predicting ICU escalation from chest X-rays using complex data patterns.
method Generalized Linear Models with Integrated Multiple Additive Regression with Kernels (GLIMARK).
result GLIMARK effectively predicts ICU escalation from chest X-rays.
Extends FC-RAG to anytime-valid sequential coverage for language model swarms.
problem Maintain distribution-free coverage for a swarm of weak language models over time.
method Introduces Anytime-FC-RAG, a sequential extension with a summable calibration-deviation budget.
result Achieves time-uniform alarm validity and safety under predictable adaptive control.
Shared Keyboard: An improved Bayesian design for phase I clinical trials via Beta kernel processstat.AP
Shared Keyboard design improves phase I clinical trials by borrowing information across doses.
problem Interim decisions based on current dose data may overlook signals from neighboring doses.
method Bayesian model-assisted design using Beta kernel process with kernel-weighted pseudo-counts.
result Significant improvements in identifying maximum tolerated dose and safety.
Escalation, timing and severity of insurgent and terrorist events: Toward a unified theory of future threatsphysics.soc-ph
I present a unified discussion of several recently published results concerning the escalation, timing and severity of violent events in human conflicts and global terrorism, and set them in the wider context of real-world and cyber-based collective violence and illicit activity. I point out how the borders distinguish…
SDF-Bayes finds safe drug combinations safely, balancing optimism and caution.
problem Finding safe drug combinations in clinical trials with multiple drugs and patient heterogeneity.
method SDF-Bayes uses Bayesian statistics to choose the most likely MTD while ensuring safety constraints.
result SDF-Bayes outperforms existing methods in both accuracy and safety for drug combination trials.
Operations is a key challenge in the domain of machine learning pipeline deployments involving monitoring and management of real-time prediction quality. Typically, metrics like accuracy, RMSE etc., are used to track the performance of models in deployment. However, these metrics cannot be calculated in production due …
Bubbles are rationalcs.GT
As we show using the notion of equilibrium in the theory of infinite sequential games, bubbles and escalations are rational for economic and environmental agents, who believe in an infinite world. This goes against a vision of a self regulating, wise and pacific economy in equilibrium. In other words, in this context, …
Lock-in, the escalating commitment of decision-makers to an ineffective course of action, has the potential to explain the large cost overruns in large scale transportation infrastructure projects. Lock-in can occur both at the decision-making level (before the decision to build) and at the project level (after the dec…
Optimizes model training efficiency with core subset selection.
problem Escalating computational costs in large dataset training.
method Core subset selection for reweighting.
result Efficiently minimizes computational time and improves model performance.
This article presents results from the first statistically significant study of cost escalation in transportation infrastructure projects. Based on a sample of 258 transportation infrastructure projects worth US$90 billion and representing different project types, geographical regions, and historical periods, it is fou…
This paper shows diffusion models improve intrusion detection by purifying adversarial examples.
problem Adversarial examples mislead ML intrusion detection systems, leading to false alerts or evasion.
method Used diffusion models to purify adversarial examples in intrusion detection.
result Diffusion models enhance adversarial robustness in intrusion detection without significantly impacting normal performance.
Proposes a framework to explain KS deterioration in credit risk models.
problem Inconsistent and ad hoc diagnosis of KS decline in credit risk models.
method Counterfactual diagnostic framework attributing KS decline to sampling variability, portfolio composition, covariate shift, and residual deterioration.
result The proposed approach provides more interpretable and governance-relevant explanations than threshold-based review alone.
Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithmq-fin.RM
Study uses neural networks to predict credit risk in banks.
problem Credit risk management in commercial banks.
method Backpropagation neural network model.
result Neural network model improves credit risk prediction.
SCORE: A 1D Reparameterization Technique to Break Bayesian Optimization's Curse of Dimensionalitycs.LG
SCORE technique reduces BO's high-dimensional search costs.
problem Bayesian optimization's high computational costs in high-dimensional spaces.
method 1D reparametrization trick to maintain linear time complexity.
result Successfully finds global minimum in high-dimensional optimization.
LLMs show biases in investment analysis, leading to unreliable recommendations.
problem LLMs face conflicts between pre-trained knowledge and real-time market data, leading to biases in investment analysis.
method Experimental framework to investigate emergent behaviors in LLMs, analyzing sector, size, and momentum biases.
result Distinct, model-specific biases observed, including a tendency to prefer technology stocks, large-cap stocks, and contrarian strategies.
Parallel Markov Chain Monte Carlo for Bayesian Hierarchical Models with Big Data, in Two Stagesstat.ME
Due to the escalating growth of big data sets in recent years, new Bayesian Markov chain Monte Carlo (MCMC) parallel computing methods have been developed. These methods partition large data sets by observations into subsets. However, for Bayesian nested hierarchical models, typically only a few parameters are common f…
I study the limit of a large random economy, where a set of consumers invests in financial instruments engineered by banks, in order to optimize their future consumption. This exercise shows that, even in the ideal case of perfect competition, where full information is available to all market participants, the equilibr…
This paper tackles AI model governance challenges in financial services.
problem Challenges in current AI model governance practices in financial services.
method Proposes a system-level framework for increased self-regulation.
result Enhanced model governance and risk management capabilities.
A system for supervising decentralized finance risks using LLMs and structured evidence.
problem Supervising decentralized finance risks
method Forecast-grounded agentic supervision system
result Developed a system that scores tickets against a regulator-aligned ground truth and false-intervention rate.
DeXposure-Claw supervises decentralized finance risks by grounding LLM decisions in evidence.
problem Weak evidence leads to over-interventions by general-purpose LLM agents in decentralized finance.
method DeXposure-Claw uses a graph time-series foundation model to forecast exposure networks, turning forecasts into alerts and constraining escalation with data-health gates.
result DeXposure-Claw reduces false alarms and improves regulator alignment in decentralized finance risk supervision.
New framework assesses LLM security risks in BFSI.
problem Lack of domain-specific security evaluation for LLMs in BFSI.
method Risk-aware evaluation framework combining taxonomy, automated red-teaming, and ensemble judging.
result Higher decoding stochasticity and adaptive interaction lead to more severe disclosures.
Our study analyzes how neural network initialization affects privacy and utility in overparameterized models.
problem Privacy and utility trade-off in overparameterized neural networks.
method Analytical proof of KL divergence privacy bound, focusing on initialization, width, and depth.
result Privacy bound improvement with increasing depth under certain initializations, degradation under others.
Chatbot uses BERT to handle financial investment questions, improving accuracy and decision-making.
problem Improving accuracy and decision-making in financial investment customer service.
method Deep Bidirectional Transformer (BERT) model, uncertainty measure comparison, mixed-integer programming, automatic spelling correction.
result Chatbot can recognize 381 intents and decide when to escalate questions.
The paper uses conformal prediction for forecasting conflict sequences in Markov processes.
problem Forecasting future conflict states for national policy decisions.
method Conformal prediction on Markov processes for temporally-dependent data.
result Conformal prediction provides valid uncertainty quantification and robustness to model misspecification.
Method certifies edge predictions with cloud-level reliability.
problem Ensuring reliability of edge intelligence models.
method Conformal alignment-based cascading mechanism.
result Certifies conditional coverage with user control over risk level.
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learningcs.LG
Detects physiological patterns to hemodynamic stress using unsupervised deep learning.
problem Identify and characterize physiological responses to hemorrhage in raw vital sign data.
method Transform vital sign time series into latent space using unsupervised deep learning, identify clusters, and evaluate latent embeddings.
result Clusters in latent embeddings correspond to physiological response patterns matching physicians' intuition.
This chapter focuses on developing datasets for machine learning, addressing data preparation challenges.
problem Challenges in collecting and transforming data for practical machine learning applications.
method Detailed process of dataset development including data collection, transformation, quality evaluation, and distribution.
result Provides insights and practical tools for effective dataset development and management.
This paper enhances uplift modeling for multi-treatment marketing campaigns.
problem Optimizing marketing strategies by selecting individuals likely to respond to different treatments.
method Leveraging score ranking and calibration techniques.
result Improves overall performance of marketing campaigns.
The paper tackles online learning problems with monotone arm sequences, achieving optimal or near-optimal regret bounds.
problem Online learning problems with ordinal and monotone arm sequences, such as dynamic pricing and clinical trials.
method Proposes algorithms for continuum-armed bandit problems with monotone arm sequences, achieving optimal or near-optimal regret bounds.
result Achieves optimal or near-optimal regret bounds for monotone arm sequences, differing from the continuous-armed bandit literature.
A new method reduces the computational cost of KernelSHAP for explaining predictions.
problem Efficiently approximating Shapley values for complex models with many features.
method Replacing stochastic weights with deterministic ones in KernelSHAP.
result Reduces the number of contribution function evaluations by 5% to 50%.
Mini-Hes improves LFA model performance on HDI tasks with missing data.
problem Effective representation of high-dimensional, incomplete data for user behavior understanding.
method Proposes Mini-Hes, a parallelizable second-order LFA model using mini-block diagonal Hessian-free optimization.
result Mini-Hes outperforms state-of-the-art models in missing data estimation tasks on recommender system datasets.
HyPV-LEAD detects cryptocurrency anomalies proactively, improving financial security.
problem Cryptocurrency anomalies like mixing, fraud, and pump-and-dump operations are hard to detect due to class imbalance and temporal volatility.
method HyPV-LEAD integrates lead time into anomaly detection through window-horizon modeling, Peak-Valley sampling, and hyperbolic embedding.
result HyPV-LEAD achieves a PR-AUC of 0.9624 on Bitcoin transaction data, significantly outperforming state-of-the-art methods.
Deep neural networks predict CVCM track circuit failures early.
problem Subtle anomalies in CVCM track circuits lead to failures, causing disruptions.
method Deep neural networks classify anomalies before they escalate.
result Deep neural networks achieve 99.31% overall accuracy in detecting CVCM failures.
Stockformer uses wavelet transform and multi-task learning to predict stock returns and trends.
problem Challenges in predicting market dynamics due to policy uncertainty and economic events.
method Integrates wavelet transformation and multitask self-attention networks to capture market trends and fluctuations.
result Stockformer outperforms existing models on multiple real stock market datasets, demonstrating exceptional stability and reliability.
Gaming-Resistant Insurance Contracts for Autonomous AI Agents: Strategy-Proof Toll Mechanism Designcs.GT
Insurance contracts for autonomous AI agents must be actuarially sound and resistant to gaming.
problem Designing insurance contracts for autonomous AI agents that are actuarially sound and resistant to gaming.
method Characterizing a five-attack space and proving the actuarial runtime is gaming-resistant.
result An incentive-compatible layer for actuarial control of autonomous-agent side effects.
Bayesian deep learning predicts satellite collisions.
problem Space debris poses planetary risk.
method Bayesian deep learning with LSTM networks.
result Predicts conjunction event evolution with uncertainties.
Develops adaptive algorithms for sustainable fertilizer use in agriculture.
problem Sustaining high yields while reducing environmental impacts of fertilizer use.
method Nonlinear model-based bandit algorithms linking biological processes to decision-making.
result Faster learning and higher profits with interpretable recommendations.
A new method for learning to defer decisions with expert advice improves over standard methods.
problem Learning to defer decisions with expert advice in systems where expert information can be modified after selection.
method An augmented surrogate that operates on the composite expert-advice action space, providing consistency guarantees and excess-risk bounds.
result The method improves over standard Learning-to-Defer and adapts its advice acquisition behavior to the cost regime.
This paper introduces Probability Engineering to improve deep learning models.
problem Challenges in traditional probabilistic modeling for AI applications.
method Treats learned probability distributions as engineering artifacts and actively modifies them.
result Improves robustness, efficiency, adaptability, and trustworthiness of deep learning models.
This paper proposes deep generative models to predict rare feature combinations in population synthesis.
problem Estimating rare combinations of feature attributes in multivariate distributions.
method Wasserstein Generative Adversarial Network (WGAN) and Variational Autoencoder (VAE) adapted for large-scale population synthesis.
result The models effectively recover sampling zeros while keeping structural zeros low, improving upon existing methods.
Unified pipeline predicts equipment anomalies with high precision and reduced false positives.
problem Predicting equipment anomalies before failures using small models.
method Triplet Feature Fusion combining statistical, time-series, and text embeddings.
result Achieves high precision and reduced false positives.
Paper proposes a framework for precise daily default risk prediction of Chinese credit bonds.
problem Inadequate and inaccurate bond information disclosure creates risk of default for investors.
method Framework includes summarizing factors impacting defaults, constructing a risk index system, and using ConvLSTM neural network for prediction.
result The model provides more responsive and accurate daily default risk predictions than authoritative ratings.
Study predicts high-cost patients using insurance claims data.
problem Accurately identifying high-cost patients to manage costs.
method Applied machine learning to health insurance claims data.
result Developed a high-performance algorithm with 91.2% AUC.
The paper tackles gradual domain adaptation with manifold-constrained DRO, showing error bounds across distributions.
problem Gradual domain adaptation challenge with manifold-constrained data distributions.
method Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius.
result Theoretical bounds on classification error across distributions, demonstrating error propagation dynamics.
ToolChain-CRC addresses the risk-control problem for retrieval-augmented and tool-using agents under drift.
problem Risk-control problem for retrieval-augmented and tool-using agents under drift.
method ToolChain-CRC uses conformal risk-control under exchangeable calibration runs.
result Trajectory-level risk control keeps accepted-trajectory risk below the target.
The July Revolution in Bangladesh was fueled by state violence, which paradoxically strengthened the movement.
problem Understanding how state repression can paradoxically lead to increased mobilization during civil resistance.
method Mixed-methods approach combining qualitative narrative and quantitative analysis using machine learning and statistical modeling.
result The July Revolution was driven by a contingent, non-linear backfire effect triggered by specific catalytic moral shocks and accelerated by the viral reaction to state brutality.