ARTEO algorithm optimizes safety-critical systems with uncertainty.
problem Decision-making under uncertainty with safety constraints in real-time optimization.
method ARTEO algorithm uses multi-armed bandits as a mathematical programming problem subject to safety constraints, learning unknown characteristics through exploration and incorporating uncertainty quantification.
result ARTEO achieves less cumulative regret with accurate and safe decisions.
Combines machine learning and optimization for real-time decision-making.
problem Optimizing decisions in contextually constrained problems.
method Generative model combining interior point methods and adversarial learning.
result Generative model produces optimal decisions with in-sample and out-of-sample guarantees.
Self-Organizing Maps provide quick flood predictions for real-time decision making.
problem Computational demand of physically-based overland flow models limits their use in real-time applications.
method Developed a flood-simulation specific SOM using cellular automata flood model results and synthetic DEM.
result SOMs can generate accurate water depth and flood extent results in a short time.
Modeling social conventions from real-time interactions and sensorimotor control.
problem The role of real-time control and learning in social convention formation.
method Control-based Reinforcement Learning (CRL) model grounded in Distributed Adaptive Control (DAC) theory.
result CRL model achieves human-level performance in multi-agent game-theoretic tasks.
Investigates safe decision-making in interactive environments.
problem Learning the best safe decision in real-time systems.
method Reduces to a constrained linear bandits problem, proposes adaptive experimental design-based algorithm.
result First results on best-arm identification in linear bandits with safety constraints.
Paper proposes real-time VaR estimation using quantile regression forest with conformal calibration.
problem Real-time estimation of Value at Risk (VaR) in rapidly changing markets.
method Quantile regression forest trained offline, real-time VaR estimates via observed risk factors, conformalized estimator for reliability.
result The proposed method provides reliable real-time VaR estimates.
New method controls false discoveries in real-time data streams.
problem Online testing of hypotheses with strict error constraints and no future data.
method Structure-adaptive sequential testing (SAST) with alpha-investment algorithm.
result Substantial power gain over existing online testing rules.
GraPhyR uses GNNs to optimize power grid reconfiguration in real-time.
problem Optimizing power grid reconfiguration for reliability and efficiency.
method Physics-informed Graph Neural Network (GNN) framework.
result GraPhyR learns to optimize DyR tasks efficiently.
LLMs improve financial analysis by processing large data sets.
problem Traditional financial analysis methods struggle with large data volumes.
method Integrating LLMs for enhanced data processing and analysis.
result LLMs offer new capabilities for real-time financial decision-making.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
problem Fair, transparent, and explainable decision-making in Taekwondo.
method Pose-based action recognition, epistemic uncertainty modeling, interactive dashboards.
result 85% reduction in decision review time, 93% referee trust in AI-assisted decisions.
Study optimizes decisions in real-time using inexact simulation solutions.
problem Real-time decision-making in simulation optimization with inexact solutions.
method Optimize then predict (OTP) approach, analyzing bias and variance in simulation-optimization algorithms.
result Unified analysis framework for OTP, establishing convergence rates and optimal allocation of computational budget.
FinBloom enhances LLMs for real-time financial queries.
problem Limited access to real-time financial data by LLMs.
method Developed a custom 7B parameter LLM, Financial Context Dataset, and a Financial Agent.
result Significantly improved LLMs' capability to handle dynamic financial tasks.
KryptoOracle predicts cryptocurrency prices using Twitter sentiments.
problem Real-time price prediction for high-volatility cryptocurrencies.
method Spark-based architecture, sentiment analysis, online learning.
result Real-time adaptation of learning algorithms to new data.
The paper aims to reduce bias in online decision-making by optimizing fairness and regret.
problem Achieving fair and justified real-time decisions in online systems.
method Adapting the learning-from-experts scheme to optimize fairness and regret for multiple label classes and sensitive groups.
result Approximately equalized odds can be achieved without significant loss in regret.
This paper proposes a real-time signal plan recommendation system for traffic incidents.
problem Limited effectiveness of traffic incident management due to late response and workload.
method Decomposes recommendation task into real-time traffic prediction and plan association, learning from historical data and metric learning.
result Precision score of 96.75% and recall of 87.5% on testing plan, with 22.5 minutes lead time ahead of Waze alerts.
Paper introduces online tensor inference for real-time data analysis.
problem Real-time processing of high-dimensional tensor data.
method Stochastic Gradient Descent (SGD) for efficient online inference.
result Establishes non-asymptotic convergence and optimal estimation error rate.
New AI assistant for power grid operators simplifies complex decision-making.
problem Complexity and uncertainty in power grid operations.
method Unified human-machine interface and AI integration.
result Development of a new assistant framework for power grid operators.
AI-Trader benchmarks LLMs in live financial markets, revealing poor trading performance.
problem Challenges in real-time financial decision-making by autonomous agents.
method Fully automated, live evaluation benchmark with minimal human intervention.
result General intelligence does not translate to effective trading, highlighting limitations.
The algorithmic trading comes from digitalisation of the processing of trading assets on financial markets. Since 1980 the computerization of the stock market offers real time processing of financial information. This technological revolution has offered processes and mathematic methods to identify best return on trans…
Framework detects anomalies in real-time PMU data.
problem Anomaly detection in power grid operations.
method Statistical learning and dynamical model.
result Effective anomaly detection and classification.
This research introduces an autonomous robot navigation method using reinforcement learning.
problem Improving robot navigation in complex environments.
method Deep Q Network (DQN) and Proximal Policy Optimization (PPO) models for path planning and decision-making.
result The models enhance robot navigation ability and adaptive learning in unknown environments.
DefogGAN predicts hidden RTS game information to aid strategic decision-making.
problem Predicting hidden information in real-time strategy games like StarCraft.
method Conditional Generative Adversarial Network (GAN) with pyramidal reconstruction loss.
result DefogGAN predicts enemy buildings and combat units as accurately as professional players.
mm-Pose detects human skeletons in real-time using mmWave radar and CNNs.
problem Real-time human skeletal posture estimation in various scenarios.
method mmWave radar, radar-to-image representation, forked CNN architecture.
result Accurate predictions for human skeletal joints in 3D space.
This paper proposes a web-based visual graph analytics platform for interactive graph mining, visualization, and real-time exploration of networks. GraphVis is fast, intuitive, and flexible, combining interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoni…
Proposes real-time risk monitoring for machine learning systems under unknown shifts.
problem Dynamic distribution shifts challenge real-world machine learning systems' risk assurances.
method Sequential hypothesis testing with 'testing by betting' to detect risk violations.
result Effective real-time risk monitoring under various unknown shifts.
MM-DREX adapts LLM experts for financial trading via dynamic routing.
problem Challenges of non-stationary financial markets and static expert designs.
method MM-DREX uses a VLM-powered dynamic router to allocate expert weights and designs heterogeneous trading experts.
result Significantly outperforms 15 baselines across key metrics.
DAD learns to design experiments quickly, outperforming traditional methods.
problem Real-time decision-making in sequential Bayesian experimental design.
method Amortized design network trained with contrastive information bounds.
result DAD outperforms alternative strategies on various problems.
3D-CNN method visualizes localized geometric features for manufacturability analysis.
problem Interpreting 3D-CNN decisions for complex geometries.
method 3D-CNN with surface normals, 3D-GradCAM for feature visualization.
result Identifies critical local features for manufacturability.
A framework for data-driven decision-making in infectious disease control.
problem Optimizing trade-offs between public health and economic impacts.
method Multi-objective model-based reinforcement learning.
result Pareto-optimal policies minimizing long-term costs.
Model predicts stock prices using GAN and RoI Pooling.
problem Predicting stock prices influenced by macroeconomic factors.
method Markov Decision Process, GAN, RoI Pooling.
result Identifies macroeconomic factors' influence on stock prices.
Online learning framework for inverse optimization improves decision-making in noisy data.
problem Real-time decision-making with noisy data and limited historical information.
method Developed an online learning algorithm with implicit update rule for noisy data.
result Algorithm converges at O ( 1 / T ) \mathcal{O}(1/\sqrt{T}) O ( 1/ T ) rate and is statistically consistent. A novel method for efficient CDRL over wireless networks.
problem Challenges in collaborative deep reinforcement learning over wireless networks.
method Semantic-aware heterogeneous federated deep reinforcement learning (HFDRL) algorithm.
result Superior performance compared to state-of-the-art baselines.
Model predicts stock market volatility, leading to successful trading.
problem Real-time risk management in stock markets.
method Algebraic theory of news impact, Bessel and hypergeometric functions, ML procedures.
result Trading system proved successful in historical and real-time experiments.
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
problem Adapting LLMs for real-time financial decision-making in noisy markets.
method ATLAS integrates structured market data, uses Adaptive-OPRO for prompt optimization, and employs multi-agent coordination.
result Adaptive-OPRO consistently outperforms fixed prompts in financial trading.
A new algorithm detects changes in high-dimensional data efficiently under sampling constraints.
problem Real-time monitoring of high-dimensional streaming data with limited sampling.
method Incorporates multi-armed bandit approaches into sequential change-point detection.
result Proposes TSSRP algorithm for efficient detection of changes.
Adaptive RL optimizes testing resource allocation for dynamic software environments.
problem Optimizing resource allocation for evolving software testing environments.
method Integrates Q-learning with hybrid reward design for sequential decision-making.
result Consistently outperforms static and optimization-based baselines in simulation studies.
LiveTradeBench evaluates LLMs in live trading environments.
problem Static benchmarks fail to assess real-world trading ability.
method Live data streaming, portfolio management abstraction, multi-market evaluation.
result LLMs show distinct portfolio styles and adapt to live signals.
Paper introduces mcTangent for real-time dynamical systems.
problem Real-time accurate solutions for complex dynamical systems.
method Synergy of tangent slope learning, model-constrained approach, sequential learning, and data randomization.
result Robust and long-time accurate solutions for various equations.
Meta-DRL improves resource allocation in O-RAN networks.
problem Dynamic resource allocation in O-RAN networks.
method Meta Deep Reinforcement Learning (Meta-DRL) inspired by MAML.
result 19.8% improvement in network management performance.
Proposes a privacy-preserving system for federated learning of road networks.
problem Privacy and security of data shared between vehicles and infrastructure.
method Federated learning over V2V and V2N links, non-IID dataset modeling.
result Improves learning performance and prevents eavesdropping.
Proposes a real-time anomaly detection system using IRL.
problem Real-time anomaly detection in sequential data.
method Uses inverse reinforcement learning to infer reward function and evaluate anomalies.
result Effective in identifying anomalies in real-world data.
Proposes an active RBI framework using Rényi information measures for more informed decision-making.
problem Optimal latent variable estimates in real-time settings with streaming noisy observations.
method Unified inference and query selection steps through Rényi entropy and α-divergence; new objective called Momentum for exploration.
result Analytically demonstrates superior performance compared to conventional methods like mutual information.
This review explores probabilistic forecasting methods in evolving energy markets.
problem Volatility and uncertainty in renewable energy markets require probabilistic forecasting for risk assessment.
method Traces evolution from Bayesian and distribution-based approaches to conformal prediction.
result Probabilistic forecasting offers a more comprehensive approach to risk assessment and market participation.
New RL framework improves real-time control performance.
problem Real-time RL systems assume static states, leading to suboptimal outcomes.
method Introduces a new real-time RL framework where states and actions evolve simultaneously.
result RTAC algorithm outperforms existing state-of-the-art algorithms in real-time and non-real-time settings.
A new active learning strategy for real-time data in production.
problem High annotation costs and time required for quality inspections of unlabeled data.
method A stream-based active learning approach using optimal experimental design theory.
result The proposed algorithm allows for faster reduction in prediction error.
New deep learning method for real-time regression analysis.
problem Real-time regression analysis for time series data.
method Novel deep learning algorithms for real-time regression analysis.
result Demonstrated real-time regression analysis for time series data.
AI models assess psychological risks in currency trading.
problem Identifying psychological risks in currency traders.
method Developed a decision tree model to identify patterns in historical data.
result Enhanced decision-making through real-time alerts.
Study builds an EWS for early detection of stock market turbulence.
problem Early detection of stock market turbulence.
method SWARCH filtering probabilities, two-peak method, LSTM network.
result 96.6% test-set accuracy and 2.4 days of forewarned period.