Generative AI improves stock selection by synthesizing features from diverse data sources.
problem Automating feature discovery in stock market data.
method Used large language models with retrieval-augmented generation and structured prompting to synthesize features from various data sources.
result AI-generated features consistently outperform baselines, with Sharpe improvements ranging from 14% to 91%.
Optimal allocation of human effort to correct AI assessments in decision-making.
problem How to allocate costly human effort to correct noisy or biased AI-generated assessments.
method Decision-theoretic framework treating AI assessments as signals and human judgments as costly information. Developed estimation procedures under nonparametric and linear models.
result Our approach substantially outperforms LLM-only predictions and achieves performance comparable to full human review while using only 20-30% of the human information.
ChatGPT selects stocks for investment portfolios, but optimization models improve results.
problem Using AI for investment advice due to model inaccuracies.
method Used ChatGPT to generate a stock universe, then compared various portfolio optimization strategies.
result Combining AI-generated stock selection with advanced optimization models yields better investment outcomes.
MarketSenseAI uses AI to select stocks with 10-30% excess alpha.
problem Selecting profitable stocks in financial markets.
method Integrates GPT-4 for analyzing diverse data and decision-making.
result Demonstrated exceptional performance with up to 72% cumulative return.
Develops methods for AI self-assessment to improve trustworthiness.
problem Uncertainty in AI predictions and lack of trust in AI systems.
method Uncertainty estimation techniques considering practical impacts and costs.
result Guidelines for selecting and designing effective AI self-assessment methods.
Proposes a sample selection algorithm for fair and robust AI training.
problem Balancing fairness and robustness in AI models, especially with corrupted data.
method Formulates and solves a combinatorial optimization problem for unbiased sample selection, proposing a greedy algorithm.
result Improves fairness and robustness compared to state-of-the-art techniques, both synthetically and on real datasets.
Generative AI models enhance sector-based investment portfolios, but performance varies by market conditions.
problem Improving investment performance through better stock selection in volatile markets.
method Applied LLMs from OpenAI, Google, Anthropic, DeepSeek, and xAI to select and weight stocks within S&P 500 sectors.
result LLM-weighted portfolios outperform sector indices in stable markets but underperform in volatile ones.
Paper tackles selective regression using uncertainty estimation.
problem Selective regression for machine learning models to avoid predictions when uncertain.
method Model-agnostic non-parametric uncertainty estimation.
result Superior performance compared to state-of-the-art selective regressors.
Adaptive querying learns user psychometrics with AI personas.
problem Learning user psychometrics within query budgets.
method Persona-induced latent variable model with AI personas and large language model response distributions.
result Persona-based posteriors deliver accurate probabilistic predictions.
si4onnx enables selective inference on deep learning models.
problem Establishing the reliability of AI systems through statistical significance of identified regions.
method Selective inference techniques implemented through a Python package.
result Controlled type I error rates for hypothesis testing on deep learning models.
Study uses AI agents to improve equity portfolio management.
problem Improving stock selection and portfolio management efficiency.
method Role-based multi-agent systems for equity research.
result Multi-agent approach outperforms benchmarks in stock selection.
AI uses KGs to assess economic impact of selective lockdowns on Italian companies.
problem Impact of selective lockdowns on Italian companies' economic stability.
method Automated Reasoning and Knowledge Graphs to analyze company networks.
result Identifies strategic companies at risk of takeover during lockdowns.
New methods for selecting variables in complex biomedical data.
problem Selecting important variables in multivariate, functional, and complex biomedical data.
method Optimization-based variable selection methods for various regression models.
result Outperforms state-of-the-art methods in accuracy and speed.
The paper tackles AI advice giving by considering adherence levels and defer options.
problem Inadequate consideration of human adherence to AI recommendations.
method Sequential decision-making model that considers adherence levels and incorporates a defer option.
result Specialized learning algorithms provide better convergence and empirical performance.
New approach selects sparse features without validation.
problem Overfitting and high storage/compute costs in AI models.
method Phase transition in probability of feature retrieval.
result Flexible approach for various models and loss functions.
Annealed importance sampling (AIS) is a common algorithm to estimate partition functions of useful stochastic models. One important problem for obtaining accurate AIS estimates is the selection of an annealing schedule. Conventionally, an annealing schedule is often determined heuristically or is simply set as a linear…
New AI stock indices classify firms' AI engagement using 10-K filings.
problem Opaque AI selection criteria in existing ETFs.
method NLP analysis of 10-K filings to classify AI stocks.
result Companies with higher AI engagement have greater positive returns.
Proposes a new trust framework for AI models to maximize utility.
problem Concerns over bias and discrimination in predictive models.
method Introduces a novel trust framework inspired by philosophy, focusing on maximizing Bayes utility.
result Properly-ranked models are inherently U-trustworthy. Framework evaluates AI proposals for drug discovery, finds no LLM advantage.
problem No principled framework exists for evaluating AI-guided scientific selection under budget constraints.
method Formally verified metric (BSDS/DQS) penalizes false discoveries and excessive abstention.
result LLMs provide no marginal value over existing classifiers in drug discovery.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
problem Cognitive biases affect human-AI collaboration, leading to suboptimal outcomes.
method Randomized experiment with 2,784 participants, manipulating AI suggestion quality, task burden, and financial incentives.
result Individual attitudes toward AI are the strongest predictor of performance, influencing accuracy and overcorrection.
Commentary on Cheng's fairness comparison between tests and AI.
problem Distinction between equality and equity in fairness.
method Systematic comparison of test fairness and algorithmic fairness.
result Importance of causality in fairness research.
Optimizes AIS hyperparameters for efficient marginal likelihood estimation.
problem Limited computation budget affects AIS performance.
method Flexible intermediary distributions defined by residual density, parameter sharing, and fix linear schedule.
result Optimized-Path AIS reduces sampling iterations and improves performance.
A framework selects GANs for specific applications efficiently.
problem Fragmented knowledge leads to trial-error selection of GANs.
method Comprehensive summary of GANs, comparison, and novel framework.
result Significant reduction in search space for GAN selection.
Study merges datasets to improve AI model performance.
problem Improving machine learning models with diverse datasets.
method Developed an algorithm using oracle inequality and data-driven estimators.
result Algorithm reduces population loss with high probability.
ALPODS AI diagnoses high-dimensional biomedical data with human-understandable explanations.
problem AI decisions in high-dimensional biomedical data are not explainable to humans.
method ALPODS method classifies data based on clusters and generates fuzzy reasoning rules.
result ALPODS provides understandable explanations for AI diagnoses.
Developing an AI economist agent using RAG, knowledge graphs, and LLMs for economic scenario analysis.
problem Economic scenario analysis using large language models and knowledge graphs.
method Proposing an RAG-based AI economist framework that utilizes knowledge graphs and LLMs.
result Improves economic coherence and traceability in generated reports.
The study evaluates AI model performance measures for medical use.
problem Selecting appropriate performance measures for AI models in medical practice.
method Assessed 32 performance measures across five domains for binary outcomes.
result 17 measures are both proper and reflect decision-analytic performance.
CausalGame benchmarks LLM agents' causal thinking in games.
problem Evaluating causal thinking in AI Scientists with LLMs.
method Interactive games with 14 scenarios incorporating selection bias, measurement error, and hidden confounders.
result None of the 30 LLM agents demonstrated reliable causal thinking, with the best model achieving only 68.0% survival.
Differential privacy improves AI security, fairness, and learning.
problem Privacy violations, security issues, and model fairness in AI.
method Application of differential privacy in various AI areas.
result Differential privacy enhances AI performance in multiple areas.
AI enhances personalized drug development and decision-making in pharma.
problem Traditional drug development lacks personalized treatment plans.
method Application of AI in drug discovery, clinical trials, and post-marketing assessment.
result AI improves personalized medicine, optimizing health outcomes.
AI systems need reliable testing to ensure safety and trustworthiness.
problem Current AI Act lacks functional trustworthiness for AI systems.
method Define technical application distribution, set risk-based performance, and conduct statistically valid testing.
result Reliable functional trustworthiness is essential for AI systems.
FinRL-X unifies trading components for AI and rule-based strategies.
problem Inconsistent between research and live deployment in trading platforms.
method Modular architecture integrating data processing, strategy construction, backtesting, and execution.
result Unified protocol supports AI and rule-based trading components without altering execution.
This paper explores how balancing and filtering techniques affect predictive multiplicity in machine learning models.
problem Predictive multiplicity due to Rashomon effect in high-stakes environments.
method Investigates the impact of balancing and filtering techniques on predictive multiplicity using 21 real-world datasets.
result Data-centric AI strategies can mitigate predictive multiplicity, but preprocessing methods may introduce it.
ML Compass helps organizations choose AI models that balance utility, cost, and compliance.
problem Selecting AI models that meet user utility, deployment costs, and compliance requirements.
method Develops ML Compass, a framework for constrained optimization over a capability-cost frontier, using internal measures and empirical data.
result ML Compass produces deployment-aware recommendations that differ from capability-only rankings, clarifying trade-offs between capability, cost, and safety.
AI system analyzes financial analyst recommendations and track records for portfolio construction.
problem Human PMs rely on analyst recommendations and track records for portfolio decisions.
method Develops AI-based Recommender Systems to replicate analyst conviction and track records.
result AI can improve portfolio construction by integrating analyst conviction and track records.
A fuzzy expert system selects stocks for BSE using AI techniques.
problem Selecting stocks for investment allocation is challenging due to many influencing factors.
method Dempster-Shafer (DS) evidence theory for rule base generation, portfolio optimization model with ACO algorithm.
result The model's performance is satisfactory for short-term investment.
Unified Bayesian-AI framework improves epidemiological risk prediction and uncertainty quantification.
problem Lack of calibrated uncertainty in machine learning models for epidemiology.
method Combines Bayesian prediction with Bayesian hyperparameter optimization using logistic regression and Gaussian-process Bayesian optimization.
result Unified Bayesian-AI framework provides reliable coverage and improved calibration, enhancing epidemiological decision making.
AI predicts stock winners with 2.43 Sharpe ratio, but returns are highly concentrated.
problem Predicting stock returns with AI, focusing on identifying top winners.
method Deployed a state-of-the-art LLM to autonomously search the web for stock attractiveness, avoiding look-ahead bias.
result AI can generate alpha by identifying top winners, but returns are highly concentrated.
Active IRL selects optimal human demonstrations for learning AI preferences.
problem Costly human demonstrations in IRL for autonomous systems.
method Information-theoretic acquisition function for selecting informative human demonstrations.
result Efficiently reduces human effort in learning AI preferences.
New approach to algorithmic fairness for human-AI collaboration considers compliance with human decisions.
problem Current fairness approaches assume perfect human compliance, but real-world compliance is often poor.
method Defines compliance-robustly fair algorithms and proposes an optimization strategy to improve fairness.
result Algorithmic recommendations can improve fairness even if humans do not fully comply with fair algorithms.
ProEval efficiently estimates AI performance and discovers failures using pre-trained Gaussian Processes.
problem Resource-intensive evaluation of generative AI models.
method ProEval uses pre-trained Gaussian Processes and Bayesian quadrature to estimate performance and discover failures.
result ProEval requires significantly fewer samples to achieve accurate performance estimates and reveals more diverse failure cases.
FreB protocol uses AI to infer hidden parameters with valid confidence regions.
problem Generating biased or overconfident conclusions from AI-generated posterior distributions.
method Frequentist-Bayes (FreB) protocol reshapes AI-generated posterior distributions into valid confidence regions.
result FreB provides valid confidence regions that consistently include true parameters with expected probability.
Urbanism is no longer planned on paper thanks to powerful models and 3D simulation platforms. However, current work is not open to the public and lacks an optimisation agent that could help in decision making. This paper describes the creation of an open-source simulation based on an existing Dutch liveability score wi…
CoE modularizes LLMs for scalable, cost-effective AI systems.
problem Challenges in scalability, cost, and customization of monolithic LLMs.
method Composition of Experts (CoE) leverages multiple expert LLMs with a router for dynamic selection.
result CoE achieves superior performance with reduced computational overhead and cost.
New causal measures improve feature selection in AI models.
problem Lack of causal interpretability in AI models.
method Introduces causal entropy and causal information gain to assess feature control.
result Demonstrates superiority of causal information gain in feature selection.
This paper optimizes AI inference on edge devices with reduced communication and computation costs.
problem Efficiently performing AI inference on resource-constrained edge devices with reduced communication and computation costs.
method A three-step framework for effective inference: model split point selection, communication-aware model compression, and task-oriented encoding of intermediate features.
result Our proposed framework achieves a better trade-off and significantly reduces inference latency compared to baseline methods.
Platform combines RL and language models to study narrative influence on AI decisions.
problem Understanding how narrative elements shape AI decision-making.
method Dual-system architecture with reinforcement learning and language model integration.
result Initial experiments show narrative frameworks can influence AI decision-making.
TorchKM: A GPU-Oriented Library for Kernel Learning and Model Selection
problem Kernel learning and model selection
method GPU acceleration
result Competitive predictive performance with speedups