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Tag: risk

  • On the Generalization and Robustness in Conditional Value-at-Risk

    On the Generalization and Robustness in Conditional Value-at-Risk arXiv:2602.18053v1 Announce Type: new Abstract: Conditional Value-at-Risk (CVaR) is a widely used risk-sensitive objective for learning under rare but high-impact losses, yet its statistical behavior under heavy-tailed data remains poorly understood. Unlike expectation-based risk, CVaR depends on an endogenous, data-dependent quantile, which couples tail averaging with threshold…

  • Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning

    Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning Estimating neighborhood-level pedestrian risk from real-world incident data The post Modeling Urban Walking Risk Using Spatial-Temporal Machine Learning appeared first on Towards Data Science. Aneesh Patil Go to original source

  • Causal Inference as Distribution Adaptation: Optimizing ATE Risk under Propensity Uncertainty

    Causal Inference as Distribution Adaptation: Optimizing ATE Risk under Propensity Uncertainty arXiv:2512.18083v1 Announce Type: new Abstract: Standard approaches to causal inference, such as Outcome Regression and Inverse Probability Weighted Regression Adjustment (IPWRA), are typically derived through the lens of missing data imputation and identification theory. In this work, we unify these methods from a Machine…

  • Bayesian–AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence

    Bayesian–AI Fusion for Epidemiological Decision Making: Calibrated Risk, Honest Uncertainty, and Hyperparameter Intelligence arXiv:2511.11983v1 Announce Type: new Abstract: Modern epidemiological analytics increasingly use machine learning models that offer strong prediction but often lack calibrated uncertainty. Bayesian methods provide principled uncertainty quantification, yet are viewed as difficult to integrate with contemporary AI workflows. This paper proposes…

  • Conservative Decisions with Risk Scores

    Conservative Decisions with Risk Scores arXiv:2509.25588v1 Announce Type: new Abstract: In binary classification applications, conservative decision-making that allows for abstention can be advantageous. To this end, we introduce a novel approach that determines the optimal cutoff interval for risk scores, which can be directly available or derived from fitted models. Within this interval, the algorithm…

  • Risk-averse Fair Multi-class Classification

    Risk-averse Fair Multi-class Classification arXiv:2509.05771v1 Announce Type: new Abstract: We develop a new classification framework based on the theory of coherent risk measures and systemic risk. The proposed approach is suitable for multi-class problems when the data is noisy, scarce (relative to the dimension of the problem), and the labeling might be unreliable. In the…

  • In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality

    In-Context Learning as Nonparametric Conditional Probability Estimation: Risk Bounds and Optimality arXiv:2508.08673v1 Announce Type: new Abstract: This paper investigates the expected excess risk of In-Context Learning (ICL) for multiclass classification. We model each task as a sequence of labeled prompt samples and a query input, where a pre-trained model estimates the conditional class probabilities of…

  • Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity

    Performative Risk Control: Calibrating Models for Reliable Deployment under Performativity arXiv:2505.24097v1 Announce Type: new Abstract: Calibrating blackbox machine learning models to achieve risk control is crucial to ensure reliable decision-making. A rich line of literature has been studying how to calibrate a model so that its predictions satisfy explicit finite-sample statistical guarantees under a fixed,…

  • Why Most Cyber Risk Models Fail Before They Begin

    Why Most Cyber Risk Models Fail Before They Begin Cybersecurity leaders are being asked impossible questions. “What’s the likelihood of a breach this year?” “How much would it cost?” And “how much should we spend to stop it?” Yet most risk models used today are still built on guesswork, gut instinct, and colorful heatmaps, not…

  • On the Robustness of Kernel Ridge Regression Using the Cauchy Loss Function

    On the Robustness of Kernel Ridge Regression Using the Cauchy Loss Function arXiv:2503.20120v1 Announce Type: new Abstract: Robust regression aims to develop methods for estimating an unknown regression function in the presence of outliers, heavy-tailed distributions, or contaminated data, which can severely impact performance. Most existing theoretical results in robust regression assume that the noise…

  • Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms

    Risk-sensitive Bandits: Arm Mixture Optimality and Regret-efficient Algorithms arXiv:2503.08896v1 Announce Type: new Abstract: This paper introduces a general framework for risk-sensitive bandits that integrates the notions of risk-sensitive objectives by adopting a rich class of distortion riskmetrics. The introduced framework subsumes the various existing risk-sensitive models. An important and hitherto unknown observation is that for…

  • Efficient Risk-sensitive Planning via Entropic Risk Measures

    Efficient Risk-sensitive Planning via Entropic Risk Measures arXiv:2502.20423v1 Announce Type: new Abstract: Risk-sensitive planning aims to identify policies maximizing some tail-focused metrics in Markov Decision Processes (MDPs). Such an optimization task can be very costly for the most widely used and interpretable metrics such as threshold probabilities or (Conditional) Values at Risk. Indeed, previous work…

  • Achievable distributional robustness when the robust risk is only partially identified

    Achievable distributional robustness when the robust risk is only partially identified arXiv:2502.02710v1 Announce Type: new Abstract: In safety-critical applications, machine learning models should generalize well under worst-case distribution shifts, that is, have a small robust risk. Invariance-based algorithms can provably take advantage of structural assumptions on the shifts when the training distributions are heterogeneous enough…

  • Local minima of the empirical risk in high dimension: General theorems and convex examples

    Local minima of the empirical risk in high dimension: General theorems and convex examples arXiv:2502.01953v1 Announce Type: new Abstract: We consider a general model for high-dimensional empirical risk minimization whereby the data $mathbf{x}_i$ are $d$-dimensional isotropic Gaussian vectors, the model is parametrized by $mathbf{Theta}inmathbb{R}^{dtimes k}$, and the loss depends on the data via the projection…

  • Are Data Scientists at Risk in 2025?

    Are Data Scientists at Risk in 2025? The impact of AI on data science jobs. Continue reading on Towards Data Science » Natassha Selvaraj Go to original source

  • if anyone can count risk…

    https://www.swissre.com/press-release/Economic-losses-set-to-increase-due-to-climate-change-with-US-and-Philippines-the-hardest-hit-Swiss-Re-Institute-finds/3051a9b0-e379-4bcb-990f-3cc8236d55a1