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Tag: under

  • Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness

    Characterizing Online and Private Learnability under Distributional Constraints via Generalized Smoothness arXiv:2602.20585v1 Announce Type: new Abstract: Understanding minimal assumptions that enable learning and generalization is perhaps the central question of learning theory. Several celebrated results in statistical learning theory, such as the VC theorem and Littlestone’s characterization of online learnability, establish conditions on the hypothesis…

  • 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…

  • Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models

    Partial Identification under Missing Data Using Weak Shadow Variables from Pretrained Models arXiv:2602.16061v1 Announce Type: new Abstract: Estimating population quantities such as mean outcomes from user feedback is fundamental to platform evaluation and social science, yet feedback is often missing not at random (MNAR): users with stronger opinions are more likely to respond, so standard…

  • Mixture-of-Experts under Finite-Rate Gating: Communication–Generalization Trade-offs

    Mixture-of-Experts under Finite-Rate Gating: Communication–Generalization Trade-offs arXiv:2602.15091v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures decompose prediction tasks into specialized expert sub-networks selected by a gating mechanism. This letter adopts a communication-theoretic view of MoE gating, modeling the gate as a stochastic channel operating under a finite information rate. Within an information-theoretic learning framework, we specialize…

  • Under the Uzès Sun: When Historical Data Reveals the Climate Change

    Under the Uzès Sun: When Historical Data Reveals the Climate Change Longer summers, milder winters: analysis of temperature trends in Uzès, France, year after year. The post Under the Uzès Sun: When Historical Data Reveals the Climate Change appeared first on Towards Data Science. Marc Polizzi Go to original source

  • Agents Under the Curve (AUC)

    Agents Under the Curve (AUC) Towards understanding if your agentic solution is actually better The post Agents Under the Curve (AUC) appeared first on Towards Data Science. Lambert Leong Go to original source

  • Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift

    Spectral Algorithms in Misspecified Regression: Convergence under Covariate Shift arXiv:2509.05106v1 Announce Type: new Abstract: This paper investigates the convergence properties of spectral algorithms — a class of regularization methods originating from inverse problems — under covariate shift. In this setting, the marginal distributions of inputs differ between source and target domains, while the conditional distribution…

  • AI Operations Under the Hood: Challenges and Best Practices

    AI Operations Under the Hood: Challenges and Best Practices Building robust, reproducible, and reliable GenAI applications requires a framework of continuous improvement, rigorous evaluation, and systematic validation The post AI Operations Under the Hood: Challenges and Best Practices appeared first on Towards Data Science. Erika G. Gonçalves Go to original source

  • Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?

    Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work? arXiv:2507.11891v1 Announce Type: new Abstract: We study A/B experiments that are designed to compare the performance of two recommendation algorithms. Prior work has shown that the standard difference-in-means estimator is biased in estimating the global treatment effect (GTE) due to a particular…

  • 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,…

  • Spectral Algorithms under Covariate Shift

    Spectral Algorithms under Covariate Shift arXiv:2504.12625v1 Announce Type: new Abstract: Spectral algorithms leverage spectral regularization techniques to analyze and process data, providing a flexible framework for addressing supervised learning problems. To deepen our understanding of their performance in real-world scenarios where the distributions of training and test data may differ, we conduct a rigorous investigation…

  • Estimating Unbounded Density Ratios: Applications in Error Control under Covariate Shift

    Estimating Unbounded Density Ratios: Applications in Error Control under Covariate Shift arXiv:2504.01031v1 Announce Type: new Abstract: The density ratio is an important metric for evaluating the relative likelihood of two probability distributions, with extensive applications in statistics and machine learning. However, existing estimation theories for density ratios often depend on stringent regularity conditions, mainly focusing…

  • Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality

    Understanding Inverse Reinforcement Learning under Overparameterization: Non-Asymptotic Analysis and Global Optimality arXiv:2503.17865v1 Announce Type: new Abstract: The goal of the Inverse reinforcement learning (IRL) task is to identify the underlying reward function and the corresponding optimal policy from a set of expert demonstrations. While most IRL algorithms’ theoretical guarantees rely on a linear reward structure,…

  • Optimal Nonlinear Online Learning under Sequential Price Competition via s-Concavity

    Optimal Nonlinear Online Learning under Sequential Price Competition via s-Concavity arXiv:2503.16737v1 Announce Type: new Abstract: We consider price competition among multiple sellers over a selling horizon of $T$ periods. In each period, sellers simultaneously offer their prices and subsequently observe their respective demand that is unobservable to competitors. The demand function for each seller depends…

  • Conformal Prediction Under Generalized Covariate Shift with Posterior Drift

    Conformal Prediction Under Generalized Covariate Shift with Posterior Drift arXiv:2502.17744v1 Announce Type: new Abstract: In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance…

  • Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information

    Contributions to the Decision Theoretic Foundations of Machine Learning and Robust Statistics under Weakly Structured Information arXiv:2501.10195v1 Announce Type: new Abstract: This habilitation thesis is cumulative and, therefore, is collecting and connecting research that I (together with several co-authors) have conducted over the last few years. Thus, the absolute core of the work is formed…