February 2026

arXiv:2602.10018v1 Announce Type: cross Abstract: Selective conformal prediction aims to construct prediction sets with valid coverage for a test unit conditional on it being selected by a data-driven mechanism. While existing methods in the offline setting handle any selection mechanism that is permutation invariant to the labeled data, their extension to the online setting — where data arrives sequentially and later decisions depend on earlier ones — is challenged by the fact that the selection mechanism is naturally […]

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arXiv:2602.09969v1 Announce Type: cross Abstract: We study a canonical multi-task demand learning problem motivated by retail pricing, in which a firm seeks to estimate heterogeneous linear price-response functions across a large collection of decision contexts. Each context is characterized by rich observable covariates yet typically exhibits only limited historical price variation, motivating the use of multi-task learning to borrow strength across tasks. A central challenge in this setting is endogeneity: historical prices are chosen by managers or algorithms […]

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arXiv:2602.09959v1 Announce Type: cross Abstract: We study the problem of learning multi-index models (MIMs), where the label depends on the input $boldsymbol{x} in mathbb{R}^d$ only through an unknown $mathsf{s}$-dimensional projection $boldsymbol{W}_*^mathsf{T} boldsymbol{x} in mathbb{R}^mathsf{s}$. Exploiting the equivariance of this problem under the orthogonal group $mathcal{O}_d$, we obtain a sharp harmonic-analytic characterization of the learning complexity for MIMs with spherically symmetric inputs — which refines and generalizes previous Gaussian-specific analyses. Specifically, we derive statistical and computational complexity lower bounds […]

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arXiv:2602.09704v1 Announce Type: cross Abstract: Compared to theoretical frameworks that assume equal sensitivity to deviations in all features of data, the theory of anomaly detection allowing for variable sensitivity across features is less developed. To the best of our knowledge, this issue has not yet been addressed in the context of isolation-based methods, and this paper represents the first attempt to do so. This paper introduces an Extended Isolation Forest with feature sensitivities, which we refer to as […]

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arXiv:2602.09639v1 Announce Type: cross Abstract: We analyze, theoretically and empirically, the performance of generative diffusion models based on emph{blind denoisers}, in which the denoiser is not given the noise amplitude in either the training or sampling processes. Assuming that the data distribution has low intrinsic dimensionality, we prove that blind denoising diffusion models (BDDMs), despite not having access to the noise amplitude, emph{automatically} track a particular emph{implicit} noise schedule along the reverse process. Our analysis shows that BDDMs […]

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arXiv:2602.09456v1 Announce Type: cross Abstract: We propose an algorithmic framework, Offline Estimation to Decisions (OE2D), that reduces contextual bandit learning with general reward function approximation to offline regression. The framework allows near-optimal regret for contextual bandits with large action spaces with $O(log(T))$ calls to an offline regression oracle over $T$ rounds, and makes $O(loglog(T))$ calls when $T$ is known. The design of OE2D algorithm generalizes Falcon~citep{simchi2022bypassing} and its linear reward version~citep[][Section 4]{xu2020upper} in that it chooses an action […]

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arXiv:2602.09317v1 Announce Type: cross Abstract: Neural networks are increasingly used as surrogate solvers and control policies, but unconstrained predictions can violate physical, operational, or safety requirements. We propose SnareNet, a feasibility-controlled architecture for learning mappings whose outputs must satisfy input-dependent nonlinear constraints. SnareNet appends a differentiable repair layer that navigates in the constraint map’s range space, steering iterates toward feasibility and producing a repaired output that satisfies constraints to a user-specified tolerance. To stabilize end-to-end training, we introduce […]

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arXiv:2602.09314v1 Announce Type: cross Abstract: Optimizers leveraging the matrix structure in neural networks, such as Shampoo and Muon, are more data-efficient than element-wise algorithms like Adam and Signum. While in specific settings, Shampoo and Muon reduce to spectral descent analogous to how Adam and Signum reduce to sign descent, their general relationship and relative data efficiency under controlled settings remain unclear. Through extensive experiments on language models, we demonstrate that Shampoo achieves higher token efficiency than Muon, mirroring […]

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arXiv:2602.09240v1 Announce Type: cross Abstract: We consider the problem of parameter estimation from a generalized linear model with a random design matrix that is orthogonally invariant in law. Such a model allows the design have an arbitrary distribution of singular values and only assumes that its singular vectors are generic. It is a vast generalization of the i.i.d. Gaussian design typically considered in the theoretical literature, and is motivated by the fact that real data often have a […]

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arXiv:2602.09196v1 Announce Type: cross Abstract: As machine learning models increasingly impact society, their opaque nature poses challenges to trust and accountability, particularly in fairness contexts. Understanding how individual features influence model outcomes is crucial for building interpretable and equitable models. While feature importance metrics for accuracy are well-established, methods for assessing feature contributions to fairness remain underexplored. We propose two model-agnostic approaches to measure fair feature importance. First, we propose to compare model fairness before and after permuting […]

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