This work establishes a probabilistic version of parametric operator inference, casting the learning problem as a Bayesian linear regression, and designs a sequential adaptive sampling scheme to select new training parameter vectors that promote ROM stability and accuracy globally in the parameter domain.
Shane A. Mcquarrie, Meng-Wu Guo, A. Chaudhuri· Journal of Scientific Comput...· 1 citation
We study the problem of selecting the best heterogeneous treatment effect (HTE) estimator from a collection of candidates in settings where the treatment effect is fundamentally unobserved. We cast estimator selection as a multiple testing problem and introduce a ground-truth-free procedure based on a cross-fitted, exp...
This work proposes kernel-based methods for identifying and forecasting Hamiltonian systems directly from trajectory data, and provides a more general, problem-agnostic numerical framework that goes beyond Hamiltonian systems and can be used for data-driven learning of arbitrary dynamical systems.
Yasamin Jalalian, Mostafa Samir, B. Hamzi et al.· arXiv.org· 4 citations
We study counterfactual regression, which maps features to outcomes under hypothetical scenarios that differ from those observed in the data. This problem is central to decision-making under distribution shift, where treatment patterns may change at deployment. We develop a semiparametric framework for counterfactual r...
Kwangho Kim· 1 citation
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Activation-Keyed Momentum (AK-Momentum), which builds direction-awareness into the momentum update rule, is proposed, and it is proved that it is a valid momentum, that it applies the input-side curvature correction without matrix inversion, and that it clears stale directions faster than EMA under both a fixed and a d...
SPACR (Single-Pass Adaptive Conformal Regressor), a novel method for directly training uncertainty-aware regressors within a differentiable loss, jointly optimizes accuracy, efficiency, and validity without batch-splitting or a predefined confidence level during training.
S. Messoudi, Sylvain Rousseau, Sébastien Destercke· arXiv.org· 0 citations
This work proposes that the objective for learning simulators should be strategic robustness rather than predictive accuracy, and formulate this as a zero-sum minimax game between a model player and an adversarial policy player, which yields a provably convergent active data selection algorithm.
Christoph Dann, Yishay Mansour, M. Mohri· arXiv.org· 0 citations
Distinguishing profile-shape equivalence from fixed-parameter operator equivalence separates new normalization shapes from adaptive rescalings and organizes the operators by support, tail behavior, and realization complexity.
This paper extends the cooperative multi-task SemCom framework to jointly handle heterogeneous classification and regression tasks on the complex Cityscapes dataset and adopts the information maximization (InfoMax) principle so that it accommodates mixed discrete and continuous semantic variables.
Ahmad Halimi Razlighi, M. Rahman, Maximilian H. V. Tillmann et al.· 0 citations
We study the large-depth behavior of residual networks whose weights are correlated across layers at initialization. Our results confirm and extend a conjecture of Marion et al. [2025], according to which correlated initializations should interpolate continuously between the Brownian stochastic differential equation ar...
This paper derives oracle risk bounds for learning dense and sparse routing with evolving experts, separating approximation, expert-learning, and router-estimation errors, and characterize how sparse Top-K routing can retain the benefits of localized aggregation while controlling per-input computation.
Si-Yuan He, Bo-Kai Yang, Jie Hu et al.· 0 citations
This work proposes Adaptive Local Relational Alignment (ALRA), a position-specific framework combining student proposals with teacher guidance, which replaces the teacher-mass coefficients of the two conditional terms with unit coefficients, preventing either term from being downweighted solely because its region has l...
Quang Hoang Trung, Quang Huu Hieu, Phuc-Thanh Nguyen et al.· 0 citations
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
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