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3,686 papers
It is demonstrated that pre-training can induce an implicit bias with a clear statistical advantage over random initialization, enabling feature learning from scarce fine-tuning data.
This work generalizes FM to the broader class of location-scale families for handling data inhomogeneity and introduces a novel class of probability paths defined as geodesics on the manifold of probability distributions, called probabilistic geodesic flow matching.
Tail-Corrected Top-$k$ On-Policy Distillation (TT-OPD) is proposed, which preserves the advantages of TK-OPD, including rich distributional supervision and low computational cost, while providing an unbiased estimator of the gradient of the reverse KL divergence.
An unsupervised framework for functional data that spans both univariate and multivariate cases, with emphasis on the multivariate setting, and factorizes the latent space into separate amplitude and phase embeddings derived from all channels is proposed.
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Multivariate time series anomaly detection (MTAD) is crucial for ensuring the safe and reliable operation of complex systems. Many existing methods learn normal patterns by training reconstruction or forecasting models on predominantly normal data. However, a large portion of these approaches rely on deterministic mode...
What determines the unavoidable sample cost of learning cyclic causal structure? For cyclic linear non-Gaussian models, we study exact condensation recovery from observational data: identifying the strongly connected component (SCC) partition and all edges between components. We establish the first information-theoreti...
Non-negative matrix factorization (NMF) is a singular statistical model whose Bayesian asymptotics are governed by the real log canonical threshold (RLCT). We study the local geometry of the factorization map and derive an upper bound for the RLCT of NMF. Let $H$ be the model inner dimension and $H_0$ the non-negative...
Testing whether two populations of networks share the same edge probabilities is a basic problem in network inference. How hard it is depends on the norm used to measure the difference. For the inhomogeneous Erd\H{o}s--R\'enyi (IER) model, the optimal sample complexity is known for every integer $L_r$ norm and for $1\l...
A novel multi-marginal (MM) inverse OT (IOT) contrastive learning (CL) approach called Neg-MMIOT-CL is developed, which learns representations such that the global multi-marginal OT (MMOT) coupling between a triple of data marginals, with respect to a carefully designed ground-cost between triplets of data points in th...
The Weighted Spline-Expanded Network (WSENet), an end-to-end neural framework that addresses causal effects with continuous treatments in observational studies by combining covariate balancing, structured treatment embedding, and bias-corrected outcome estimation, is proposed.
This review presents a unified Bayesian formulation of KD that formulates teacher predictions as prior information and establishes a rigorous connection to uncertainty quantification, providing a principled interpretation of how teacher information is incorporated into student learning.
This work introduces SPatial coHErent risk control for REplay (SPHERE), a general replay-allocation method applicable across a broad range of learning settings and demonstrates that SPHERE improves accuracy and reduces forgetting across noisy-label vision tasks, continual language-model instruction tuning, and code-gen...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
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Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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