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3,686 papers
The message is that the statistical cost of depth is the metric entropy of the set $B(k,F)$ of compositions, and it separates the models whose estimation error is independent of the depth from those whose estimation error grows with it.
In multi-agent reinforcement learning (MARL) settings, agents are often heterogeneous with diverse intrinsic utility functions. In this study, we introduce Latent Objective Discovery (LOD) as a framework for decomposing agents' distinct utilities into mixed underlying objectives in a low-dimensional latent space. Laten...
We apply entropy to reinforcement learning in two ways: entropy-augmented reward defines the soft objective, while relative-entropy regularization controls the size of a policy-improvement step without changing that objective. Together they yield a one-parameter family of advanced policies connecting a base policy to i...
We study two-arm contextual bandits with arm-specific single indices and a shared unknown monotone link. Monotonicity makes the optimal action depend only on the contrast between the index directions, hence arm-specific reward functions need not be estimated. We introduce Natural Boundary Learning (NBL), a greedy proce...
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Synthetic experiments show that FSNM recovers a low-rank density ratio and its leading spectral structure, and that the same learned kernel can answer multiple conditional queries without refitting.
MTL-CMO is introduced, a multi-task framework that jointly learns shared function spaces and task-specific operators across multiple datasets and T-CMO, a transfer learning method that reuses the shared spaces to estimate, in closed form, the operator of a new conditional distribution.
This paper studies convex optimization when the gradient cannot be evaluated exactly, but only approximated by a hierarchy of algorithms whose compute grows like $\delta^{-\gamma}$ in the accuracy $\delta$. When $\gamma>2$, falling into the Harder-Than-Monte-Carlo (HTMC) regime, the price of accuracy outruns the varian...
A hierarchy of three entropy-based Shapley games makes this output-side choice explicit for any ordered multivariate outcome, ranging from per-component marginal entropy to fully joint entropy, and establishes a chain-rule decomposition of the joint attribution and characterize the cross-component term through conditio...
GUIDE-FBO is proposed, in which agents exchange compact distributions over the locations of their respective optima inferred from local Gaussian process (GP) posteriors, rather than raw observations, query points, or surrogate parameters, and remains effective across settings ranging from homogeneous to severely hetero...
Tabular foundation models (TFMs) provide predictive distributions for regression, but their prediction regions can exhibit undercoverage or overcoverage even when point predictions are accurate. We introduce C-USIM (Conditionally-Uniformized Score Integration Method), a lightweight application of highest predictive den...
It is shown how pretrained information reshapes the implicit bias and training dynamics, and can thereby reduce the sample complexity of recovering the target parameters and support, by gradient-based fine-tuning.
An information-theoretic framework for generalization in next-token prediction under temporally dependent data is developed, revealing the roles of context length, model complexity, sample size, margin, and temporal mixing and yielding informative guarantees for deterministic algorithms over continuous hypothesis space...
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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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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