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3,686 papers
It is shown that a simple Follow-the-Perturbed-Leader algorithm with Gaussian perturbation for each observed context achieves the optimal $\widetilde O(\sqrt{T\log N})$ expected regret for a class of $N$ experts, while requiring one optimization-oracle call per round and no explicit enumeration of the class.
This paper develops a method to analyze benign overfitting in linear regression for general norms and general (sub-Gaussian) distributions and proves that minimum-p-norm interpolation with p>1 can benignly overfit even for non-Gaussian distributions, under suitable conditions.
RareTrap provides model developers a principled approach for evaluating language models under a common distribution, and prioritizing alignment effort to improve safety and mitigate risks.
Observational State-Space Models are introduced, a class of recurrent models that interprets the observed input time series as measurements of an underlying autonomous dynamical system and supports a simple principle for recurrent forecasting: observations should correct the estimated latent state, rather than control...
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SLP-ProbHard is introduced, a cross-family, representation-centered framework for probabilistic hard-constrained learning when explicit structural parameterizations are available, and how feasible coordinates and maps affect stochastic dimension, dependence, calibration, expressiveness, and computation is studied.
Posterior sampling for reinforcement learning (PSRL) is one of the simplest and most effective exploration methods, but a basic question has remained open: does unmodified PSRL achieve minimax regret without structural assumptions on the prior? We answer yes. Exact vanilla PSRL is minimax optimal in leading-order Bayes...
This work analytically marginalize the sparse GP posterior to derive a tractable drift-matching objective that accounts for both the posterior mean and uncertainty of the unknown drift, and introduces an irregular-time-aware variational state posterior that incorporates the actual observation times during both encoding...
Low-rank matrix and tensor bandits exploit structured interactions but typically assume a known reward link. Recent single-index bandit methods accommodate unknown links without directly exploiting matrix or tensor rank. We address this gap by studying stochastic matrix and tensor bandits with an unknown shared Lipschi...
Two parallel approaches to inference and uncertainty quantification over QoI are developed, one uses conditional diffusion and the other conditional autoencoders, and are compared against classical Bayesian methodology, unconditional generative models and deterministic supervised methods.
This work establishes explicit radius conditions under which the robust average-reward Bellman equation admits a constant-gain solution, while for total variation and Wasserstein balls, any positive radius suffices without requiring the nominal MDP to be weakly communicating.
This work introduces a symbolic distillation pipeline that converts trained neural likelihoods into explicit, interpretable expressions optimized for efficient parameter estimation, and uses a recovery-directed objective to guide symbolic regression toward expressions that preserve parameter-recovery accuracy.
We study whether benchmarks reliably guide model selection as developers adapt to evaluation feedback across multiple criteria. We find that the worst-case test-set size needed to estimate the best score among $k$ adaptively chosen models, under any convex combination of the criteria, grows exponentially with the numbe...
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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