Skip to content

Category

data science

3,686 papers

#machine learning Preprint Sep 2026

Control-Geometry Straightening for Sampling-Based Latent Planning

Joint-embedding predictive architectures enable planning with latent world models, but accurate transition prediction alone does not ensure that the planning objective is easy to optimize. We introduce Control-Geometry Straightening (CGS), a single auxiliary loss that learns planner-friendly representations by directly...

Zi-Lai Fu, Ning Ning · 0 citations
#machine learning Preprint Sep 2026

Simplex Diffusion Models

Simplex Diffusion Models (SDMs) are proposed, a framework that lifts the diffusion process to the probability simplex to represent beliefs over categories and can carry uncertainty across denoising steps, which mitigates information collapse.

Justin Deschenaux, Alexandre Galashov, Andrew Campbell et al. · 2 citations
#machine learning Preprint Sep 2026

Universal Approximation of Measure-to-Measure Operators by Pushforwards

Any continuous measure-to-measure operator with outputs of finite $p-th moment can be uniformly approximated, in the $p-Wasserstein distance, by continuous measure-dependent pushforwards, using measure-theoretic transformers.

Takashi Furuya, N. Nelsen, Frank Cole · 1 citation
#machine learning Preprint Open access Sep 2026

Propagate, Then Sharpen: Post-Hoc Refinement of Frozen Node Classifiers

We study post-hoc refinement of frozen node classifiers: given only the graph $G$ and class distributions $Q$ predicted by a frozen model, can we improve accuracy without access to node features, model parameters, or gradients? APPNP answers this by propagating logits with a restart towards the initial predictions, min...

Preben Johnsen Bentdal, Nello Blaser, Xue-Cheng Tai · 0 citations
#machine learning Preprint Sep 2026

Universality and Generalization of Causal Transformers Across Context Lengths

This work studies whether one masked transformer can approximate causal token-to-token maps uniformly over sequences of arbitrary length sampling a fixed normalized horizon, and characterizes the causal families admitting uniform approximation on these compact input classes by a single transformer with length-independe...

Takashi Furuya, Maarten V. de Hoop, Gabriel Peyré · 0 citations
#machine learning Preprint Sep 2026

Uniform Race: Parameter-Free Approximate Rejection Sampling

We study approximate sampling: given $N$ independent samples from a proposal distribution $\mu$, the goal is to select one whose distribution is close to a target $\pi$ specified only up to a normalizing constant. Block and Polyanskiy (2023) provide finite budget error bounds for approximate rejection sampling (RS) as...

Seiyun Shin, Juhyeong Pang, Kwang-Sung Jun · 0 citations
#machine learning Preprint Sep 2026

Deep Weighted Bellman Residual Minimization for $Q^*$ Estimation

A weighted Bellman residual minimization framework that incorporates density ratio weighting by effectively integrating expert demonstrations with behavioral data is introduced, providing specific guidance for the rational utilization of expert demonstrations.

Li-Can Kang, Jerry Zhijian Yang, Cheng Yuan et al. · 0 citations
#machine learning Preprint Open access Sep 2026

On the Relation Between Interval Regret and Dynamic Regret

Non-stationary online learning has attracted much attention in recent years, as static regret is insufficient to guide algorithm design in changing environments. To address this limitation, interval regret and dynamic regret have been introduced as two representative performance metrics that strengthen static regret in...

Yi-Han Wang, Peng Zhao, Zhi-Hua Zhou · 0 citations
#machine learning Review Sep 2026

Unlocking Few-Step Diffusion for Faithful Previews

Sampling latency compounds in diffusion workflows, where users generate and discard many candidates before keeping one. Surprisingly, the poor outputs of standard few-step samplers do not reflect a lack of reconstruction capacity: by optimizing only the initial noise, frozen 3-4-step samplers can closely reproduce thei...

Jing Jia, Si-Fan Liu, Guanyang Wang · 0 citations
#machine learning Preprint Sep 2026

Posterior Regimes and Latent Deception: Variational Bayesian Inference in Hidden Markov Models for Sequential Fraud Detection in Financial Transactions

We present a three-tier progression of Hidden Markov Models: maximum-likelihood (Baum-Welch), variational Bayesian (VBEM), and a neural variational extension (Neural VBEM), that model each customer's transaction history as a trajectory through a small number of latent behavioural regimes, one of which is empirically id...

Joseph Uririoghene Obukofe, A. O'Hare, Chioma Sandra Dike · 0 citations

From tech blogs

See all →
GPT-Lab Nov 3, 2026

Sovereignty Is the Option to Leave

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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.