arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
arXiv:2608.24582v1 Announce Type: cross Abstract: Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regres...
Mitra‑v2 is a tabular foundation model that achieves state‑of‑the‑art performance on a wide range of real‑world classification and regression tasks, including credit‑risk scoring, clinical prediction, equipment‑failure detection, and house‑price estimation. Trained solely on synthetic data with a larger and more diverse pretraining distribution than its predecessor, it uses a compact 2D Transformer backbone and improved optimization to handle longer contexts and larger feature spaces. On the TabArena and TALENT benchmarks, Mitra‑v2 outperforms leading models such as TabPFN‑3 and TabICLv2, matching the performance of a 1.6B‑parameter TabFM with only 77M parameters, and ranks first on multi‑class classification tasks with more than ten classes.
Xiaomi-TabLDM is a tabular foundation model that performs classification and regression via in-context learning without task‑specific fine‑tuning. It is pretrained solely on synthetic data from structural causal models, achieving top‑ranked regression results on multiple benchmarks while reducing training and prediction time compared to leading models. The architecture incorporates a three‑stage training strategy, dual‑stream feature grouping, lightweight attention residuals, and sparse mixture‑of‑experts, and it can further improve accuracy through test‑time compute scaling.
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients ar...
arXiv:2606. 30410v1 Announce Type: cross Abstract: Foundation models for predictive machine learning on tabular data have recently gained significant traction in academia and industry.
arXiv:2608. 06137v1 Announce Type: new Abstract: Tabular data are ubiquitous in real-world applications and are crucial for data-driven prediction and decision-making across science, industry, finance, healthcare, and public services.
arXiv:2609.37959v1 Announce Type: new Abstract: Tabular machine learning typically relies on per-dataset workflows, fitting tree ensembles or running AutoML searches from scratch for every task. We p...
arXiv:2609.09945v1 Announce Type: cross Abstract: Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where ap...
arXiv:2605. 18383v2 Announce Type: replace Abstract: We present TabH2O, a foundation model for tabular data that performs classification and regression in a single forward pass via in-context learning.
The paper evaluates TabPFN, a tabular foundation model, for motor insurance pricing by comparing it to generalized linear models (GLMs) and XGBoost on two motor third‑party liability datasets. Results show that TabPFN does not consistently outperform GLMs: it lags on claim frequency, especially when the in‑context training set is small, but performs competitively on claim severity where data are limited. Additionally, TabPFN’s inference times are substantially longer than those of the baseline methods.
arXiv:2608. 27076v1 Announce Type: new Abstract: Algorithmic trading now represents a market exceeding $20 billion, where even marginal gains in signal robustness can translate into economically significant returns.