Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
Shifts: A dataset of real distributional shift across multiple large-scale tasks
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A gradient norm from a post-hoc self-supervised trajectory forecasting decoder detects distribution shifts in prediction models, with reported improvements on Shifts and Argoverse datasets.
Argoverse 2 introduces three new datasets with annotated sensor data, massive lidar collections, and challenging motion forecasting scenarios for autonomous driving research.
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citing papers explorer
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Beyond IID: How General Are Tabular Foundation Models, Really?
Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
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Forecasting the Past: Gradient-Based Distribution Shift Detection in Trajectory Prediction
A gradient norm from a post-hoc self-supervised trajectory forecasting decoder detects distribution shifts in prediction models, with reported improvements on Shifts and Argoverse datasets.
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Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Argoverse 2 introduces three new datasets with annotated sensor data, massive lidar collections, and challenging motion forecasting scenarios for autonomous driving research.
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A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
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