The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time framework that adapts pretrained tabular foundation models (PFNs) to strategic manipulation by aligning predictions with approximated post-manipulation distributions via strategic in-context examples.
Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , pages=
9 Pith papers cite this work. Polarity classification is still indexing.
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TabTransformer uses Transformer self-attention to generate contextual embeddings from categorical features in tabular data, outperforming prior deep learning methods by at least 1% mean AUC and matching tree-based ensembles on 15 public datasets while showing robustness to missing and noisy features
UEC-STD is an architecture-agnostic corrector that uses seasonal-trend decomposition to mitigate autoregressive error accumulation in deep forecasters and reports gains across 4 backbones and 10 datasets.
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.
On five tabular security datasets at 10% labels, tuning only the classifier with Bayesian optimization recovers a median 86% of the gains from full joint SSL-classifier optimization.
A hybrid RDC model tuned by genetic algorithms and XGBoost recovers parameters to simulate wildfire spread in Argentine Patagonia.
citing papers explorer
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When Tabular Foundation Models Meet Strategic Tabular Data: A Prior Alignment Approach
The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time framework that adapts pretrained tabular foundation models (PFNs) to strategic manipulation by aligning predictions with approximated post-manipulation distributions via strategic in-context examples.
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TabTransformer: Tabular Data Modeling Using Contextual Embeddings
TabTransformer uses Transformer self-attention to generate contextual embeddings from categorical features in tabular data, outperforming prior deep learning methods by at least 1% mean AUC and matching tree-based ensembles on 15 public datasets while showing robustness to missing and noisy features
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Reviving Error Correction in Modern Deep Time-Series Forecasting
UEC-STD is an architecture-agnostic corrector that uses seasonal-trend decomposition to mitigate autoregressive error accumulation in deep forecasters and reports gains across 4 backbones and 10 datasets.
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Assessing Estimate of CATE from Observational Data via an RCT Study
CAFE assesses the fit of observational CATE estimates by partitioning RCT data via propensity scores and comparing to experimental group averages, with theory and extensions for confounders.
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ERPPO: Entropy Regularization-based Proximal Policy Optimization
ERPPO adds a DSA-based ambiguity estimator to MAPPO and switches between L1 and L2 entropy regularization to improve exploration and stability in non-stationary multi-dimensional observations.
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Adaptive MSD-Splitting: Enhancing C4.5 and Random Forests for Skewed Continuous Attributes
Adaptive MSD-Splitting improves C4.5 and Random Forest performance on skewed data by adjusting standard deviation multipliers for discretization while retaining linear time complexity.
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SemiScope: Disentangling Classifier Tuning and Joint Optimization in Semi-Supervised Security Classification
On five tabular security datasets at 10% labels, tuning only the classifier with Bayesian optimization recovers a median 86% of the gains from full joint SSL-classifier optimization.
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Data-Driven Modelling to predict forest fire spread in the Patagonian region in Argentina
A hybrid RDC model tuned by genetic algorithms and XGBoost recovers parameters to simulate wildfire spread in Argentine Patagonia.
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