DTSemNet gives an exact, invertible neural-network encoding of hard oblique decision trees that supports direct gradient training for both classification and regression without probabilistic softening or quantized estimators.
International Conference on Machine Learning , pages=
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The paper proposes Strategic Prior-data Fitted Network (SPN), an inference-time method that adapts pretrained tabular foundation models to strategic feature manipulation by constructing aligned in-context examples.
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Approximation-Free Differentiable Oblique Decision Trees
DTSemNet gives an exact, invertible neural-network encoding of hard oblique decision trees that supports direct gradient training for both classification and regression without probabilistic softening or quantized estimators.
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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 method that adapts pretrained tabular foundation models to strategic feature manipulation by constructing aligned in-context examples.