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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One-pass algorithms achieve Õ(M²/ε) space for regression splits and Õ(1/ε) space for Gini splits with matching Ω lower bounds.
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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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Nearly Optimal Bounds for Computing Decision Tree Splits in Data Streams
One-pass algorithms achieve Õ(M²/ε) space for regression splits and Õ(1/ε) space for Gini splits with matching Ω lower bounds.