TabOrder learns unsupervised causal variable orderings and enforces them with order-constrained attention for tabular prediction and imputation under distribution shifts.
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- background tured diffusion bridge framework, SR involves learning a conditional stochastic coupling that transports mass from the low-resolution endpoint distribution to the high-resolution endpoint distribution, while preserving the conditioning signal provided by y. The same supervision protocol as described in Section 5.1 is employed, varying the paired fraction ρ∈[0,1] while maintaining a fixed total number of training samples. Appendix D contains detailed descrip- tions of data construction, model arc
- background The proof of (a) is straightforward under the assumption 2. proof of (b) E h eh(w)(n) 2 Fn i =mNE h Y (w) n+1 −y (w) n 2 Fn i .(9) Next, we add and subtract A(w)⊤ ∇f(x n) inside the norm and apply the inequality ∥u+v∥ 2 ≤ 2∥u∥2 + 2∥v∥2, which yields E h Y (w) n+1 −y (w) n 2 Fn i ≤2 A(w)⊤ ∇f(x n−τ (w) n )−y (w) n 2 + 2E h A(w)⊤e∇f(x n−τ (w) n )−A (w)⊤ ∇f(x n−τ (w) n ) 2 Fn i . (10) In view of Assumption 2 we obtain E h eh(w)(n) 2 Fn i ≤2mN A(w)⊤ ∇f(x n)−y (w) n 2 + 2mN ¯A2σ2, which establishes th
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