Alphabeta Math

Integral probability metrics and Wasserstein GANs

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How statement and proof provenance work

The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.

  • Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
  • AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
  • AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

✓ 2 results · all verified · 2 also independently AI-judged
Every result on this page is machine-checked by a proof checker and read in full and owner-audited; the judge is an additional, independent cross-model AI review of the proofs; all 2 also cleared it.
1 · Prerequisites
Abstract Algebra
Combinatorics
Coxeter Groups
Functional Analysis
Information Theory
Linear Algebra
Machine Learning Algorithms
Measure Theory
Numerical Pdes and Spatial Discretization
Partial Differential Equations
Probability
Real Analysis
Set Theory
Statistical Models and Experiments
Topology
2 · Logical flowchart

3 · Items

Definition

Integral probability metrics

Theorem

Continuity and ideal-critic gradients for compact-space Wasserstein GAN objectives

Corollary

The compact-space WGAN value is differentiable almost everywhere under choice

Sources