Smoothed Picard HMC
Smoothing, Picard dynamics and the proxy warm start.
Main paper results remain open in Lean. Verified building blocks are listed separately below.
Paper, notation and source conventions
Smoothed Picard Hamiltonian Monte Carlo
Source: 2609.06906v1. Pinned source version
- The Hessian inequalities hold at every point; \(\alpha\) and \(\beta\) are positive real numbers. Separate Hessian-to-chord and Gibbs-integrability proofs support the compiled normalized PBPS augmentation certificate: positive Gibbs integral, probability and exact product-volume joint density. Consult linked cells for independent-review and admission states. Conditional kernels, the sampling process and its guarantees remain separate.
- \(\operatorname{TV}(P,Q)\) means sup over measurable events of \(|P(A)-Q(A)|\). For probability laws with densities it is half their \(L^1\) distance.
- \(R_2(P\Vert Q)=\log\int\left(\frac{dP}{dQ}\right)^2\,dQ\) when P is absolutely continuous with respect to Q, and infinity otherwise. The PBPS paper writes \(D_2\) and calls the target \(\mu\); here the common target is \(\pi\).
- Expected oracle queries are not worst-case runtime. Reference-point preparation, proximal access and gradient-only reductions must be charged under the particular theorem's model.
1. Main statements
RED · source theorem known · local proof open
Low-accuracy W2 guarantee
Source: 2609.06906v1. SPHMC Theorem 1.1; Algorithm 3.2; Section 4.5; Appendix D
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Statement
Given a reference point with small gradient, the source algorithm outputs a probability law satisfying the displayed normalized Wasserstein error. This theorem counts gradient and proximal queries; gradient-only implementation is a separate reduction.
- The common \(C^2\) strong-convexity/smoothness setting.
- \(0<\varepsilon\le1\); a supplied \(x_{\mathrm{ref}}\) satisfies \(\|\nabla V(x_{\mathrm{ref}})\|\le\sqrt{\alpha d}\).
- Use the source algorithm and its parameter choices, not an arbitrary Picard or HMC discretization.
Lean statement — not formalized yet
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Proof architecture and calculations · 3 steps
This is a source-linked proof route, not a complete reconstruction of all cited lemmas. The dependencies below remain separate formalization tasks.
Smooth the density
\[e^{-V_\eta}=e^{-V}*\mathcal N(0,\eta I),\qquad \nabla V_\eta(y)=\mathbb E[\nabla V(X)\mid X+\sqrt\eta G=y]\]This is not the Gaussian average of V. Differentiation under the integral and a specified conditional-law version justify the score identity. The implementable score estimator has bias as well as variance; it is not assumed exactly unbiased.
Source: 2609.06906v1. Section 3.1, (3.1)–(3.2); Section 4.1
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Integrate the smoothed Hamiltonian equation
\[X_t=X_0+tP_0-\int_0^t(t-s)\nabla V_\eta(X_s)\,ds\]Picard iteration approximates the trajectory, while Chebyshev–Lobatto quadrature approximates its integrals. Smoothing regularity, quadrature remainder and stochastic score error are separate inputs to the local-error estimate.
Source: 2609.06906v1. Sections 3.2, 4.2–4.4; Appendix B
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Budget all three errors
\[W_2(\widehat\pi,\pi)\le W_2(\widehat\pi,\pi_\eta)+W_2(\pi_\eta,\pi)\]The first term includes iteration and numerical errors; the second is the smoothing bias. Finite second moments and the exact-kernel contraction must be supplied before using this triangle inequality.
Source: 2609.06906v1. Section 4.5; Appendix C.3
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Strict boundary
No Wq, Renyi warmness, exact stationarity of a discretization, or cost-free proximal oracle follows from this \(W_2\) statement.
Further source statements · 1
RED · source theorem known · local proof open
An implementable law near a Renyi-warm proxy
Source: 2609.06906v1. SPHMC Theorem 1.2; Theorem 7.1(ii); Sections 5–6
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Statement
For every allowed \(\delta\) the algorithm produces the actual law below. A second, existential comparison law is close in TV and has bounded order-2 Renyi divergence. The comparison law need not be directly sampled.
- The common setting and the same reference-point bound as Theorem 1.1.
- 0 < \(\delta\) < 1/2.
- The recursive generator uses higher-moment smoothed sampling and terminal RGO implementation; this is not a general \(W_2\)-to-Renyi implication.
Lean statement — not formalized yet
A future declaration must bind the probability laws, normalization, regularity, source algorithm and oracle model explicitly. The formula is a source theorem contract, not Lean code.
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Proof architecture and calculations · 3 steps
This is a source-linked proof route, not a complete reconstruction of all cited lemmas. The dependencies below remain separate formalization tasks.
Turn a moment bound into a nearby bounded-displacement coupling
\[W_p(P,Q)\le r\quad\Longrightarrow\quad\exists P^\dagger:\ \operatorname{TV}(P,P^\dagger)\le\delta,\quad W_\infty(P^\dagger,Q)\le r\delta^{-1/p}\]For \(p\ge2\) and finite p-moments, replace a coupled sample by its partner on the event where displacement exceeds \(r\delta^{-1/p}\). Markov's inequality bounds that event; measurability and coupling attainment must be justified.
Source: 2609.06906v1. Lemma 6.2
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Regularize both laws before measuring divergence
\[R_q(P^\dagger*\gamma_\tau\Vert Q*\gamma_\tau)\le\frac{qW_p(P,Q)^2}{2\tau\delta^{2/p}},\qquad q>1,\quad\tau>0\]A Gaussian reverse-transport bound applies after convolution. Recursive conditional/RGO updates then recover the desired unsmoothed target; deleting that recursion would change the theorem.
Source: 2609.06906v1. Lemma 6.3(ii); Theorems 6.1 and 6.5
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Track the moving conditional target and oracle bill
\[(A^+)^{-1}=A^{-1}+a^{-1},\qquad u^+=A^+(u/A+y/a)\]Completing the square closes the RGO family under another RGO. The recursive stages and outer proximal sampler improve conditioning; the \(\kappa\)-squared setup term from Theorem 1.1 is not silently discarded.
Source: 2609.06906v1. Lemma 6.4; Sections 6.3–6.4 and 7.1
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Lean proof — not formalized yet
Formalization will first match the named technology interfaces, then assemble this source theorem. No placeholder proof or source-cited wrapper has been added.
No corresponding ASTIS declaration is asserted. Search locations below are candidates, not established dependencies or copied library proofs.
Strict boundary
Only the proxy has the displayed Renyi certificate. Genuine Renyi warmness of the actual output requires the stronger W_psi2 input in the separate source theorem.
3. What has been verified?
130 source-mapped declarations; 119 independently reviewed. These are building blocks, not completed paper results.
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Actual affine normalized Gibbs transport
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Actual finite-count Rademacher sums converge weakly to the unit Gaussian
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Compact finite-Hilbert standard Gaussian log-Sobolev inequality
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Compact finite-product Gaussian log-Sobolev inequality
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Actual posterior score and covariance identities
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Actual Gaussian convolution density and C2 normalized potential
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Gaussian Lipschitz first moment and signed exponential domain
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Noncompact C2 Gaussian function log-Sobolev inequality
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Sharp standard Gaussian concentration for every Lipschitz observable
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Actual Gaussian-Gibbs square-root density and energy domains
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Actual Gibbs covariance lower
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Actual Gibbs gradient has zero mean
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Actual Gibbs linear covariance upper
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Why canonical a.e. representatives do not automatically carry classical scores
- Almost-everywhere equality does not determine classical gradients
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The genuine Hessian integral represents the full gradient difference
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Supporting Hessian-to-strong-convexity implication
- From a genuine Hessian lower bound to strong convexity
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A common Markov kernel cannot amplify total-variation error
- A common Markov kernel cannot amplify total-variation error
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Curvature and actual gradient smoothness of the RGO potential
- Quadratic regularization shifts actual curvature and gradient smoothness
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Actual source smoothed Gibbs potential and preserved partition
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For every actual input pair and every common two-vector noise value, the source exact-gradient numerical phase obeys the M_kappa norm contraction with simultaneous explicit universal constant c=1/65536 under h Lambda_J<=c/kappa
- For every actual input pair and every common two-vector noise value, the source exact-gradient numerical phase obeys the M_kappa norm contraction with simultaneous explicit universal constant c=1/65536 under h Lambda_J<=c/kappa
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For every real kappa and phase point, define the exact source block-matrix quadratic form Q_kappa
- For every real kappa and phase point, define the exact source block-matrix quadratic form Q_kappa
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For every real inner-product E, kappa>=1 and phase point z, the true coordinate energy is bounded by6 times the exact source Q
- For every real inner-product E, kappa>=1 and phase point z, the true coordinate energy is bounded by6 times the exact source Q
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For every real inner-product E, kappa>=1 and phase point z, the exact source Q is bounded by3/2 times the true coordinate energy
- For every real inner-product E, kappa>=1 and phase point z, the exact source Q is bounded by3/2 times the true coordinate energy
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Actual numerical phase: synchronous increment in the source metric
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Genuine nonlinear common-noise coupling and raw transport cost
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Actual Gaussian numerical kernel: integrated law and source transport contraction
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A measurable random-center RGO with actual joint recovery
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Actual adaptive backward-kernel KL error propagation
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Actual approximate initialization gradient moments
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Gradient-only approximate proximal execution: certificate and pointwise work
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Chebyshev-Lobatto nodes and integrated coefficient certificate
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ChebyshevLobattoMomentum actual source component
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Actual clipped-gradient Poisson retry program
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Actual clipped-output exponential mean-error bounds
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Actual clipped and ideal RGO bidirectional RN moments
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Counted phase program
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Counted phase work
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Actual finite-output KL with terminal residual discharged
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Actual Gibbs enhanced-state one-step KL recurrence
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Actual enhanced-state termination and terminal FORS execution
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Actual finite recursive RGO KL error accumulation
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Finite threshold-absorbed RGO execution, terminal marginal and ideal recovery
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The actual exact-gradient Picard phase and its first-order difference
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Actual proximal Gaussian oracle with sharp centered concentration
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Actual Gaussian gradient-arc joint law and calculus
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Gaussian KL reverse transport from the actual quadratic transport budget
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Power perspective from Holder with infinite moments allowed
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Actual Gaussian mixture reverse transport from a bounded coupling
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The actual translated Gaussian likelihood and RN derivative
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Exact integrable Gaussian RN power moment and normalized logarithm
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Actual Gaussian observation and finite recursive A1 budget
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Actual Gibbs gradient second moment
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Actual Gibbs position moment about a stationary point
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Actual unclipped gradient-arc mean
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Actual ideal RGO identification and normalization
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The actual implemented two-layer Picard phase kernel
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Both actual Picard query layers under the full phase law
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SPHMC minimizer existence and unconditional q=2 initialization
- A positive strongly convex potential attains its minimum
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SPHMC actual smoothed-Gibbs q=2 initialization component
- Numerical initialization cost for the actual smoothed Gibbs phase law
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SPHMC initial phase coupling, q=2 proof edge
- Couple the initial and reference phase laws with identical momentum
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Actual joint inner reference stopping and expected count
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Full-dimensional kinetic drift dissipation in the actual source metric
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Prescribed logarithmic terminal depth with explicit constants
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MonotoneProximalMap actual source component
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Actual normalized reference and returned sampling precision
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Actual observation-conditioned retained-state execution
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Shared prerequisite: standard-Gaussian squared norm
- The squared norm of a standard Gaussian has mean d
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SPHMC Algorithm 3.1 first partial momentum refresh
- The actual OU half-refresh and its corrected moment budget
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Actual phase accuracy and expected work with identical witnesses
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SPHMC M-kappa phase metric and moment adapter
- The exact SPHMC M-kappa phase cost
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- M-kappa transport controls ordinary phase transport
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SPHMC stationary phase-reference moments
- Gaussian smoothing adds exactly one noise second moment
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SPHMC Wasserstein-to-phase-state second moment
- Wasserstein distance transfers a Lipschitz observable's second moment
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SPHMC D.7 proof edge: two Picard layers from a phase-state moment
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SPHMC first Picard Gaussian input law
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Actual Poisson rejection query tails with explicit constants
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Actual bounded Poisson rejection output and full-batch cost
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Actual Chebyshev-Lobatto momentum integrals are nonnegative
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Actual proximal estimator: stability in both inputs
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Successful stopped executions have one output and count
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Gradient-only proximal execution: expected pointwise work under an input moment
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Actual proximal Gaussian estimator and its fiberwise moments
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ProximalPhaseStability actual source component
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Finite-fuel meaning of a gradient-only proximal query
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One truncation proxy for every Gaussian time and order
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Expected work of the same implemented query at its realized center
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Actual recursive schedule: persistent good conditioning and a terminal parameter bound
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Well-conditioned RGO update: finite variance and contraction
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Actual reference count under the recursive state law
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Actual reference-carrying recursive Markov transition
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One actual RGO backward kernel: precision update, recovery and KL control
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RGO calculus: curvature, normalized target and condition-number update
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Lemma 6.4: RGO calculus and normalized-law closure
- An RGO of an RGO has an explicit normalized target law
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Exact Fisher energy of the restricted-Gaussian fiber score
- The quadratic fiber score has exact relative Fisher energy
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Restricted-Gaussian fiber log ratio and exact relative score
- Canonical llr equals the explicit normalized tilt ratio a.e.
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Actual smooth gradient-arc clipping excess
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Actual smooth gradient-arc exponential moment
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Actual SPHMC smoothed Hessian bounds
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Actual source smoothed Hessian upper
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True smoothed score and standardized posterior expectation
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Actual source quadrature and full phase work
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Allpositive-step true standardized RGO position and Fisher
- Allpositive-step true standardized RGO position and Fisher
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True standardized RGO unique proximal point and finite canonical entropy
- True standardized RGO unique proximal point and finite canonical entropy
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True standardized RGO square-root density domains
- True standardized RGO square-root density domains
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Joint posterior recovery with measurable state-dependent RGO parameters
- Joint posterior recovery with measurable state-dependent RGO parameters
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Actual stopped Gaussian RGO output correctness under sampler interfaces
- Actual stopped Gaussian RGO output correctness under sampler interfaces
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Actual RGO stopping depth and stability beyond the finite cap
- Actual RGO stopping depth and stability beyond the finite cap
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Actual terminal reference, FORS kernel and source program
- Actual terminal reference, FORS kernel and source program
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Actual terminal reference by gradient descent
- Actual terminal reference by gradient descent
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Actual terminal sampler accuracy and cached gradient costs
- Actual terminal sampler accuracy and cached gradient costs
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Canonical KL through the explicit restricted-fiber log ratio
- Evaluate canonical KL with an explicit normalized tilt ratio
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A continuous transport cost attains its infimum
- A continuous transport cost attains its infimum
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The actual SPHMC truncation proxy from an infimum p-cost budget
- The actual SPHMC truncation proxy from an infimum p-cost budget
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An actual two-noise RGO stage with the added-time KL denominator
- An actual two-noise RGO stage with the added-time KL denominator
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Lemma 6.6(i): progress of the ill-conditioned recursive stages
- Why an ill-conditioned recursive stage makes geometric progress
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Numerical canonical KL bound for the actual standardized RGO
- Numerical canonical KL bound for the actual standardized RGO
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Canonical KL bound for the actual standardized RGO
- Canonical KL bound for the actual standardized RGO
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Supporting Gibbs normalization theorem without an assumed minimizer
- Normalize a strongly convex Gibbs target without assuming its minimizer
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Signed two-point square entropy upper bound
- Signed two-point square entropy upper bound
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