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James Tian

Publications and source records attributed to James Tian.

At least 37 records · Page 2Linked to original sources

Boundary Disintegration for Weighted Residual Energy Trees

We study iterated weighted residual (WR) splittings generated by a positive operator $R_{0}\in B\left(H\right)_{+}$ and a finite family of contractions $C_{1},\dots,C_{m}$ in $B\left(H\right)$. The associated residual update $R\mapsto R^{1/2}(I-C^{*}_{j}C_{j})R^{1/2}$ produces an $m$-ary energy tree of residuals $\left\{ R_{w}\right\} $ and dissipated pieces $\left\{ D_{w,j}\right\} $ indexed by finite words. From this tree we construct intrinsic path measures on the path space by biasing transitions either by a fixed quadratic form $x\mapsto\left\langle x,D_{w,j}x\right\rangle $ (defining the measures $ν_{x}$) or, in the trace-class setting, by ${\rm tr}\left(D_{w,j}\right)$ (yielding a reference measure $ν_{\mathrm{tr}}$). When $R_{0}\in S_{1}\left(H\right)_{+}$, we show that $ν_{\mathrm{tr}}$ dominates the family $\left\{ ν_{x}\right\} $ and identify $dν_{x}/dν_{\mathrm{tr}}$ as a canonical martingale limit of cylinder likelihood ratios. Along $ν_{\mathrm{tr}}$-almost every branch the residuals decrease to a terminal trace-class random variable $R_{\infty}$, which we interpret as the WR boundary variable. We then disintegrate $ν_{\mathrm{tr}}$ over $σ\left(R_{\infty}\right)$, obtaining a boundary law $μ_{\mathrm{tr}}=\left(R_{\infty}\right)_{\#}ν_{\mathrm{tr}}$ and conditional path measures $\left\{ ν^{T}_{\mathrm{tr}}\right\} $. Finally, we show that each $ν_{x}$ admits a boundary representation as a mixture of $\left\{ ν^{T}_{\mathrm{tr}}\right\} $ with an explicit boundary density $h_{x}=dμ_{x}/dμ_{\mathrm{tr}}$, thereby organizing the family of intrinsic WR path measures by a single trace-biased boundary disintegration.

math.PR↗

Random Frame Decompositions from Weighted Residual Flows

We study the evolution of a positive operator under weighted residual maps determined by a finite family of orthogonal projections. Iterating these maps along the rooted tree of multi-indices produces a "weighted residual energy tree", together with natural path measures obtained by normalizing the dissipated energy or trace at each step. Under a quantitative coverage condition on the projections, we show that along almost every branch the residuals converge strongly to zero and the dissipated pieces admit a rank-one decomposition that reconstructs the initial operator. In the special case where the initial operator is the identity on a subspace, this yields almost surely a random Parseval frame generated intrinsically by the weighted residual dynamics.

math.FA↗

Moment kernels, nested defects, and Cuntz dilations

Random operator tuples possess a rich second-moment structure that is not visible at the level of pointwise operator inequalities. This paper shows that their averaged word moments form a positive kernel whose behavior is controlled by a single shift-positivity condition. When this condition holds, the kernel admits a Cuntz dilation, and all mean-square interactions are realized inside a canonical isometric model. This leads to a mean-square version of the free von Neumann inequality and to a free functional calculus for random tuples. We further introduce a hierarchy of higher-order defects of the moment kernel and prove that their positivity is equivalent to the existence of a nested chain of projections inside one Cuntz dilation. This yields a multi-level decomposition of moment structure, a Wold-type splitting into dissipative and unitary parts, and a curvature-type invariant that measures the asymptotic non-dissipating content of the tuple.

math.FA↗

Alternating Weighted Residual Flows and the Non-Commutative Gap

This work develops a nonlinear analogue of alternating projections on Hilbert space, based on iterating a weighted residual transformation that removes the portion of an operator detected by a projection after conjugation by its square root. Although this map is neither linear nor variational and falls outside classical operator-mean frameworks, the alternating flow between two fixed projections is shown to be monotone and to converge strongly to a positive limit supported on their common kernel. The analysis identifies an intrinsic representation of this limit inside the operator range of the initial datum, which makes it possible to compare the nonlinear limit with the shorted operator of Anderson-Duffin-Trapp. The nonlinear flow always produces an operator dominated by the shorted operator, with equality precisely in the commuting regime. A global energy identity describes how mass is dissipated at each step of the iteration, and a factorized description localizes the gap between the nonlinear limit and the classical shorted operator.

math.FA↗

Shorting Dynamics and Structured Kernel Regularization

This paper develops a nonlinear operator dynamic that progressively removes the influence of a prescribed feature subspace while retaining maximal structure elsewhere. The induced sequence of positive operators is monotone, admits an exact residual decomposition, and converges to the classical shorted operator. Transporting this dynamic to reproducing kernel Hilbert spaces yields a corresponding family of kernels that converges to the largest kernel dominated by the original one and annihilating the given subspace. In the finite-sample setting, the associated Gram operators inherit a structured residual decomposition that leads to a canonical form of kernel ridge regression and a principled way to enforce nuisance invariance. This gives a unified operator-analytic approach to invariant kernel construction and structured regularization in data analysis.

math.FA↗

Residual-Weighted Decomposition of Positive Operators

This paper investigates an iterative rank-one decomposition scheme for positive operators on a Hilbert space based on a residual-weighted congruence update. At each step the operator is compressed along a chosen unit vector while remaining inside the positive cone, and the resulting map defines a monotone dynamical system on the cone of positive operators. We prove that the associated residuals admit a canonical telescoping decomposition into rank-one terms and a limiting positive operator, and we identify this limit together with an exact energy identity expressing the defect between the initial and limiting operators as a convergent series of rank-one contributions. In the case where the iteration exhausts the operator, the residual directions form a Parseval frame for the natural range space, yielding a constructive procedure that produces Parseval frames without spectral calculus. We further solve the inverse problem by characterizing those decreasing chains with rank-one steps that arise from such dynamics via an intrinsic normalization condition involving the Moore-Penrose inverse. For trace-class operators we obtain a scalar energy identity and show that mild greedy or density conditions on the chosen directions guarantee exhaustion. An application to reproducing kernel Hilbert spaces illustrates the abstract results.

math.FA↗

Random Operator-Valued Frames in Hilbert Spaces

We study strongly measurable random bounded operators on separable Hilbert spaces and analyze two simple iterations driven by independent random positive contractions. The first, a Kaczmarz-like iteration, converges in mean square and almost surely and produces a random operator-valued frame. In the projection case it yields a Parseval identity. The second, a residual-weighted iteration, enjoys an exact step-by-step identity: the accumulated analysis terms plus a residual equal the identity operator. Under a mild mean-coercivity condition, the residual shrinks at a geometric rate in expectation, vanishes almost surely, and admits nonasymptotic tail bounds. As a result, the construction delivers an almost-sure Parseval frame for any independent sequence of positive contractions, not only projections.

math.FA↗

Universal Kernel Models for Iterated Completely Positive Maps

We study how iterated and composed completely positive maps act on operator-valued kernels. Each kernel is realized inside a single Hilbert space where composition corresponds to applying bounded creation operators to feature vectors. This model yields a direct formula for every iterated kernel and allows pointwise limits, contractive behavior, and kernel domination to be read as standard operator facts. The main results include an explicit limit kernel for unital maps, a Stein-type decomposition, a Radon-Nikodym representation under subunitality, and an almost-sure growth law for random compositions. The construction keeps all iterates in one space, making their comparison and asymptotic analysis transparent.

math.FA↗

Stability of Maximum-Entropy Inference in Finite Dimensions

We study maximum-entropy inference for finite-dimensional quantum states under linear moment constraints. Given expectation values of finitely many observables, the feasible set of states is convex but typically non-unique. The maximum-entropy principle selects the Gibbs state that agrees with the data while remaining maximally unbiased. We prove that convergence of moments and entropy implies convergence of states in trace norm, derive explicit quantitative bounds linking data and entropy deviations to state distance, and show that these results are stable under unital completely positive maps. The analysis is self-contained and relies on convex duality, relative entropy, and Pinsker-type inequalities, providing a rigorous and unified foundation for finite-dimensional maximum-entropy inference.

quant-ph↗

Random Nonlinear Fusion Frames from Averaged Operator Iterations

We study random iterations of averaged operators in Hilbert spaces and prove that the associated residuals converge exponentially fast, both in expectation and almost surely. Our results provide quantitative bounds in terms of a single geometric parameter, giving sharp control of convergence rates under minimal assumptions. As an application, we introduce the concept of random nonlinear fusion frames. Here the atoms are generated dynamically from the residuals of the iteration and yield exact synthesis with frame-like stability in expectation. We show that these frames achieve exponential sampling complexity and encompass important special cases such as random projections and randomized Kaczmarz methods. This reveals a link between stochastic operator theory, frame theory, and randomized algorithms, and establishes a structural tool for constructing nonlinear frame-like systems with strong stability and convergence guarantees.

math.FA↗

A Class of Random-Kernel Network Models

We introduce random-kernel networks, a multilayer extension of random feature models where depth is created by deterministic kernel composition and randomness enters only in the outermost layer. We prove that deeper constructions can approximate certain functions with fewer Monte Carlo samples than any shallow counterpart, establishing a depth separation theorem in sample complexity.

cs.LG↗

Ridge Kernel Averaging and Uniform Approximation

We develop a framework for function classes generated by parametric ridge kernels: one-dimensional kernels composed with affine projections and averaged over a parameter measure. The induced kernels are positive definite, and the resulting integral class coincides isometrically with its reproducing kernel Hilbert space. We characterize all kernels obtainable by varying the measure as the uniform closure of the conic hull of ridge atoms, giving a sharp universality criterion; a slice-wise polynomial versus non-polynomial dichotomy governs expressivity. We then analyze random-kernel networks whose activations may be indefinite but have a positive-definite mean. For these networks we prove a Monte Carlo rate with mean-squared error of order one over $N$ and a high-probability uniform bound on compact sets, without requiring pathwise positive definiteness.

math.FA↗

Random Operator-Valued Kernels and Moment Dilations

This paper studies random operator-valued positive definite (p.d.) kernels and their connection to moment dilations. A class of random p.d. kernels is introduced in which the positivity requirement is imposed only in expectation, extending the deterministic case. Basic properties are established, including convexity, sampling stability, and the relationship between mean kernels and deterministic ones. A Gaussian factorization result is proved under a Hilbert-Schmidt/trace condition, clarifying the difference between pathwise and mean-square positive definiteness and linking the construction to radonification in the non-trace-class setting. Moment dilation is then formulated for random operators, extending classical dilation theory to preserve equality of mixed moments in expectation. The resulting dilation triples admit interpretation in terms of unitary dynamics, connecting statistical moment structure with operator-theoretic realization.

math.FA↗

Factorization of positive definite kernels. Correspondences: $C^{*}$-algebraic and operator valued context vs scalar valued kernels

We introduce and study a class $\mathcal{M}$ of generalized positive definite kernels of the form $K\colon X\times X\to L(\mathfrak{A},L(H))$, where $\mathfrak{A}$ is a unital $C^{*}$-algebra and $H$ a Hilbert space. These kernels encode operator-valued correlations governed by the algebraic structure of $\mathfrak{A}$, and generalize classical scalar-valued positive definite kernels, completely positive (CP) maps, and states on $C^{*}$-algebras. Our approach is based on a scalar-valued kernel $\tilde{K}\colon(X\times\mathfrak{A}\times H)^{2}\to\mathbb{C}$ associated to $K$, which defines a reproducing kernel Hilbert space (RKHS) and enables a concrete, representation-theoretic analysis of the structure of such kernels. We show that every $K\in\mathcal{M}$ admits a Stinespring-type factorization $K(s,t)(a)=V(s)^{*}π(a)V(t)$. In analogy with the Radon--Nikodym theory for CP maps, we characterize kernel domination $K\leq L$ in terms of a positive operator $A\inπ_{L}(\mathfrak{A})'$ satisfying $K(s,t)(a)=V_{L}(s)^{*}π_{L}(a)AV_{L}(t)$. We further show that when $π_{L}$ is irreducible, domination implies scalar proportionality, thus recovering the classical correspondence between pure states and irreducible representations.

math.OA↗

Hilbert space-valued Gaussian processes, and quantum states

We offer new results and new directions in the study of operator-valued kernels and their factorizations. Our approach provides both more explicit realizations and new results, as well as new applications. These include: (i) an explicit covariance analysis for Hilbert space-valued Gaussian processes, (ii) optimization results for quantum gates (from quantum information), (iii) new results for positive operator-valued measures (POVMs), and (iv) a new approach/result in inverse problems for quantum measurements.

quant-ph↗

From Classical to Quantum: Polymorphisms in Non-Commutative Probability

We present a parallel between commutative and non-commutative polymorphisms. Our emphasis is the applications to conditional distributions from stochastic processes. In the classical case, both the measures and the positive definite kernels are scalar valued. But the non-commutative framework (as motivated by quantum theory) dictates a setting where instead now both the measures (in the form of quantum states), and the positive definite kernels, are operator valued. The non-commutative theory entails a systematic study of positive operator valued measures, abbreviated POVMs. And quantum states (normal states) are indexed by normalized positive trace-class operators. In the non-commutative theory, the parallel to the commutative/scalar valued theory helps us understand entanglement in quantum information. A further implication of our study of the non-commutative framework will entail an interplay between the two cases, scalar valued, vs operator valued.

quant-ph↗

New duality in choices of feature spaces via kernel analysis

We present a systematic study of the family of positive definite (p.d.) kernels with the use of their associated feature maps and feature spaces. For a fixed set $X$, generalizing Loewner, we make precise the corresponding partially ordered set $Pos\left(X\right)$ of all p.d. kernels on $X$, as well as a study of its global properties. This new analysis includes both results dealing with applications and concrete examples, including such general notions for $Pos\left(X\right)$ as the structure of its partial order, its products, sums, and limits; as well as their Hilbert space-theoretic counterparts. For this purpose, we introduce a new duality for feature spaces, feature selections, and feature mappings. For our analysis, we further introduce a general notion of dual pairs of p.d. kernels. Three special classes of kernels are studied in detail: (a) the case when the reproducing kernel Hilbert spaces (RKHSs) may be chosen as Hilbert spaces of analytic functions, (b) when they are realized in spaces of Schwartz-distributions, and (c) arise as fractal limits. We further prove inverse theorems in which we derive results for the analysis of $Pos\left(X\right)$ from the operator theory of specified counterpart-feature spaces. We present constructions of new p.d. kernels in two ways: (i) as limits of monotone families in $Pos\left(X\right)$, and (ii) as p.d. kernels which model fractal limits, i.e., are invariant with respect to certain iterated function systems (IFS)-transformations.

math.FA↗

Efficient support ticket resolution using Knowledge Graphs

A review of over 160,000 customer cases indicates that about 90% of time is spent by the product support for solving around 10% of subset of tickets where a trivial solution may not exist. Many of these challenging cases require the support of several engineers working together within a "swarm", and some also need to go to development support as bugs. These challenging customer issues represent a major opportunity for machine learning and knowledge graph that identifies the ideal engineer / group of engineers(swarm) that can best address the solution, reducing the wait times for the customer. The concrete ML task we consider here is a learning-to-rank(LTR) task that given an incident and a set of engineers currently assigned to the incident (which might be the empty set in the non-swarming context), produce a ranked list of engineers best fit to help resolve that incident. To calculate the rankings, we may consider a wide variety of input features including the incident description provided by the customer, the affected component(s), engineer ratings of their expertise, knowledge base article text written by engineers, response to customer text written by engineers, and historic swarming data. The central hypothesis test is that by including a holistic set of contextual data around which cases an engineer has solved, we can significantly improve the LTR algorithm over benchmark models. The article proposes a novel approach of modelling Knowledge Graph embeddings from multiple data sources, including the swarm information. The results obtained proves that by incorporating this additional context, we can improve the recommendations significantly over traditional machine learning methods like TF-IDF.

cs.AI↗