Papers

Many stochastic systems in operations and economics exhibit feedback between their long-run state distribution and the transition law governing their dynamics. In this paper, we develop a computational framework for stationary equilibria in such measure-dependent Markov systems when this feedback operates through a finite-dimensional aggregate. We show that the original stationary-equilibrium problem can be reduced to a finite-dimensional self-consistency equation, separating steady-state analysis of the underlying Markov system from equilibrium computation. We use properties of the resulting self-consistency map to guide the choice among fixed-point iteration, relaxed fixed-point iteration, and minimization of the fixed-point residual. The last approach requires derivatives of the self-consistency map, which are typically unavailable in closed form. We therefore develop finite-time infinitesimal perturbation analysis estimators for these derivatives, with error bounds that separate Monte Carlo error from finite-time bias. We illustrate the framework through a strategic $G/G/c$ queue and an opinion-dynamics model, showing how different structural properties lead naturally to different equilibrium-computation methods.

The problem of the Nile has been stated by R. Fisher in 1936. This problem concerns the fair division of an agricultural land and is the eponymous problem of a general class of problems on measures and similar regions. In 1938, W. Feller has shown that the general problem has no solution. However, we will show that under a natural hypothesis, the original problem of the Nile is solvable with connected plots.

We establish Malliavin nondegeneracy for every weighted integral against a singular Hermite kernel, with all negative moments of the Malliavin derivative norm finite, uniformly over admissible compact families of bounded deterministic weights. In particular, all finite-dimensional distributions of Hermite processes of every fixed finite order have Schwartz densities, as do vectors of non-overlapping increments and, more generally, finite families of Wiener integrals with respect to a Hermite process. For such vectors, linear independence of the weights is both necessary and sufficient for absolute continuity; under this condition, all inverse Malliavin determinant moments exist and the joint density belongs to the Schwartz space, with bounds that are again uniform over compact families. The proof rests on a nonvanishing condition on compact weight families that is preserved under directional differentiation. An analytic-tail property of fractional transforms of the directions verifies this condition at every chaos level. Uniform Malliavin estimates then convert lower-order gradient bounds into bounded joint densities of arbitrarily many first directional derivatives, and Bessel's inequality closes the induction. The argument works directly with fixed non-Gaussian laws and singular kernels, without a Gaussian-limit assumption and without a separate local-nondeterminism transfer.

One method of generalizing a distribution is to add new parameter to the baseline distribution. In this paper the author discusses this methodology to create a new six parameters generalized Weibull-Kumaraswamy-{Lomax) Distribution. The author utilizes the approach of the T-X{Y} family to add new parameters the baseline distribution Kumaraswamy distribution defined on the unit interval with two shape parameters a & b>0. The author used the Lomax distribution with two parameters {\alpha} & {\lambda}>0 , scale and shape parameters respectively, to link the baseline distribution with the generator distribution. The generator distribution is the Weibull distribution with the two parameters, c & {\gamma}>0 , shape and scale parameters respectively as will be explained in the paper. The result is a new distribution with six parameters. The author derived the PDF, CDF, and quantile functions. The author used a new technique to expand the pdf in a linear series expansion that is simple and does not require special function. This makes the computational statistics for the raw moments easier and simpler. The author discusses the Shannon entropy and the paper contains figures for the PDF, CDF and the quantile functions.

We give a self-contained proof, in the bounded variation setting, of the Hambly–Lyons uniqueness theorem, which states that (total) signature identifies the path up to tree-like equivalences. The argument is organized around two key geometric observations. First, tree-like paths have trivial signature because factorization over a loop in a tree $\tau:[0,1]\to T$ is preserved under signature lifts, which follows from an elementary property of planar curves. Second, a path with trivial total signature contains a nontrivial subpath with trivial total signature (the sub-interval lemma). This is proven by applying a winding-number argument to a two-dimensional projection of the signature lift. Collapsing all trivial-signature sub-intervals then defines a compact metric tree $T$ through which the original path factors by virtue of the sub-interval lemma.

We introduce a new method for obtaining sharp $L^p$ estimates for martingales under predictable analogues of differential subordination. By allowing suitable comparisons between continuous and jump variation adapted to the relevant range of $p$, we retain sharp constants in settings where pathwise differential subordination fails. The key idea is to study together the continuous and jump contributions arising from the appropriate Bellman functions. Although these contributions need not be nonpositive separately, we quantify their defects and show that they compensate at the predictable level. This compensation mechanism is inspired by the author's earlier work. As an application, we substantially improve the explicit dimension-free bounds for the Riesz vectors on the Hamming cube and on $\mathbb{Z}^n$.

We study a class of strongly local, regular Dirichlet forms on the standard unit interval. The aim of our work is to record some Hölder regularity properties that have not been noted in the prior literature. In particular, as our main result, we show that for every $\delta \in (0,1]$ there exists a metric measure space $(X,d,\mu)$ equipped with a strongly local, regular Dirichlet form $(\mathcal{E},\mathcal{F})$ on $L^2(\mu)$ with the property that $\delta$ is the supremum of $\alpha \in (0,1]$ for which the domain $\mathcal{F}$ of the Dirichlet form contains a non-constant $\alpha$-Hölder continuous function. To the best of our knowledge, such examples were previously known only for $\delta = 1$. In our construction, the value $\delta$ is characterized by the upper Hausdorff dimension of a certain Radon measure that is used to define $(\mathcal{E},\mathcal{F})$.

The Lehmer transform of a positive signal is the ratio of its power sums at consecutive orders, read as a function of the order, and it is a strictly increasing analytic curve from the smallest observation to the largest. Composing this curve with a map whose inverse is the Lambert function turns it into a probability law on the real line, the Lehmer-Lambert distribution, which we introduce and study. Its distribution, density and quantile functions are closed-form, its random variates are exact, and the law is invariant under changes of gain and of power-law calibration of the signal. Its modes lie at orders at which the signal changes scale, and for a sample its tails are exponential with rates equal to the two extreme logarithmic gaps of the sample. We derive moments, generating functions and limit theorems, and we investigate applications of the Lehmer-Lambert distribution in the analysis of electroencephalograms of patients with major depressive disorder.

Stochastic gradient descent (SGD) with gradient clipping and additive noise has become a standard technique for training machine learning models, particularly in applications requiring robustness or privacy guarantees. However, clipping introduces a bias in stochastic gradients, while additive noise introduces additional variance, making the long-run behaviour of individual optimization trajectories difficult to characterize. In this work, we prove that SGD with clipping and additive Gaussian noise (SGD-CN) converges almost surely (a.s.) under smoothness and uniformly bounded stochastic-gradient noise assumptions, provided the step sizes satisfy some standard decaying conditions. Our analysis extends to momentum variants such as the stochastic heavy ball and Nesterov's accelerated gradient, where we show that careful energy constructions yield similar guarantees. These results provide stronger theoretical foundations for understanding the pathwise behaviour of clipped stochastic gradient methods and suggest that, despite the bias and noise introduced by clipping and perturbation, the algorithm remains stable in both convex and nonconvex regimes.

We develop a martingale-transform framework for Dunkl harmonic analysis and apply it to prove $L^p$ estimates for the Dunkl Riesz transforms using the martingale decomposition of the Dunkl process. A fundamental difference from the classical Brownian setting is that the martingale representing the Dunkl Riesz transform of a function is not, in general, differentially subordinated to the Poisson martingale of the function itself. Our main argument bypasses this obstruction by applying Burkholder's Bellman function directly. The possible positive defect of the continuous Itô drift is compensated by the negative contribution produced by the reflection jumps. This yields $L^p$ estimates for single Dunkl Riesz transforms for $1<p<\infty$, and vector-valued estimates for $p\geq2$ with a spectral dependence on the root system. For $G$-invariant functions, differential subordination can be recovered and the resulting estimates are independent of the root system.

Outliers are important for stress-testing algorithms and understanding system behaviour under rare conditions. Despite being commonly described as low-likelihood events, existing generative approaches rarely control likelihood explicitly. In this work, we introduce a measure-theoretic notion of outliers based on the distribution of log-likelihood values, which is guaranteed to assign higher probability mass to low-likelihood events with a specifiable magnitude. Building on this formulation, we derive how likelihood reweighting modifies the diffusion score and use this relation to motivate a controlled modification of the reverse-time dynamics. In particular, likelihood reweighting implies a scaling of the score function with a control term derived from the Radon-Nikodym derivative of the likelihood distributions. Correspondingly, the updated score function can be obtained with no retraining of the diffusion model. We exploit the Ornstein-Uhlenbeck semigroup underlying diffusion models to motivate an exponentially interpolated controller which approximates the true control. Experiments demonstrate controlled generation of low-likelihood samples while remaining consistent with the data geometry.

We study limit theorems for random dynamical systems. We recast the random dynamics via the skew-product of the two-point motion (the Varadhan trick). Using this approach we prove several statistical properties for the system, including quenched central limit theorems with rates, quenched local limit theorem, large deviations, and decay of correlations. Then, in applications we show how to verify the general conditions using spectral properties of appropriate operators. A key application is the study of effectively expanding on average random diffeomorphisms (in any dimension, which are allowed to be dissipative). In particular, this is the first instance of proving local limit theorems in such a broad setting, without imposing restrictions on the dynamics. We provide several new examples.

We study the mixing time of a simple random walk on the small-world network defined by adding edges to $\mathbb{Z}_n^d$ as follows: for each pair $\{x,y\}$ we add an edge with probability $Z_n/\|x-y\|^{d}$ with $Z_n$ chosen so that the average number of added edges to every vertex is $1$. When $d\geq 3$, we show that with high probability the mixing time is of order $\log n$ and that the random walk does not exhibit cutoff.

We study an entanglement transition at logarithmic depth in random hypercube linear optical networks. Starting from an initial Gaussian state with all modes squeezed, we prove that random hypercube linear optical networks on $n$ modes generate ensemble-averaged subsystem entanglement within a constant factor of its maximum in all subsystems at circuit depth $\Theta(\log n)$. Below depth $\log_2n$, there is an extensive subsystem with no entanglement. The entanglement generation in logarithmic depth can be compared to recent progress towards $O(\log n)$ depth average-case sampling hardness for Gaussian boson sampling in a hypercube network.

Wasserstein spaces carry two natural geometries: a vertical one, inherited from the linear structure of signed measures, and a horizontal one, induced by couplings. We prove that a Radon-Nikodym property holds in both. In the vertical geometry, bounded subsets of $P_p(R^d)$ are dentable in an ambient Banach space which itself fails the Radon-Nikodym property; we derive variational principles, Radon-Nikodym theorems for vector measures and martingale convergence results. In the horizontal geometry, we show that a Radon-Nikodym property is inherited from the one of spaces of random variables. We introduce horizontal martingales and prove that they are exactly the laws of $L^p$ -valued martingales.

We establish an a priori bound for the dynamical parabolic $\Phi_3^4$ model with harmonic potential. This bound yields the global well-posedness of the equation and, via the Krylov-Bogoliubov method, the existence of an invariant measure, shown to be non-Gaussian. The argument builds on the strategy developed by Mourrat and Weber for the periodic $\Phi_3^4$ model, with substantial modifications to handle the non-compact geometry of $\mathbb{R}^3$ and the spectral framework imposed by the harmonic oscillator. We further prove that this measure is unique in the small-coupling regime.

In this paper, we develop a linear response theory for a class of stochastic differential equations driven by jump processes. We investigate the response of the system to small time-dependent perturbations from three complementary perspectives: probability density functions, mean exit times, and escape probabilities. By performing perturbation analyses of the corresponding forward and backward Kolmogorov equations, we derive the first-order response equations governing these statistical quantities and establish explicit linear response formulas, which characterize the sensitivity of the evolution of the probability distribution and the escape behavior of the system to variations in its coefficients.

We show that the adjacency operator of the $1$-skeleton of any regular tiling of the hyperbolic plane has no nonzero square-integrable eigenfunctions. As a consequence, the same holds for every infinite connected regular graph admitting a proper planar embedding with regular dual.

The Free $\mathbf{w}$-Maximal Spanning Forest (FMaxSF) is a weighted generalization of the classical Free Minimal Spanning Forest (FMSF) that is able to detect nonhyperfiniteness in percolation on nonunimodular graphs. We introduce a parameterized family of invariant random spanning forests that interpolates between these models. For every finite positive value of the parameter $\beta$, the construction retains many of the desired properties of FMaxSF while also admitting the finite-subtree forcing property of FMSF. We study local limits of these forests and, in particular, show that the small-$\beta$ limit of the wired variant coincides with FMSF if and only if $p_h=p_u$, where $p_h$ is the threshold for the existence of heavy clusters and $p_u$ is the uniqueness threshold for Bernoulli$(p)$ percolation. Finally, we show that the Free and the Wired $\mathbf{w}$-Maximal Spanning Forests may coincide even if $p_h<p_u$, providing a negative answer to a question of Terlov and Timár.

Let $A_1,\ldots,A_n$ be independent $d \times d$ real symmetric Gaussian random matrices, and consider the linear operator $A(x) = n^{-1/2}\sum_{i=1}^n x_i A_i$, $x\in \mathbb{R}^n$. We construct an iterative algorithm in the Approximate Message Passing family which iterates over $A$ and its adjoint $A^*$, and establish a state evolution result which characterizes its behavior in the limit $d\rightarrow \infty, 2n/d^2 \rightarrow \alpha$ in terms of a correlated Gaussian-semicircular process in a free probability space, in the sense of strong convergence of operators. We then apply this iteration to the random matrix discrepancy problem which asks for a binary vector $x \in \{-1,+1\}^n$ such that $A(x)$ has a small operator norm. Our algorithm achieves an operator norm $2\sigma(\alpha)$, for an explicit expression of the standard deviation $\sigma(\alpha)<1$ for all $0<\alpha<\alpha_* \simeq 5.74$. This resolves the algorithmic question of Kunisky-Zhang (2023) and Maillard (2025) in this interval.

We study interview positioning in the secretary problem when an applicant is uncertain about their own absolute rank. The employer uses a given rejection threshold, and the applicant's self-rank distribution is truncated geometric. We show that the applicant need only compare the first eligible position with the final position and that, for finite $n$, a unique critical value exists precisely when $r(r+1)>n-1$. If $r_n$ is the employer's rejection threshold and $r_n/n\to\alpha\in(0,1)$ as $n\to\infty$, then \[ \lim_{n\to\infty}q_c(n,r_n) = q_c(\alpha) = \frac{1-\alpha}{1-\alpha+\alpha^2}. \] Thus uncertainty about absolute rank produces an explicit asymptotic phase transition in optimal interview positioning.

Banerjee, Chen, and Sivakumar asked at COLT 2015 whether a uniform small-ball condition on the rows of a random design matrix forces a restricted-eigenvalue lower bound whose sample complexity is governed by the ordinary Euclidean Gaussian width of an arbitrary spherical subset. We give a negative answer to the natural distribution-free formulation of that question. For every sample size $n$, we construct a centered, genuinely heavy-tailed row distribution in dimension $p=4^n+1$ and a set $A=C\cap\mathbb{S}^{p-1}$, where $C$ is a closed polyhedral convex cone, such that \[ \inf_{v\ne 0}\mathbb{P}\!\left( \left|\left\langle Z,v\right\rangle\right| \ge \frac{\left\lVert v\right\rVert_2}{\sqrt{2}} \right)\ge \frac{1}{12} \quad\text{and}\quad w(A)<2. \] Nevertheless, for the matrix $X$ with $n$ independent copies of $Z$ as rows, \[ \mathbb{P}\!\left( \inf_{u\in A}\left\lVert Xu\right\rVert_2^2=0 \right) \ge 1-\exp(-2^n). \] Thus no positive constants depending only on the fixed small-ball parameters can yield a lower bound of the proposed form $c_1n-c_2w(A)^2$ with high probability. The construction isolates the obstruction: a marginal small-ball lower bound controls every fixed direction, but does not control the distribution-dependent complexity of searching over many directions. We state the quantifiers explicitly and discuss why isotropic or upper-tail assumptions lead to a different, still meaningful problem.

We prove a generalized circular rearrangement inequality: among all circular arrangements of a finite collection of positive numbers, the greedy arrangement simultaneously maximizes the sums of products of $k$ consecutive entries for every $k$. This resolves a 2023 conjecture of Holmes-Holroyd-Ramírez and extends the classical circular rearrangement inequality for products of adjacent entries. As an application, we show that the greedy ordering minimizes the speed of a cyclic birth-and-death chain, resolving another conjecture of the same authors.

In this paper, we develop a new approach to studying the Fourier transform of Gaussian multiplicative chaos. We determine the Fourier dimension of these random measures on tori for every smooth log-correlated covariance and in every dimension, and prove sharp bounds and exact formulas for chaos on natural classes of bounded sets and hypersurfaces in Euclidean space. These results highlight how boundary geometry and multifractal concentration jointly govern Fourier decay, including regimes in which the boundary strictly reduces the Fourier dimension. In the circle setting, this gives a substantially simpler new proof of a conjecture of Garban and Vargas.

We develop a cluster representation for one-dimensional CBI processes with jumps. Using this representation, we establish total variation convergence under conditions on the branching Lévy measure. In the subcritical case, we obtain polynomial and exponential rates under distinct regularity assumptions, while the strong Feller property is also established. In the critical case, we derive an explicit bound involving the cumulant integral. Our proofs use a coupling method that has proved effective for establishing ergodicity of Ornstein–Uhlenbeck processes.

Billingsley's theorem states that the normalized logarithms of the prime factors of a uniform random integer in $[1,x]$ converge to the Poisson–Dirichlet law $PD(1)$, while weighting an integer $n$ by the generalized divisor function $d_\theta(n)$ gives $PD(\theta)$. We ask what remains of this picture when the integer is also rewarded according to its largest prime factor $P^+(n)$. For fixed $\theta,\beta>0$ and $\gamma\ge0$, we sample $N_x=n\le x$ with probability proportional to $d_\theta(n)\exp\{\beta H_\gamma(n)\}$, where $H_\gamma(n)=(\log P^+(n))^\gamma$ for $\gamma>0$, while $H_0(1)=0$ and $H_0(n)=1$ for $n\ge2$. Two phase transitions occur. At $\gamma=0$ the $PD(\theta)$ partition survives, whereas every fixed $\gamma>0$ forces one prime to carry asymptotically all logarithmic mass. The second transition, at $\gamma=1$, concerns the cofactor $R_x=N_x/P^+(N_x)$. With $a_x=\beta\gamma(\log x)^{\gamma-1}$, its law is asymptotic in total variation to $Q_{a_x}(m)=d_\theta(m)m^{-1-a_x}/\zeta(1+a_x)^\theta$. Thus, for $0<\gamma<1$, $a_x\log R_x$ has a $Gamma(\theta,1)$ limit, with shape $\theta$ and rate one, and the normalized logarithmic prime factors of $R_x$ have an independent $PD(\theta)$ limit; at $\gamma=1$, $R_x$ has a nondegenerate discrete limit; and for $\gamma>1$, $R_x=1$ with high probability. We also determine the joint limits involving $N_x/x$ and the normalizing constants, which include the classical Alladi–Erdős asymptotic as a special case.

In this paper, we investigate the generalization performance of distributed gradient descent algorithms in a reproducing kernel Hilbert space under a robust loss function $l_{\sigma}$. By exploiting the spectral characterization of gradient descent together with the intrinsic properties of robust loss functions, we establish optimal learning rates for the distributed kernel-based robust gradient descent (DKRGD) algorithm with an appropriately chosen scale parameter $\sigma$. The proposed parameter choice of $\sigma$ simultaneously alleviates the saturation phenomenon and guarantees statistical robustness. A key technical contribution is a novel error analysis that provides substantially sharper bounds for products of operators, thereby significantly relaxing existing restrictions on the maximum number of local machines while retaining optimal learning rates. Finally, we develop a communication-efficient strategy that further improves the convergence performance of DKRGD.

We study the maximum visibility in a ball inside the vacant set of three obstacle models in $\mathbb R^d$ with slow decay of spatial correlations and disparate obstacle geometries: Poisson Boolean models with general i.i.d. radii distributions, Poisson cylinders and Brownian interlacements. Let $M_r$ be the maximum distance between points $x$ and $y$ in the ball $B(r)$ such that $x$ is visible from $y$. We prove that $M_r$ divided by $q_r$ converges in probability to an explicit model dependent constant, where $q_r=\log r$, except for the Brownian interlacements in dimension $d=3$, where $q_r = \log r\log\log r$.

Let $A=(A_n)_{n\ge2}$ be a triangular array of random matrices, where $A_n=(a_{ij})_{1\le i,j\le n}$ is an $n\times n$ random matrix with independent real entries satisfying $\mathbb E a_{ij}=0$ and $\mathbb Ea_{ij}^2=1$, and put $\mathcal L_n=\log|\det A_n|$ and \[ W_n^{\mathrm d}(A_n):=\frac{\mathcal L_n - \frac12\log(n-1)!}{\sqrt{\frac12\log n}},\quad W_n^{\mathrm e}(A_n):= \frac{\mathcal L_n-\mathbb E \mathcal L_n}{\sqrt{\frac12\log n}}. \] We prove that $W_n^{\mathrm d}(A_n) \Rightarrow \mathcal N(0,1)$, whenever the family $\left\{\frac{|a_{ij}|^{4}}{\sqrt{\log(e+|a_{ij}|)}} \right\}_{n\geq 2;1\leq i,j\leq n}$ is uniformly integrable. If, in addition, the entries have uniformly bounded densities, then $W_n^{\mathrm e}(A_n) \Rightarrow \mathcal N(0,1)$ whenever the family $\left\{\frac{|a_{ij}|^{4}}{\log(e+|a_{ij}|)}\right\}_{n\geq 2;1\leq i,j\leq n} $ is uniformly integrable. These two conditions are optimal at the level of universal moment assumptions. We further establish the corresponding Berry–Esseen bounds, and show that for $0<\delta\le\tfrac12$, if $\sup\limits_{n}\max\limits_{1\le i,j\le n}\mathbb E \frac{|a_{ij}|^4} {\{\log(e+|a_{ij}|)\}^{1/2-\delta}}<\infty$, then \begin{align*} d_{\mathrm K}(W_n^{\mathrm d}(A_n),\mathcal N(0,1))\le C(\log n)^{-\delta}. \end{align*} For $0<\gamma\le1$, if $\sup\limits_{n}\max\limits_{1\le i,j\le n}\mathbb E \frac{|a_{ij}|^4} {\{\log(e+|a_{ij}|)\}^{1-\gamma}}<\infty$ and the entries have uniformly bounded densities, then \begin{align*} d_{\mathrm K}(W_n^{\mathrm e}(A_n),\mathcal N(0,1))\le C(\log n)^{-\gamma}. \end{align*} When $\delta = 1/2$ and $\gamma = 1$, the bounds $(\log n)^{-1/2}$ and $(\log n)^{-1}$ are optimal, respectively. Our results improve the earlier Central Limit Theorem by \cite{BaoPanZhou2015} and the Berry–Esseen bound by \cite{NguyenVu2014}.

Random geometric graph detection asks whether a graph generated from $n$ independent uniform points on $S^{d-1}$, with edge probabilities depending on inner products, can be distinguished from an Erdos-Renyi graph of the same edge density using only its adjacency matrix. Mao, Wu, and Xu conjectured that the cubic trace of the standardized kernel determines the detection boundary: $n^3(\mathrm{tr}\,K^3)^2$ tending to zero or infinity should imply impossibility or strong detection, respectively. We construct counterexamples and prove a uniform result for spherical polynomial kernels of uniformly bounded degree with mean edge density bounded away from zero and one. For the integral operator $K$ of the standardized centered kernel, define \[ n_* = \min\left\{ \frac{1}{|\mathrm{tr}\,K^3|^{2/3}},\quad \frac{1}{\sqrt{\mathrm{tr}\,K^4}},\quad \frac{d}{\mathrm{tr}\,K^2} \right\}, \] with zero denominators interpreted as infinity. The total variation distance between the geometric and independent-edge models tends to zero if $n/n_* \to 0$ and to one if $n/n_* \to \infty$. The three scales correspond to triangles, four-cycles, and global geometry. The pure degree-four spherical harmonic kernel has only positive nonzero eigenvalues, yet its detection scale is $n \asymp d^5$. Throughout $d^5 \ll n \ll d^{16/3}$, the triangle and four-cycle signals and every fixed-degree standardized polynomial mean gap vanish, while the full graph remains strongly detectable. Thus the cubic-trace conjecture fails even without spectral sign cancellation. The impossibility proof combines higher-order spherical integration by parts, a graph expansion of cumulants, and a finite-order relative entropy comparison.