arXiv research

Study tackles nonlinear factor models with unknown monotone links from incomplete and noisy data.

New deficit functions link elliptic and parabolic inequalities, proving log Sobolev.

In a recent work "Arc-presentation of links: Monotonic simplification" Ivan Dynnikov showed that each rectangular diagram of the unknot, composite link, or split link can be monotonically simplified into a trivial, composite, or split diagram, respectively. The following natural question arises: Is it always possible t…

We prove that any arc-presentation of the unknot admits a monotonic simplification by elementary moves; this yields a simple algorithm for recognizing the unknot. We obtain similar results for split links and composite links.

We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences.…

Brenier isotonic regression extends multi-output isotonic regression using optimal transport.

We study a generalized functional related to the pullback metrics (3). We derive the first variation formula which yield stationary maps. We introduce the stress-energy tensor which is naturally linked to conservation law and yield monotonicity formula via the coarea formula and comparison theorem in Riemannian geometr…

We prove three new monotonicity formulas for manifolds with a lower Ricci curvature bound and show that they are connected to rate of convergence to tangent cones. In fact, we show that the derivative of each of these three monotone quantities is bounded from below in terms of the Gromov-Hausdorff distance to the neare…

Develops a two-level monotonic multistage recommender system for better user-specific prediction.

Under certain topological assumptions, we show that two monotone Lagrangian submanifolds embedded in the standard symplectic vector space with the same monotonicity constant cannot link one another and that, individually, their smooth knot type is determined entirely by the homotopy theoretic data which classifies the …

Efficiently learns Single-Index Models with constant factor approximation.

Gaussian process (GP) modulated Cox processes are widely used to model point patterns. Existing approaches require a mapping (link function) between the unconstrained GP and the positive intensity function. This commonly yields solutions that do not have a closed form or that are restricted to specific covariance funct…

A new method learns proper multiclass losses and probabilities.

In this paper, we prove an extended version of the Minkowski Inequality, holding for any smooth bounded set $Ω\subset \mathbb R^n$, $n\geq 3$. Our proof relies on the discovery of effective monotonicity formulas holding along the level set flow of the $p$-capacitary potentials associated with $Ω$, for every $p$ suffici…

Probit Monotone BART estimates binary outcomes using monotonic functions.

Trivial links are unique up to number of link components, but they can be hard to recognize from arbitrary diagrams. We define a new measure of the complexity of a link embedding, the crumple, and show how this may be used to measure progress toward a trivial embedding. In conjunction with a modified form of arc presen…

Introduces Fitzpatrick losses, tighter than Fenchel-Young losses.

In this note, we describe a new link between Perelman's monotonicity formula for the reduced volume and ideas from optimal transport theory.

Paper proposes an alternative to MLE for GLMs with non-canonical link functions.

Monotone neural networks can approximate and interpolate functions efficiently.

We study deep neural networks with polynomial activations, particularly their expressive power. For a fixed architecture and activation degree, a polynomial neural network defines an algebraic map from weights to polynomials. The image of this map is the functional space associated to the network, and it is an irreduci…

The paper develops algorithms to restore monotonicity in non-monotone functions.

The paper investigates quantitative rigidity using Colding's monotonicity formulas for Ricci curvature.

Paper proposes fitting loss functions to data using source functions from information geometry.

The study finds monotonic properties of harmonic functions on 3-manifolds with a flat end.

In [S. Basu, A. Gabrielov, N. Vorobjov, Semi-monotone sets. arXiv:1004.5047v2 (2011)] we defined semi-monotone sets, as open bounded sets, definable in an o-minimal structure over the reals, and having connected intersections with all translated coordinate cones in R^n. In this paper we develop this theory further by d…

Paper extends ranking metrics theory for financial positions.

Paper extends ranking metrics theory for financial positions.

In the paper, the author studies properties of three functions relating to the exponential function and the existence of partitions of unity, including accurate and explicit computation of their derivatives, analyticity, complete monotonicity, logarithmically complete monotonicity, absolute monotonicity, and the like.

Researchers found infinite links with specific bridge positions.

Study learns a neuron with non-monotonic activation functions.

In this paper, we study monotonicity formulas of eigenvalues and entropies along the rescaled List's extended Ricci flow. We derive some monotonicity formulas of eigenvalues of Laplacian which generalize those of Li in [8] and Cao-Hou-Ling in [3]. Moreover, we also consider monotonicity formulas of $\mathcal{F}_k$-func…

Derives monotonic quantities for $p$-harmonic functions on manifolds.

The paper solves MMV and MV problems with random coefficients and finds shared optimal strategies.

In this paper we generalize the monotonicity formulas of [C] for manifolds with nonnegative Ricci curvature. Monotone quantities play a key role in analysis and geometry; see, e.g., [A], [CM1] and [GL] for applications of monotonicity to uniqueness. Among the applications here is that level sets of Green's function on …

Study on $p$-Green functions on specific manifolds, proving monotonicity.

Researchers extend monotonicity formulas for harmonic functions in RCD(0,N) spaces.

Study causal inference under specific sampling methods with monotonicity assumptions.

We propose some natural generalizations of Reidemeister moves that do not increase the number of crossings in the generated diagrams. Experimentations make us conjecture that this class of monotonic moves is complete for computing canonical forms and then deciding isotopy.

Monotonic differentiable sorting networks improve upon previous methods.

Paper tackles stochastic $k$-submodular bandits with full feedback, achieving sublinear regret.

New algorithms for generalized linear bandits with unknown reward functions.

The paper is motivated by a problem concerning the monotonicity of insurance premiums with respect to their loading parameter: the larger the parameter, the larger the insurance premium is expected to be. This property, usually called loading monotonicity, is satisfied by premiums that appear in the literature. The inc…

We propose learning deep models that are monotonic with respect to a user-specified set of inputs by alternating layers of linear embeddings, ensembles of lattices, and calibrators (piecewise linear functions), with appropriate constraints for monotonicity, and jointly training the resulting network. We implement the l…

We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a nonparametric model of monotonic functions that allows for interpretable priors and principled quantification of hierarchical uncertainty. W…

Derive monotone quantities for harmonic functions on asymptotically flat 3-manifolds with nonnegative scalar curvature.

Develops monotone tree-based GAMI models using XGBoost.

We prove monotonicity of a parabolic frequency on manifolds. This is a parabolic analog of Almgren's frequency function. Remarkably we get monotonicity on all manifolds and no curvature assumption is needed. When the manifold is Euclidean space and the drift operator is the Ornstein-Uhlenbeck operator this can been see…