arXiv research

Paper shows approximate Dirichlet domain works as well as exact one for tiling hyperbolic balls.

Paper connects free-energy and low-degree hardness in high-dimensional statistics.

Recent research has made significant progress on the problem of bounding log partition functions for exponential family graphical models. Such bounds have associated dual parameters that are often used as heuristic estimates of the marginal probabilities required in inference and learning. However these variational est…

In this paper, we propose a simple, versatile model for learning the structure and parameters of multivariate distributions from a data set. Learning a Markov network from a given data set is not a simple problem, because Markov networks rigorously represent Markov properties, and this rigor imposes complex constraints…

Analysis shows exploding and vanishing gradients in neural nets with ReLU activations.

RealStats detects fake images rigorously, combining multiple detectors for robustness.

We provide a rigorous numerical computation method to validate periodic, homoclinic and heteroclinic orbits as the continuation of singular limit orbits for the fast-slow system $x' = f(x,y,ε), y' = εg(x,y,ε)$ with one-dimensional slow variable $y$. Our validation procedure is based on topological tools called isolatin…

The Goldman-Parker Conjecture classifies the complex hyperbolic C-reflection ideal triangle groups up to discreteness. We proved the Goldman-Parker Conjecture in [Ann. of Math. 153 (2001) 533--598] using a rigorous computer-assisted proof. In this paper we give a new and improved proof of the Goldman-Parker Conjecture.…

This paper proposes a novel uncertainty quantification framework for computationally demanding systems characterized by a large vector of non-Gaussian uncertainties. It combines state-of-the-art techniques in advanced Monte Carlo sampling with Bayesian formulations. The key departure from existing works is the use of i…

The study of Platonic solids' unfoldings leads to high genus Teichmüller curves.

Machine learning should incorporate maximum likelihood for better estimation.

Researchers prove existence of a stable self-similar blowup solution.

For a given cusped 3-manifold $M$ admitting an ideal triangulation, we describe a method to rigorously prove that either $M$ or a filling of $M$ admits a complete hyperbolic structure via verified computer calculations. Central to our method are an implementation of interval arithmetic and Krawczyk's Test. These techni…

ANNs efficiently approximate high-dimensional Black-Scholes PDEs without the curse of dimensionality.

We prove two conjectures of C. Gordon. We show that the maximal number of exceptional Dehn surgeries on a 1-cusped hyperbolic 3-manifold is 10, and that the maximal intersection number between exceptional slopes is 8. The proof uses a combination of new geometric techniques and a rigorous computer-assisted calculation.

We discuss the existence of Killing tensors for certain (physically motivated) stationary and axially symmetric vacuum space-times. We show nonexistence of a nontrivial Killing tensor for a Tomimatsu-Sato metric (up to valence 7), for a C-metric (up to valence 9) and for a Zipoy-Voorhees metric (up to valence 11). The …

We outline a rigorous algorithm, first suggested by Casson, for determining whether a closed orientable 3-manifold M is hyperbolic, and to compute the hyperbolic structure, if one exists. The algorithm requires that a procedure has been given to solve the word problem in π_1(M).

The study finds dense clusters of solutions in a simple neural network model, providing bounds for their existence.

STAND-DA improves AD in DA target domains with limited data.

ConformalHDC improves HDC's uncertainty quantification for neuromorphic learning.

Researchers use physics methods to predict gaps between computational and statistical problems.

Into a geometric setting, we import the physical interpretation of index theorems via semi-classical analysis in topological quantum field theory. We develop a direct relationship between Fedosov's deformation quantization of a symplectic manifold X and the BV quantization of a one-dimensional sigma model with target X…

New Einstein metrics constructed on complex line bundle over CP1.

A spin network is a cubic ribbon graph labeled by representations of $\mathrm{SU}(2)$. Spin networks are important in various areas of Mathematics (3-dimensional Quantum Topology), Physics (Angular Momentum, Classical and Quantum Gravity) and Chemistry (Atomic Spectroscopy). The evaluation of a spin network is an integ…

We present three equivalent definitions of $S^1$-equivariant symplectic homology. We show that, using rational coefficients, the positive part of $S^1$-equivariant symplectic homology is isomorphic to linearized contact homology, when the latter is defined. We present several computations and applications, and introduc…

Survey on rigorous construction of supersymmetric path integral.

Chern-Simons theory on a closed contact three-manifold is studied when the Lie group for gauge transformations is compact, connected and abelian. A rigorous definition of an abelian Chern-Simons partition function is derived using the Faddeev-Popov gauge fixing method. A symplectic abelian Chern-Simons partition functi…

This survey reviews portfolio choice in settings where investment opportunities are stochastic due to, e.g., stochastic volatility or return predictability. It is explained how to heuristically compute candidate optimal portfolios using tools from stochastic control, and how to rigorously verify their optimality by mea…

Defines interpretability in machine learning and calls for rigorous evaluation.

Paper provides a rigorous proof of the index theorem for economists.

This thesis explores geometric stacks and Poisson manifolds, proving new results in their classification and equivalence.

Constructs a rigorous path integral for supersymmetric spin manifolds.

Paper derives CLT for Bayesian neural networks trained with variational inference.

Efficiently estimates optimal transport maps with rigorous guarantees.

Estimating boundaries from point clouds with improved accuracy and rigorous error estimates.

We prove the Goldman-Parker Conjecture: A complex hyperbolic ideal triangle group is directly embedded in PU(2,1) if and only if the product of its three standard generators is not elliptic. We also prove that such a group is indiscrete if the product of its three standard generators is elliptic. A novel feature of thi…

New method reduces Gibbs partition function estimation complexity.

Efficient algorithm for clustering and classification using MBO scheme.

We prove properties of linking forms on rational homology spheres.

We study recursive-cube-of-rings (RCR), a class of scalable graphs that can potentially provide rich inter-connection network topology for the emerging distributed and parallel computing infrastructure. Through rigorous proof and validating examples, we have corrected previous misunderstandings on the topological prope…

New methods improve Reservoir Computing for chaotic time series prediction.

Paper introduces a new, tractable measure of model complexity.

The issue of computing (co)homology generators of a cell complex is gaining a pivotal role in various branches of science. While this issue can be rigorously solved in polynomial time, it is still overly demanding for large scale problems. Drawing inspiration from low-frequency electrodynamics, this paper presents a ph…

A hybrid impurity measure balances theoretical soundness and computational efficiency.

This paper provides a mathematical framework for time-delay reservoir computing.

Study shows limits of certain normalizing flows in higher dimensions.

In this paper, we establish a rigorous correspondence between the two tube algebras, that one comes from the Turaev-Viro-Ocneanu TQFT introduced by Ocneanu and another comes from the sector theory introduced by Izumi, and construct a canonical isomorphism between the centers of the two tube algebras, which is a conjuga…

Deep Bayesian neural networks effectively select variables with rigorous uncertainty quantification.