arXiv research

Paper tackles efficient learning of non-convex hypotheses in metric spaces.

There is a well developed theory of weakly symmetric Riemannian manifolds. Here it is shown that several results in the Riemannian case are also valid for weakly symmetric pseudo-Riemannian manifolds, but some require additional hypotheses. The topics discussed are homogeneity, geodesic completeness, the geodesic orbit…

Weakly convex polyhedra which are star-shaped with respect to one of their vertices are infinitesimally rigid. This is a partial answer to the question whether every decomposable weakly convex polyhedron is infinitesimally rigid. The proof uses a recent result of Izmestiev on the geometry of convex caps.

Proximal methods avoid local minima in weakly convex problems.

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\ell_1$ norm. For a cl…

Smooth analog of Gromov's dihedral rigidity for 3D weakly convex domains.

Expanding FCCO to non-smooth weakly-convex problems, improving deep learning performance.

Neural network approximates weakly efficient frontier of convex vector optimization problems.

On R^n endowed with a riemannian metric of bounded nonpositive curvature, the weakly convex closed subsets are topologically trivial. The stability of such subsets under intersection characterizes the euclidean spaces.

Adaptive algorithm AMSGrad converges for weakly convex constrained optimization problems.

This paper improves inverse problem solving with weakly convex regularisers and proves convergence.

SGD avoids critical points on weakly convex functions.

We generalize the following result of White: Suppose $N$ is a compact, strictly convex domain in $\RR^3$ with smooth boundary. Let $Σ$ be a compact 2-manifold with boundary. Then a generic smooth curve $Γ\cong \partialΣ$ in $\partial N$ bounds an odd or even number of embedded minimal surfaces diffeomorphic to $Σ$ acco…

Improves label propagation for weakly supervised learning.

New algorithm solves complex non-convex problems efficiently.

New adaptive methods solve weakly convex stochastic optimization problems.

Study rigidity in Penrose's singularity theorem with weakly trapped surfaces.

Sequential tests for nonparametric hypotheses using supermartingales.

Paper analyzes convergence of stochastic methods under heavy-tailed noise.

The paper proves a Schwarz lemma for weakly Kähler-Finsler manifolds.

In this paper, we consider first-order convergence theory and algorithms for solving a class of non-convex non-concave min-max saddle-point problems, whose objective function is weakly convex in the variables of minimization and weakly concave in the variables of maximization. It has many important applications in mach…

Study on polyhedra rigidity, finding non-existence of flexible weakly convex decomposable polyhedra.

New PnP algorithm converges with relaxed proximal gradient descent.

New single-loop algorithm tackles weakly convex constraints in stochastic optimization.

The study compares spectral volumes of manifolds with weakly convex boundaries.

Decentralized method solves saddle point problems with theoretical guarantees.

A submanifold $M^m$ of a Euclidean space $R^{m+p}$ is said to have harmonic mean curvature vector field if $Δ\vec{H}=0$, where $\vec{H}$ is the mean curvature vector field of $M\hookrightarrow R^{m+p}$ and $Δ$ is the rough Laplacian on $M$. There is a conjecture named after Bangyen Chen which states that submanifolds o…

Develops a new SPP algorithm with variance reduction for weakly convex optimization.

Gradient descent performs well on weakly convex losses, offering generalization guarantees.

In this paper we consider three-manifolds with weakly umbilic boundary (the Second Fundamental form of the boundary is a constant multiple of the metric). We show that if the initial manifold has positive Ricci curvature and the boundary is convex (nonnegative Second Fundamental form), its metric can be deformed via th…

Many high dimensional sparse learning problems are formulated as nonconvex optimization. A popular approach to solve these nonconvex optimization problems is through convex relaxations such as linear and semidefinite programming. In this paper, we study the statistical limits of convex relaxations. Particularly, we con…

Marginally outer trapped surfaces are widely considered as the best quasi-local replacements for event horizons of black holes in General Relativity. However, this equivalence is far from being proved, even in stationary and static situations. In this paper we study an important aspect of this equivalence, namely wheth…

Paper extends SMM to weakly convex and multi-convex surrogates for non-convex optimization.

Paper proposes an algorithm for sampling from complex mixture distributions without requiring smoothness.

The paper proves optimal estimates and inequalities for spectral functions on certain manifolds.

Strict convexity proven for certain self-expanders in high dimensions.

Lower bound found for Steklov eigenvalue on curved manifolds.

Properties of two classes of generally convex sets in the n-dimentional real Euclidean space, called m-semiconvex and weakly m-semiconvex, 1<=m<n, are investigated in the present work. In particular, it is established that an open set with smooth boundary in the plan which is weakly 1-semiconvex but not 1-semiconvex co…

In this paper, we show that if the optimization function is restricted-strongly-convex (RSC) and restricted-smooth (RSM) -- a rich subclass of weakly submodular functions -- then a streaming algorithm with constant factor approximation guarantee is possible. More generally, our results are applicable to any monotone we…

We consider compact convex hypersurfaces contracting by functions of their curvature. Under the mean curvature flow, uniformly convex smooth initial hypersurfaces evolve to remain smooth and uniformly convex, and contract to points after finite time. The same holds if the initial data is only weakly convex or non-smoot…

We introduce the cutting construction of possibly non-compact symplectic toric manifolds, in particular, toric symplectic cones that correspond to a weakly convex good cone. Since the symplectization of a toric contact manifold is a toric symplectic cone, we can also construct toric contact manifolds that correspond to…

We study convex polyhedra in $\mathbb{R}\mathbb{P}^3$ with all their vertices on a sphere. We do not require, in particular, that the polyhedra lie in the interior of the sphere, hence the term "weakly inscribed". Such polyhedra can be interpreted as ideal polyhedra, if we regard $\mathbb{R}\mathbb{P}^3$ as a combinati…

This paper introduces a general multi-class approach to weakly supervised classification. Inferring the labels and learning the parameters of the model is usually done jointly through a block-coordinate descent algorithm such as expectation-maximization (EM), which may lead to local minima. To avoid this problem, we pr…

Unified approach tackles high-dimensional tensor bandits with convex optimization and weakly decomposable regularizers.

We consider Blackwell approachability, a very powerful and geometric tool in game theory, used for example to design strategies of the uninformed player in repeated games with incomplete information. We extend this theory to "generalized quitting games" , a class of repeated stochastic games in which each player may ha…

We show that under appropriate hypotheses, a plumbing of symplectic surfaces in a symplectic 4-manifold admits strongly convex neighborhoods. Moreover the neighborhoods are Lefschetz fibered with an easily-described open book on the boundary supporting the induced contact structure. We point out some applications to cu…

The study classifies weakly almost Fuchsian manifolds and proves geometric properties.

A distributed subgradient method tackles non-convex optimization problems in networks.