arXiv research

Algorithm finds ribbon disks for alternating knots, resolving sliceness for most prime knots.

Polynomial algorithm found for alternating link equivalence.

Improved clustering algorithm for large datasets.

The study calculates and analyzes alternating surgeries for various knots.

In this paper, we investigate the attractive properties of the proximal gradient algorithm with inertia. Notably, we show that using alternated inertia yields monotonically decreasing functional values, which contrasts with usual accelerated proximal gradient methods. We also provide convergence rates for the algorithm…

We present theoretical guarantees for an alternating minimization algorithm for the dictionary learning/sparse coding problem. The dictionary learning problem is to factorize vector samples $y^{1},y^{2},\ldots, y^{n}$ into an appropriate basis (dictionary) $A^*$ and sparse vectors $x^{1*},\ldots,x^{n*}$. Our algorithm …

Study dynamics of alternating minimization for bilinear regression under large system limits.

We establish a characterization of alternating links in terms of definite spanning surfaces. We apply it to obtain a new proof of Tait's conjecture that reduced alternating diagrams of the same link have the same crossing number and writhe. We also deduce a result of Banks and Hirasawa-Sakuma about Seifert surfaces for…

This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with $n$ agents (users) $\{x_i\}_{i \in [n]}$ and $m$ alternatives (items) $\{y_j\}_{j \in [m]}$, each of which is associated with a latent feat…

New algorithm for online collaborative filtering using linear bandits and alternating least squares.

Let $L$ be an oriented link with an alternating diagram $D$. It is known that $L$ is a fibered link if and only if the surface $R$ obtained by applying Seifert's algorithm to $D$ is a Hopf plumbing. Here, we call $R$ a Hopf plumbing if $R$ is obtained by successively plumbing finite number of Hopf bands to a disk. In t…

We give a topological characterisation of alternating knot exteriors based on the presence of special spanning surfaces. This shows that alternating is a topological property of the knot exterior and not just a property of diagrams, answering an old question of Fox. We also give a characterisation of alternating link e…

UCB algorithm adapted for large-scale, non-sub-Gaussian problems.

This paper proposes an alternating back-propagation algorithm for learning the generator network model. The model is a non-linear generalization of factor analysis. In this model, the mapping from the continuous latent factors to the observed signal is parametrized by a convolutional neural network. The alternating bac…

We propose two new alternating direction methods to solve "fully" nonsmooth constrained convex problems. Our algorithms have the best known worst-case iteration-complexity guarantee under mild assumptions for both the objective residual and feasibility gap. Through theoretical analysis, we show how to update all the al…

We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex constraint set. This type of setting arises naturally in high-dimensional statistics and signal processing, where the variables often reflect…

Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems. Theoretical guarantees for Alternating Minimization have been hard to come by and are still poorly understood. This is in part because the heuristic is iterative and non-conve…

Paper proposes an algorithm for PARAFAC2-based CMTF models with various constraints.

In this paper, we consider solving a class of nonconvex and nonsmooth problems frequently appearing in signal processing and machine learning research. The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem. …

We give an algorithm for computing the Teichmüller polynomial for a certain class of fibered alternating links associated to trees. Furthermore, we exhibit a mutant pair of such links distinguished by the Teichmüller polynomial.

Alt-GDA outperforms Sim-GDA in minimax games with near-optimal local convergence.

Many applications require recovering a ground truth low-rank matrix from noisy observations of the entries, which in practice is typically formulated as a weighted low-rank approximation problem and solved by non-convex optimization heuristics such as alternating minimization. In this paper, we provide provable recover…

Paper proposes algorithms for solving nonconvex-nonconcave problems with complexity guarantees.

Paper develops efficient AltMin algorithm for SRPCP robust matrix recovery.

The alternating gradient descent (AGD) is a simple but popular algorithm which has been applied to problems in optimization, machine learning, data ming, and signal processing, etc. The algorithm updates two blocks of variables in an alternating manner, in which a gradient step is taken on one block, while keeping the …

Efficiently completes low-rank matrices with nearly linear time complexity.

Simple proof of knot genus theorem using Alexander polynomial.

Method debiases alternative data for fair credit underwriting.

Two algorithms converge to dictionary learning with geometric rate for non-uniform data.

Stochastic algorithm achieves sublinear convergence for bi-objective optimization.

Model analyzes cooccurrence data for recommender systems and item relevance.

New tensor completion method converges linearly and is highly practical.

In this paper, we propose a general framework to accelerate significantly the algorithms for nonnegative matrix factorization (NMF). This framework is inspired from the extrapolation scheme used to accelerate gradient methods in convex optimization and from the method of parallel tangents. However, the use of extrapola…

This paper defines less discriminatory algorithms and explores their feasibility.

This paper explores orthonormalization layers as alternatives to batch normalization.

New convergence analysis for Lasso l1 reweighting improves practical performance.

Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. More than four decades after it was first proposed, the seminal error reduction algorithm of (Gerchberg and Saxton 1972) and (Fienup 1982) is still the popular choice for solving many variants o…

We simplify symmetric NMF by transforming it into a nonsymmetric problem, enabling faster and more efficient solutions.

The paper analyzes convergence properties of NGA and PAMe for $L_1$-norm PCA.

A new algorithm balances fairness in clustering to avoid discrimination.

The book explores alternatives to worst-case analysis for algorithm performance.

New theory shows how learning algorithms can create a bias towards negative outcomes.

Sparse coding is a basic task in many fields including signal processing, neuroscience and machine learning where the goal is to learn a basis that enables a sparse representation of a given set of data, if one exists. Its standard formulation is as a non-convex optimization problem which is solved in practice by heuri…

Paper uses DBSCAN variation to detect ship anomalies.

We investigate an existing distributed algorithm for learning sparse signals or data over networks. The algorithm is iterative and exchanges intermediate estimates of a sparse signal over a network. This learning strategy using exchange of intermediate estimates over the network requires a limited communication overhea…

A new framework tackles CASH problem with alternating optimization and Rising Bandits.

New algorithms improve convergence of minimax optimization.

New algorithm improves learning efficiency in multi-task contextual bandits.