arXiv research

A learner selects subsets of choices for a user who then picks from them, aiming to minimize regret.

A distributed method for Bayesian model choice using marginal likelihood and Monte Carlo sampling.

The paper proposes a new method to learn choice functions using Pareto-embeddings.

New algorithms minimize regret in combinatorial online learning with relative feedback.

New invariants defined for knots and links using quandle representations.

PAC Battling-Bandit tackles online learning with subset choice and Plackett-Luce feedback.

For a Hamiltonian action of a compact group $U$ of isometries on a compact Kähler manifold $Z$ and a compatible subgroup $G$ of $U^{\mathbb{C}}$, we prove that for any closed $G$--invariant subset $Y\subset Z$ the image of the gradient map $μ_{\mathfrak{p}}(Y)$ is independent of the choice of the invariant Kähler form …

We introduce Neural Choice by Elimination, a new framework that integrates deep neural networks into probabilistic sequential choice models for learning to rank. Given a set of items to chose from, the elimination strategy starts with the whole item set and iteratively eliminates the least worthy item in the remaining …

Algorithm identifies best item from subsets with random utility model feedback.

Novel optimization method detects change points in Gaussian data.

With the rapidly growing scales of statistical problems, subset based communication-free parallel MCMC methods are a promising future for large scale Bayesian analysis. In this article, we propose a new Weierstrass sampler for parallel MCMC based on independent subsets. The new sampler approximates the full data poster…

Gaussian Processes are widely used for regression tasks. A known limitation in the application of Gaussian Processes to regression tasks is that the computation of the solution requires performing a matrix inversion. The solution also requires the storage of a large matrix in memory. These factors restrict the applicat…

Paper generalizes Markov chain model to handle dynamic preferences and choice overload.

This paper consists of two parts. First, motivated by classic results, we determine the subsets of a given nilpotent Lie algebra $\mathfrak{g}$ (respectively, of the Grassmannian of two-planes of $\mathfrak{g}$) whose sign of Ricci (respectively, sectional) curvature remains unchanged for an arbitrary choice of a posit…

We consider the problem of learning the preferences of a heterogeneous population by observing choices from an assortment of products, ads, or other offerings. Our observation model takes a form common in assortment planning applications: each arriving customer is offered an assortment consisting of a subset of all pos…

BWS selects best window subsets for efficient data pruning.

In-degree quiver polynomials for surface-links computed.

New algorithm LUMB reduces regret for linear utility multinomial logit bandit.

The paper tackles context-dependent choice functions, proposing a model and neural network architectures.

The paper develops an algorithm to select a subset of training data for efficient regression models.

Study efficient algorithms for identifying minimum interventional sets to learn causal relationships.

This paper defines resource-constrained classifier performance and its impact on algorithm choice.

Modeling preference rankings with salient features to explain irrational choices.

We determine the topology of the moduli space of periodic tilings of the plane by parallelograms. To each such tiling, we associate combinatorial data via the zone curves of the tiling. We show that all tilings with the same combinatorial data form an open subset in a suitable Euclidean space that is homotopy equivalen…

A new method reduces complexity in estimating dynamic choice models.

The paper tackles PAC ranking with subset-wise feedback, achieving optimal sample complexity.

Model improves BERT for answering multiple-choice questions in large texts.

We study the performance of data-driven, a priori and random approaches to label space partitioning for multi-label classification with a Gaussian Naive Bayes classifier. Experiments were performed on 12 benchmark data sets and evaluated on 5 established measures of classification quality: micro and macro averaged F1 s…

Proposes robust assortment optimization from observational data.

We study the spectrum of the Dirac operator $D$ on pseudo-Riemannian spin manifolds of signature $(p,q)$, considered as an unbounded operator in the Hilbert space $L^2_ξ(S)$. The definition of $L^2_ξ(S)$ involves the choice of a $p$-dimensional time-like subbundle $ξ\subset TM$. We establish a sufficient criterion for …

PNN-smoothing improves $k$-means clustering by merging subsets' clusterings.

Optimizes product assortment for continuous range of items under incomplete customer information.

This survey covers in our opinion the most important results in the theory of continuous selections of multivalued mappings (approximately) from 2002 through 2012. It extends and continues our previous such survey which appeared in Recent Progress in General Topology, II, which was published in 2002. In comparison, our…

This study benchmarks fifteen deep learning optimizers and identifies a subset that generally performs well.

The paper develops methods for unconstrained optimization on Riemannian manifolds.

An optimal algorithm maximizes submodular functions online with no-1/2 regret.

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.…

Active learning is a powerful approach to analyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to which the query is being optimized. The standard information gain, for example, does not permit an accurate evaluation with a small committee, …

Develops a method to optimize hyperparameters for subsampling methods.

Optimizes sparse fine-tuning for privacy in neural networks.

The paper realizes 6 supergeometries for the Lie superalgebra D(2,1;a).

When tracking user-specific online activities, each user's preference is revealed in the form of choices and comparisons. For example, a user's purchase history is a record of her choices, i.e. which item was chosen among a subset of offerings. A user's preferences can be observed either explicitly as in movie ratings …

We consider some metrics and weak metrics defined on the Teichmueller space of a surface of finite type with nonempty boundary, that are defined using the hyperbolic length spectrum of simple closed curves and of properly embedded arcs, and we compare these metrics and weak metrics with the Teichmüller metric. The comp…

Simplified feature selection using a single agent with restructured choice strategy.

Develops gradient boosting for multi-label classification.

GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremental algorithm for identifying a subspace of Rn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis has shown that GROUSE converges locally at an expected linea…

Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector $w^* \in \mathbb{R}^d$ that represents the "qualities" of the $d$ ite…

A family of maximum mean discrepancy (MMD) kernel two-sample tests is introduced. Members of the test family are called Block-tests or B-tests, since the test statistic is an average over MMDs computed on subsets of the samples. The choice of block size allows control over the tradeoff between test power and computatio…