arXiv research

Neural eliminators reduce unreliable classification by eliminating improbable classes.

Proposes a flexible tournament design combining knockout and round-robin.

Improved elimination strategies for adaptive bandit identification reduce sample complexity and computational burden.

AE-DQN learns to eliminate sub-optimal actions in complex RL environments.

New algorithm eliminates arms to minimize regret in complex bandit problems.

Tensor variable elimination for plated factor graphs enables exact inference in models with repeated structure.

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 …

Aims to eliminate domain bias in authentication without domain labels.

This paper proves adding neurons eliminates all bad local minima in deep learning.

We simplify Khovanov homology for torus braids using Gaussian elimination.

AMBER method selects features efficiently using autoencoders and model-based elimination.

Logic approach finds real singularities in differential equations.

We develop an approach for feature elimination in statistical learning with kernel machines, based on recursive elimination of features.We present theoretical properties of this method and show that it is uniformly consistent in finding the correct feature space under certain generalized assumptions.We present four cas…

Two new feature selection algorithms improve on RFE.

PoWER-BERT speeds up BERT inference by eliminating redundant word-vectors.

GPE algorithm optimizes nonparametric contextual bandits with efficient regret bounds.

Paper tackles online learning in large MDPs with low Bellman rank using AVE algorithm.

A new method eliminates reward estimation variance in sequential decision processes.

Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we review techniques exploiting the graph structure for exact inference, b…

ANNs speed up radiation transport solving by 4x with minimal error.

Quantifier elimination enhances safety assurance of deep neural networks.

A new algorithm optimizes local objectives in federated learning with heterogeneous clients.

A new private algorithm for bandit problems meets lower bounds.

Paper eliminates warm-up phase for PO in linear MDPs, achieving optimal regret.

Robust algorithm optimizes corrupted Gaussian process bandits.

Classifies SL(n) covariant matrix-valued valuations on Lp-spaces.

The paper discusses the impossibility of eliminating surplus intersections in Lagrangian submanifolds.

Balances and eliminates base algorithms in bandits and RL to bound total regret.

New method eliminates domain size restrictions for X-ray transform inversion.

New method simplifies optimization landscapes by transforming saddle points.

This paper clarifies vine copula structures using graph and matrix representations.

NodeDrop reduces network size by eliminating unnecessary features.

An algorithm learns from multiple models to match an oracle's risk.

SurvNet selects important variables in DNNs with false discovery rate control.

This paper has been withdrawn. Its new version has been published.

SDD improves DD for estimating treatment effects by adjusting for confounding.

New algorithms identify Pareto optimal sets in multi-objective bandit problems.

New study shows non-adaptive trials can be outperformed by adaptive designs in treatment selection.

Extensive neural networks eliminate the need for SABR pricing formulas.

In this paper, we first give a new simple proof to the elimination theorem of definite fold by homotopy for generic smooth maps of manifolds of dimension strictly greater than $2$ into the $2$--sphere or into the real projective plane. Our new proof has the advantage that it is not only constructive, but is also algori…

The paper introduces a method to eliminate latent discrimination in predictive models.

Fairness in Naive Bayes classifiers by identifying and eliminating discrimination patterns.

We consider the best-arm identification problem in multi-armed bandits, which focuses purely on exploration. A player is given a fixed budget to explore a finite set of arms, and the rewards of each arm are drawn independently from a fixed, unknown distribution. The player aims to identify the arm with the largest expe…

Method eliminates nuisance functions for spatially varying causal effects.

Unknot recognition is one of the fundamental questions in low dimensional topology. In this work, we show that this problem can be encoded as a validity problem in the existential fragment of the first-order theory of real closed fields. This encoding is derived using a well-known result on SU(2) representations of kno…

We study the general structure of formal perturbative solutions to the Hamiltonian perturbations of spatially one-dimensional systems of hyperbolic PDEs. Under certain genericity assumptions it is proved that any bihamiltonian perturbation can be eliminated in all orders of the perturbative expansion by a change of coo…

The paper tackles best arm identification in contaminated bandits with optimal error guarantees and sample complexity.

Margin maximization in the hard-margin sense, proposed as feature elimination criterion by the MFE-LO method, is combined here with data radius utilization to further aim to lower generalization error, as several published bounds and bound-related formulations pertaining to lowering misclassification risk (or error) pe…