arXiv research

Calculates winning probability for three candidates based on support rates and information timing.

Proposes MOGFNs for generating diverse Pareto optimal solutions in multi-objective optimization.

A new method reduces variance in training early-stage rankers for large-scale search systems.

Dealing with previously unseen slots is a challenging problem in a real-world multi-domain dialogue state tracking task. Other approaches rely on predefined mappings to generate candidate slot keys, as well as their associated values. This, however, may fail when the key, the value, or both, are not seen during trainin…

Study certifies missed relevant items in candidate generation with audit labels.

Conformal Candidate Certification advances offline MBO by certifying candidate designs with statistical guarantees.

Image classification problems are typically addressed by first collecting examples with candidate labels, second cleaning the candidate labels manually, and third training a deep neural network on the clean examples. The manual labeling step is often the most expensive one as it requires workers to label millions of im…

The paper addresses biased preferences in candidate selection, proposing a fair and utility-maximizing algorithm.

Differentially Private algorithms often need to select the best amongst many candidate options. Classical works on this selection problem require that the candidates' goodness, measured as a real-valued score function, does not change by much when one person's data changes. In many applications such as hyperparameter o…

OP-GFNs sample candidates in order-preserving proportion to a learned reward function.

Develops PLL methods that are provably consistent and compatible with any deep network.

Among the machine learning applications to business, recommender systems would take one of the top places when it comes to success and adoption. They help the user in accelerating the process of search while helping businesses maximize sales. Post phenomenal success in computer vision and speech recognition, deep learn…

The paper tackles adaptive questioning to classify candidate ability.

Prophet predicts device qualities for FL to reduce training latency.

Bayesian optimization uses triangulation candidates for better performance.

A method selects candidates based on predictions with statistical control.

Model analyzes how firms balance full disclosure with selective disclosure to maintain a good reputation.

To a branched cover between closed, connected and orientable surfaces one associates a "branch datum", which consists of the two surfaces, the total degree d, and the partitions of d given by the collections of local degrees over the branching points. This datum must satisfy the Riemann-Hurwitz formula. A "candidate su…

A new method for Bayesian optimization uses Voronoi tessellation candidates to reduce search time.

Duel-Evolve uses LLM self-preferences for test-time optimization of discrete outputs.

Fast covariance calculation is required both for SLAM (e.g.~in order to solve data association) and for evaluating the information-theoretic term for different candidate actions in belief space planning (BSP). In this paper we make two primary contributions. First, we develop a novel general-purpose incremental covaria…

Bayesian-guided method selects optimal design from large candidate pool.

MIRA scores assess conditional distribution accuracy using joint samples.

This paper proposes a method for multi-class classification problems, where the number of classes K is large. The method, referred to as Candidates vs. Noises Estimation (CANE), selects a small subset of candidate classes and samples the remaining classes. We show that CANE is always consistent and computationally effi…

SCOPE-FE improves feature engineering efficiency for high-dimensional datasets.

Proposes TCV for selecting models in regions of interest.

Improves algorithm selection for thousands of candidates using dyadic features.

Paper develops a new algorithm to improve screening processes.

Improves classifier accuracy in ambiguous data settings.

A fair policy for hiring candidates from different groups is proposed in a linear contextual bandit problem.

Simulated annealing improves candidate optimization for multi-objective Bayesian optimization.

Paper proposes a method to recover accurate labels from partially valid data in multi-label learning.

This work improves molecular design by efficiently selecting diverse candidate molecules.

For a branched cover between two closed orientable surfaces, the Riemann-Hurwitz formula relates the Euler characteristics of the surfaces, the total degree of the cover, and the total length of the partitions of the degree given by the local degrees at the preimages of the branching points. A very old problem asks whe…

ConfHit provides valid guarantees for generative models without oracle access.

Online method selects candidates from data streams, ensuring irreversible decisions.

Robustly computes intrinsic coordinates on point clouds using resampling and averaging.

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

Meta-algorithm selects optimal model complexity in RL with function approximation.

Partial label learning (PLL) aims to solve the problem where each training instance is associated with a set of candidate labels, one of which is the correct label. Most PLL algorithms try to disambiguate the candidate label set, by either simply treating each candidate label equally or iteratively identifying the true…

Develops a method for identifying structured dynamical systems from data.

Algorithmic trading systems on DEXs reject most candidate tokens, but the counterfactual outcome of rejected candidates is rarely measured.

Ivy combines weak IV candidates to estimate causal effects robustly.

Imagine a large firm with multiple departments that plans a large recruitment. Candidates arrive one-by-one, and for each candidate the firm decides, based on her data (CV, skills, experience, etc), whether to summon her for an interview. The firm wants to recruit the best candidates while minimizing the number of inte…

While recent NAS algorithms are thousands of times faster than the pioneering works, it is often overlooked that they use fewer candidate operations, resulting in a significantly smaller search space. We present PR-DARTS, a NAS algorithm that discovers strong network configurations in a much larger search space and a s…

When recruiting job candidates, employers rarely observe their underlying skill level directly. Instead, they must administer a series of interviews and/or collate other noisy signals in order to estimate the worker's skill. Traditional economics papers address screening models where employers access worker skill via a…

The study finds generalized torsion elements in 3-manifolds from specific knot surgeries.

Estimates variance function using aggregation methods in regression models.