On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,051 papers · 148 categories

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48 results for “multiple query criteria”

A novel active learning framework for classification: using weighted rank aggregation to achieve multiple query criteriacs.LG

A novel MQCAL method selects high-value samples via weighted rank aggregation.

problem Lack of scalable and general integration criteria for MQCAL methods.

method Proposes a novel MQCAL method using weighted rank aggregation.

result Achieves superior results compared to state-of-the-art MQCALs.

active learning classification weighted rank aggregation multiple query criteria

2018-09-27abs ↗pdf ↗

Active Learning for Graph Embeddingcs.LG

Graph embedding provides an efficient solution for graph analysis by converting the graph into a low-dimensional space which preserves the structure information. In contrast to the graph structure data, the i.i.d. node embedding can be processed efficiently in terms of both time and space. Current semi-supervised graph…

2017-05-15abs ↗pdf ↗

Pareto-depth for Multiple-query Image Retrievalcs.IR

Most content-based image retrieval systems consider either one single query, or multiple queries that include the same object or represent the same semantic information. In this paper we consider the content-based image retrieval problem for multiple query images corresponding to different image semantics. We propose a…

2014-02-21abs ↗pdf ↗

Regional based query in graph active learningcs.LG

New methods improve graph classification accuracy using regional uncertainty.

problem Improving graph classification accuracy without labeled data.

method Proposed two new selection criteria for active learning: regional uncertainty and extended page-rank.

result Extended page-rank outperforms other methods when the fraction of labeled nodes is low.

Graph classification Active learning Regional uncertainty Page-rank Uncertainty measure

2019-06-20abs ↗pdf ↗

Multi-criteria Anomaly Detection using Pareto Depth Analysiscs.LG

We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. In most anomaly detection algorithms, the dissimilarity between data samples is calculated by a single criterion, such as Euclidean distance. However, in many cases there may not exist …

2011-10-17abs ↗pdf ↗

Criteria and Curvatures for Singularities of Finite Multiplicities of Curves in

\boldsymbol{R}^N

math.DG

The paper provides criteria and curvatures for singularities of curves in R^N.

problem Tackles the classification and characterization of singularities of curves in R^N.

method Systematic procedure for constructing criteria, explicit criteria for multiplicities 2-4, generalized curvatures.

result Generalized curvatures reinterpret Fukui's theorem for curves of finite multiplicities.

singularities curves finite multiplicities curvatures R^N

2026-05-12abs ↗pdf ↗

Optimal Experiments for Partial Causal Effect Identificationcs.AI

Optimal experiments tighten causal effect bounds efficiently.

problem Selecting experiments to tighten causal effect bounds from observational data.

method Formalized as max-potency problem, NP-hard. Polynomial-programming framework with graphical pruning criteria.

result Pruning criteria reduce search space significantly, enabling efficient experiment selection.

causal inference experimental design epistemic potency graphical criteria polynomial programming

2026-05-07abs ↗pdf ↗

Learning Smooth Distance Functions via Queriescs.LG

The paper tackles learning smooth distance functions using query-based methods.

problem Learning smooth distance functions under query constraints.

method Global and local approaches using Mahalanobis distance functions.

result Quadratic query complexity for both additive and multiplicative approximations.

distance learning query-based learning smooth functions approximation theory Mahalanobis distance

2024-12-02abs ↗pdf ↗

Relative performance criteria of multiplicative form in complete marketsq-fin.MF

The study examines Nash equilibria in utility maximization games with multiplicative performance criteria.

problem Existence and uniqueness of Nash equilibria in multiplicative performance criteria games.

method General characterization of Nash equilibria for a large class of utility functions.

result Existence and uniqueness of Nash equilibria for arbitrary initial wealth vectors.

Nash Equilibria Utility Maximization Relative Performance Criteria Multiplicative Form Complete Markets

2023-03-14abs ↗pdf ↗

Active Ordinal Querying for Tuplewise Similarity Learningstat.ML

InfoTuple efficiently selects larger tuple queries for ranking multiple objects, improving efficiency and consistency.

problem Efficiently selecting and ranking multiple objects for similarity learning.

method Adaptive selection method using mutual information maximization.

result InfoTuple outperforms state-of-the-art methods on synthetic and human response datasets.

machine learning similarity learning adaptive selection mutual information tuple queries

2019-10-09abs ↗pdf ↗

Scenario theory for multi-criteria data-driven decision makingstat.ML

Develops scenario theory for multi-criteria decision making.

problem Need for robustness assessment with multiple criteria and datasets.

method Collectively treats risks associated with individual criteria for multi-criteria decision problems.

result More accurate robustness certificates and sharper quantification of simultaneous criterion satisfaction.

scenario theory multi-criteria decision making robustness uncertainty data-driven

2026-04-01abs ↗pdf ↗

Data-driven preference learning methods for value-driven multiple criteria sorting with interacting criteriacs.LG

New method for sorting with interacting criteria using value functions and convex programming.

problem Learning models for sorting with interacting criteria.

method Additive piecewise-linear value function, convex quadratic programming, regularization, classification methods.

result The proposed method outperforms classical methods in sorting tasks.

Preference learning Multiple criteria sorting Interacting criteria Convex programming Regularization

2019-05-21abs ↗pdf ↗

A Cost-Effective Framework for Preference Elicitation and Aggregationcs.LG

We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision. We illustrate the viability of the fram…

2018-05-14abs ↗pdf ↗

Multi-Task Multicriteria Hyperparameter Optimizationcs.LG

New method finds optimal hyperparameters for multiple tasks and criteria.

problem Finding optimal hyperparameters for multiple tasks and criteria.

method Multi-Task Multi Criteria (MTMC) method that provides Pareto-optimal solutions.

result The method selects optimal hyperparameters based on given criteria significance coefficients.

hyperparameter optimization multi-task multi-criteria convolutional neural networks

2020-02-15abs ↗pdf ↗

Learning to Coordinate Multiple Reinforcement Learning Agents for Diverse Query Reformulationcs.LG

A method for efficient reinforcement learning query reformulation.

problem Efficiently learn diverse strategies for query reformulation.

method A framework with specialized sub-agents and a meta-agent trained on full data.

result Improved generalization performance and diversity of reformulation strategies.

reinforcement learning query reformulation diversity meta-agent reinforcement learning agents

2018-09-27abs ↗pdf ↗

OneFlowSBI: One Model, Many Queries for Simulation-Based Inferencestat.ML

Unified framework for simulation-based inference learns a single model for multiple tasks.

problem Simulation-based inference for multiple tasks with limited model retraining.

method Unified flow-matching generative model with query-aware masking distribution.

result Competitive performance on various inference tasks and real-world problems.

simulation-based inference flow-matching generative model query-aware posterior sampling

2026-01-30abs ↗pdf ↗

Hierarchical Subquery Evaluation for Active Learning on a Graphcs.CV

To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between ru…

2015-04-30abs ↗pdf ↗

Distribution Aware Active Learningstat.ML

Discriminative learning machines often need a large set of labeled samples for training. Active learning (AL) settings assume that the learner has the freedom to ask an oracle to label its desired samples. Traditional AL algorithms heuristically choose query samples about which the current learner is uncertain. This st…

2018-05-23abs ↗pdf ↗

Inference-Aware Meta-Alignment of LLMs via Non-Linear GRPOstat.ML

Inference-aware meta-alignment of LLMs reduces computational cost.

problem Aligning LLMs to diverse human preferences is challenging due to conflicting criteria.

method IAMA trains a base model to be aligned to multiple tasks via different inference-time alignment algorithms, using non-linear GRPO for optimization.

result IAMA enables effective alignment of LLMs to multiple criteria with limited computational budget.

LLMs alignment inference-time non-linear optimization probability measures

2026-02-02abs ↗pdf ↗

Statistical Comparisons of Classifiers by Generalized Stochastic Dominancestat.ML

Paper proposes a new method to compare classifiers across multiple datasets.

problem Comparing classifiers over multiple datasets with multiple criteria.

method Adopting decision theory, the paper introduces generalized stochastic dominance for ranking classifiers.

result Generalized stochastic dominance can be used to rank classifiers and statistically tested.

classifier comparison stochastic dominance decision theory machine learning multiple datasets

2022-09-05abs ↗pdf ↗

Active Learning for One-Class Classification Using Two One-Class Classifierscs.LG

A new active learning method for one-class classification using two classifiers.

problem Reducing manual labeling efforts in one-class classification.

method Uses two one-class classifiers for active learning, proposing new query strategies.

result Improved results compared to existing methods on various datasets.

active learning one-class classification two classifiers query strategies improved performance

2019-01-10abs ↗pdf ↗

Budgeted Multiple-Expert Deferralcs.LG

Budgeted deferral framework reduces expert query costs in machine learning.

problem High cost of querying all experts in deferral algorithms.

method Introduces budgeted deferral framework for selective expert querying.

result Empirical results show substantial reduction in training costs without sacrificing accuracy.

deferral machine learning active learning cost minimization label complexity

2025-10-30abs ↗pdf ↗

Curvature criteria of A-simple singularities R,0-> R^2,0 and their parallel curvesmath.DG

Curvature criteria for A-simple singularities and their parallel curves identified.

problem Determining singularity types of A-simple singularities and their parallel curves.

method Defined curvature parameters and criteria for A-simple singularities.

result Criteria to determine singularity types of A-simple singularities and their parallel curves.

singularities curvature A-simple parallel curves

2025-12-29abs ↗pdf ↗

BioNLP-OST 2019 RDoC Tasks: Multi-grain Neural Relevance Ranking Using Topics and Attention Based Query-Document-Sentence Interactionscs.LG

Systems rank PubMed abstracts and sentences for RDoC criteria, achieving high mAP and MAA.

problem Lack of RDoC labeled datasets and complex labelling process hinder full use of RDoC framework.

method Attention-based neural topic models, supervised and unsupervised sentence ranking models, BM25, BoW, TF-IDF.

result Best systems achieved 1st rank with 0.86 mAP and 0.58 MAA.

RDoC PubMed Relevance Ranking Neural Topic Model Sentence Ranking

2019-10-01abs ↗pdf ↗

Multi-criteria Similarity-based Anomaly Detection using Pareto Depth Analysiscs.CV

We consider the problem of identifying patterns in a data set that exhibit anomalous behavior, often referred to as anomaly detection. Similarity-based anomaly detection algorithms detect abnormally large amounts of similarity or dissimilarity, e.g.~as measured by nearest neighbor Euclidean distances between a test sam…

2015-08-20abs ↗pdf ↗

Query Complexity of Bayesian Private Learningcs.LG

Lower bounds on queries for Bayesian private learning.

problem Estimating a target within error ε while keeping it hidden from an adversary.

method Lower bound proof using Fano's inequality and proportional-sampling estimators.

result Query complexity is on the order of

L\log(1/\varepsilon)

for accurate estimation.

Bayesian learning private learning query complexity Fano's inequality proportional sampling

2019-11-15abs ↗pdf ↗

Partial Rankings of Optimizerscs.LG

Framework benchmarks optimizers on multiple criteria.

problem Benchmarking optimizers across diverse test functions.

method Union-free generic depth function for partial orders/rankings.

result Identifies central and outlying rankings of optimizers.

optimizers benchmarking partial rankings depth function ordinal information

2024-02-26abs ↗pdf ↗

Toward Metric Indexes for Incremental Insertion and Queryingcs.DS

In this work we explore the use of metric index structures, which accelerate nearest neighbor queries, in the scenario where we need to interleave insertions and queries during deployment. This use-case is inspired by a real-life need in malware analysis triage, and is surprisingly understudied. Existing literature ten…

2018-01-12abs ↗pdf ↗

"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalizationcs.IR

Personalizes query completion in digital commerce with image-based vectors.

problem High bounce rate and low recurring users in query completion.

method Injects dense vectors from product images into a noisy channel model.

result Image-based personalization improves query completion and user experience.

personalization query completion digital commerce image vectors noisy channel

2020-03-11abs ↗pdf ↗

Sharing Hash Codes for Multiple Purposesstat.ML

Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissimilarity measures. However, hash codes significantly depend on the dissimilarity, which prohibits users from adjusting the dissimilarity at q…

2016-09-11abs ↗pdf ↗

Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Expertscs.LG

New algorithm tackles dynamic query routing to multiple embedding models.

problem Dynamic query routing to multiple embedding models under adversarial conditions.

method Formalized as adversarial contextual linear bandit with low-rank experts, proposed HPG algorithm.

result HPG algorithm achieves linearized policy regret of

ilde{\mathcal O}(s\sqrt{M T})

embedding models contextual bandits policy regret adversarial feedback low-rank structure

2026-06-12abs ↗pdf ↗

Stress-Plus-X (SPX) Graph Layoutcs.CG

SPX optimizes multiple graph drawing metrics for better readability.

problem Graph drawing algorithms often optimize one metric at a time, leading to suboptimal layouts.

method Introduces Stress-Plus-X (SPX) framework that optimizes stress, crossings, angles, and upwardness simultaneously.

result SPX achieves results close to state-of-the-art algorithms that optimize metrics individually.

graph drawing layout optimization readability stress crossings

2019-08-04abs ↗pdf ↗

A unified framework for bandit multiple testingstat.ML

Unified framework controls false discovery rate in bandit multiple testing.

problem Designing adaptive algorithms to identify true discoveries in multiple hypothesis testing.

method Unified modular framework using e-processes for FDR control in arbitrary settings.

result Unified framework ensures FDR control for dependent and simultaneous arm queries.

bandit multiple testing false discovery rate e-processes martingales adaptive algorithms

2021-07-15abs ↗pdf ↗

Optimal Query Allocation in Extractive QA with LLMs: A Learning-to-Defer Framework with Theoretical Guaranteescs.CL

Optimal query allocation improves extractive QA efficiency with LLMs.

problem Efficiency and reliability in extractive question answering with LLMs.

method Learning-to-Defer framework that allocates queries to specialized models.

result Enhanced answer reliability with reduced computational overhead.

extractive QA LLMs query allocation efficiency reliability

2024-10-21abs ↗pdf ↗

Playing to Learn Better: Repeated Games for Adversarial Learning with Multiple Classifierscs.LG

Game theory helps machine learn better from adversarial queries.

problem Adversarial evasion in machine learning prediction.

method Repeated Bayesian Sequential Game to balance classifier selection and query type.

result Learner selects appropriate classifier for clean vs. adversarial queries.

Game theory Adversarial learning Machine learning Classifier selection Reinforcement learning

2020-02-10abs ↗pdf ↗

Choquet rating criteria, risk measures, and risk consistencyq-fin.RM

The paper introduces risk consistency properties for credit ratings.

problem Promoting prudent investment decisions in credit ratings.

method Introducing and studying risk consistency properties in the framework of Choquet rating criteria.

result Characterization of Choquet risk measures and rating criteria satisfying risk consistency properties.

credit ratings risk consistency Choquet theory risk measures portfolio diversification

2025-06-16abs ↗pdf ↗

Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goalcs.LG

Unified perspective unites Bayesian optimization and active learning for efficient goal-oriented optimization.

problem Efficiently optimize expensive engineering and scientific problems with limited data.

method Unified framework linking Bayesian infill criteria and active learning criteria.

result Unified approach formalizes Bayesian infill criteria and active learning criteria.

Bayesian optimization active learning goal-oriented optimization adaptive sampling infill criteria

2023-03-02abs ↗pdf ↗

Separability criteria for loops via the Goldman bracketmath.GT

Criteria for loop separability on surfaces using Goldman bracket.

problem Determining when two loops on an oriented surface can have disjoint representatives.

method Algebraic criteria in terms of the Goldman bracket, extended using hyperbolic geometry.

result Explicit algebraic conditions for loop separability on surfaces.

loop separability Goldman bracket oriented surface hyperbolic geometry surface topology

2025-11-23abs ↗pdf ↗

Active Collaborative Filteringcs.IR

Collaborative filtering (CF) allows the preferences of multiple users to be pooled to make recommendations regarding unseen products. We consider in this paper the problem of online and interactive CF: given the current ratings associated with a user, what queries (new ratings) would most improve the quality of the rec…

2012-10-19abs ↗pdf ↗

MISO-wiLDCosts: Multi Information Source Optimization with Location Dependent Costscs.LG

Optimizes black-box functions with varying costs across multiple sources.

problem Optimizing black-box functions with varying costs across multiple sources.

method Uses Augmented Gaussian Process and Gaussian Process to model fidelity and location-dependent costs, respectively. Uses Confidence Bound acquisition function to select sources and locations.

result The approach significantly outperforms existing methods on Hyperparameters Optimization tasks.

black-box optimization multi-information sources location-dependent costs augmented Gaussian Process Gaussian Process

2021-02-09abs ↗pdf ↗

Robust Batch-Level Query Routing for Large Language Models under Cost and Capacity Constraintscs.LG

Optimizes query routing to LLMs under cost and resource constraints.

problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.

method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.

result Robust routing framework improves accuracy by 1-14% over non-robust methods.

query routing large language models cost constraints resource management batch-level optimization

2026-03-25abs ↗pdf ↗

A framework for the extraction of Deep Neural Networks by leveraging public datacs.LG

Framework extracts deep neural networks from public data.

problem Extracting deep neural networks from black-box models.

method Active learning and large public datasets.

result Improved performance over uniform noise baseline.

model extraction deep learning active learning public data black-box access

2019-05-22abs ↗pdf ↗

FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomesstat.ML

FADE framework improves fairness and accuracy in ensemble learning.

problem Improving fairness in existing models without sacrificing accuracy.

method Flexible fair ensemble learning framework targeting multiple fairness criteria.

result Multiple unfairness measures can be minimized simultaneously with little impact on accuracy.

fairness ensemble learning accuracy counterfactual outcomes theoretical guarantees

2021-09-01abs ↗pdf ↗

Faster PAC Learning and Smaller Coresets via Smoothed Analysiscs.LG

This paper improves coreset size via smoothed analysis.

problem Efficiently computing small subsets that approximate query errors.

method Smoothed analysis for approximate average error over queries.

result Deterministic and randomized algorithms for smaller coresets.

PAC learning coresets smoothed analysis streaming vector summarization

2020-06-09abs ↗pdf ↗

When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairnesscs.LG

This paper examines fairness and arbitrariness in bias mitigation methods.

problem Understanding how different bias mitigation strategies affect individual predictions and whether they introduce arbitrariness.

method FRAME framework to evaluate bias mitigation through five dimensions: Impact Size, Change Direction, Decision Rates, Affected Subpopulations, and Neglected Subpopulations.

result Significant differences in the behaviors of debiasing methods were exhibited, highlighting the limitations of current fairness criteria and the inherent arbitrariness in the debiasing process.

bias mitigation fairness criteria FRAME framework multiplicity arbitrariness

2023-02-14abs ↗pdf ↗

Preference learning along multiple criteria: A game-theoretic perspectivecs.LG

A game-theoretic approach to multi-criteria ranking from ordinal data.

problem Ranking objects from ordinal data with multiple criteria.

method Generalizing von Neumann winner to multi-criteria setting using Blackwell's approachability.

result The Blackwell winner can be computed as a convex optimization problem and achieves near-optimal sample complexity.

multi-criteria ranking game theory Blackwell approachability von Neumann winner convex optimization

2021-05-05abs ↗pdf ↗

Multiple Code Hashing for Efficient Image Retrievalcs.LG

A new hashing framework learns multiple hash codes for each image to improve hash bucket search efficiency.

problem Existing hashing methods fail to handle complex image retrieval scenarios efficiently.

method Multiple Code Hashing (MCH) framework with deep reinforcement learning.

result Significant improvement in hash bucket search performance compared to single-code methods.

hashing image retrieval reinforcement learning hash bucket search deep learning

2020-08-04abs ↗pdf ↗

Clustering with Noisy Queriesstat.ML

In this paper, we initiate a rigorous theoretical study of clustering with noisy queries (or a faulty oracle). Given a set of $n$ elements, our goal is to recover the true clustering by asking minimum number of pairwise queries to an oracle. Oracle can answer queries of the form : "do elements $u$ and $v$ belong to the…

2017-06-22abs ↗pdf ↗