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.

168,657 papers · 148 categories

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48 results for “contextual models”

Contextualized Machine Learningstat.ML

Contextualized ML learns context-dependent effects using deep learning.

problem Learning heterogeneous and context-dependent effects in data.

method Applying deep learning to the meta-relationship between contextual information and context-specific parametric models.

result Unified framework for cluster analysis and cohort modeling.

Contextualized ML Deep Learning Heterogeneous Effects Meta-Relationship Varying Coefficients

2023-10-17abs ↗pdf ↗

Neural Network Models for Contextual Regressionstat.ML

Proposes a neural network for contextual regression.

problem Improving model efficiency and interpretability in regression with contextual features.

method Simple contextual neural network (SCtxtNN) that separates context identification from context-specific regression.

result SCtxtNN achieves lower excess mean squared error and more stable performance than feed-forward neural networks.

neural networks contextual regression model efficiency interpretability

2026-03-25abs ↗pdf ↗

Contextuality in the Bundle Approach, n-Contextuality, and the Role of Holonomyquant-ph

The bundle approach and n-contextuality reveal quantum model contextuality.

problem Understanding contextuality in quantum models using topology.

method Using the bundle approach, we describe contextuality as the non-existence of global sections in the measure bundle. We introduce n-contextuality to explore model dependence on scenario topology.

result Quantum theory and GHZ models exhibit all levels of n-contextuality, showing contextuality is related to holonomy group non-triviality.

contextuality quantum models bundle approach n-contextuality holonomy

2021-05-28abs ↗pdf ↗

Open Problem: Model Selection for Contextual Banditscs.LG

The study explores whether model selection guarantees apply to contextual bandits.

problem Applying model selection guarantees to contextual bandits.

method Investigates whether similar guarantees for model selection in statistical learning can be extended to contextual bandit learning.

result Initial findings suggest that model selection guarantees may not directly apply to contextual bandits.

contextual bandits model selection statistical learning algorithmic guarantees

2020-06-19abs ↗pdf ↗

Diffusion Models Meet Contextual Banditscs.LG

Efficiently handles contextual bandits with diffusion models.

problem Challenges in online decision-making with contextual bandits.

method Leverage pre-trained diffusion models as priors to capture action dependencies.

result Developed an algorithm for efficient posterior approximation.

diffusion models contextual bandits fast updates sampling

2024-02-15abs ↗pdf ↗

On the Expressive Power of Contextual Relations in Transformersstat.ML

Transformers model contextual relations using probabilistic measures, revealing their expressive power.

problem Lack of clear understanding of Transformer's ability to model contextual relations.

method Introduced a measure-theoretic framework connecting softmax attention and entropy-regularized optimal transport.

result Transformer architectures can approximate arbitrary contextual relations, and the choice of normalization affects how these relations are represented.

Transformer Contextual Relations Softmax Attention Entropy-regularized Optimal Transport Approximation Theorem

2026-03-26abs ↗pdf ↗

Learning Relevant Contextual Variables Within Bayesian Optimizationcs.LG

SADCBO optimizes contextual variables by balancing relevance and cost.

problem Optimizing contextual variables with varying costs and unknown relevance.

method Adaptive selection of relevant contextual variables using sensitivity analysis and early stopping.

result Consistent improvement in optimization across various examples.

Bayesian Optimization Contextual Variables Cost-Sensitive Sensitivity Analysis Early Stopping

2023-05-23abs ↗pdf ↗

Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oraclescs.LG

New algorithm estimates treatment effects for more efficient contextual bandits.

problem Contextual bandits struggle with action-independent reward redundancies.

method Reduces contextual bandits to heterogeneous treatment effect estimation.

result Heterogeneous treatment effect estimation leads to more efficient model estimation.

contextual bandits heterogeneous treatment effects reward estimation flexible algorithms

2022-03-30abs ↗pdf ↗

Estimation Considerations in Contextual Banditsstat.ML

Contextual bandit algorithms are sensitive to the estimation method of the outcome model as well as the exploration method used, particularly in the presence of rich heterogeneity or complex outcome models, which can lead to difficult estimation problems along the path of learning. We study a consideration for the expl…

2017-11-19abs ↗pdf ↗

Towards Domain Adaptive Neural Contextual Banditscs.LG

Paper tackles domain adaptation for contextual bandits with sub-linear regret.

problem Adapting contextual bandit algorithms across domains with distribution shift.

method Learn a bandit model for the target domain using feedback from the source domain.

result Sub-linear regret bound maintained across domains.

domain adaptation contextual bandits regret bound real-world datasets

2024-06-13abs ↗pdf ↗

Model Selection for Generic Contextual Banditsstat.ML

A new algorithm selects models for contextual bandits, reducing regret.

problem Model selection for stochastic contextual bandits under realizability assumption.

method Adaptive Contextual Bandit (ACB) algorithm, successive refinement phases.

result ACB algorithm achieves similar regret bound to known algorithms, with a model selection cost.

contextual bandits model selection successive refinement regret bound stochastic

2021-07-07abs ↗pdf ↗

Unsupervised Contextual Anomaly Detection using Joint Deep Variational Generative Modelsstat.ML

A method for unsupervised contextual anomaly detection is proposed using a cross-linked pair of Variational Auto-Encoders for assigning a normality score to an observation. The method enables a distinct separation of contextual from behavioral attributes and is robust to the presence of anomalous or novel contextual at…

2019-04-01abs ↗pdf ↗

Semi-Parametric Contextual Bandits with Graph-Laplacian Regularizationstat.ML

Proposes a new algorithm for graph-based semi-parametric contextual bandits.

problem Non-stationarity in human behavior and social interaction.

method SemiGraphTS algorithm for graph-based semi-parametric reward model.

result Derives an upper bound of cumulative regret for graph-based semi-parametric model.

contextual bandits graph-based semi-parametric Thompson sampling non-stationarity

2022-05-17abs ↗pdf ↗

Balanced Linear Contextual Banditscs.LG

2018-12-15abs ↗pdf ↗

Universal and data-adaptive algorithms for model selection in linear contextual banditscs.LG

New algorithms for model selection in linear contextual bandits without feature diversity conditions.

problem Model selection in linear contextual bandits without feature diversity conditions.

method Data-adaptive algorithms that provide model selection guarantees without feature diversity conditions.

result O(d^α T^{1-α}) model selection guarantees with no feature diversity conditions.

model selection linear contextual bandits data-adaptive no feature diversity regret minimization

2021-11-08abs ↗pdf ↗

How recurrent networks implement contextual processing in sentiment analysiscs.CL

This study analyzes how RNNs process context in sentiment analysis.

problem Understanding how recurrent neural networks process context in sentiment analysis.

method Developed methods to reverse engineer RNNs, identifying contextual effects and quantifying their strength and timescale.

result Identified inputs that induce contextual effects and quantified their properties.

sentiment analysis contextual processing recurrent neural networks neural networks natural language processing

2020-04-17abs ↗pdf ↗

S^3

-- Semantic Signal Separationcs.LG

Faster, context-aware topic modeling approach unveiled.

problem Slow and heavy preprocessing in topic modeling.

method Conceptualizes topics as independent axes, decomposes embeddings using ICA.

result 4.5x faster than BERTopic on average, with diverse and coherent topics.

topic modeling semantic space independent component analysis contextual embeddings fast algorithms

2024-06-13abs ↗pdf ↗

Structure Learning of Contextual Markov Networks using Marginal Pseudo-likelihoodstat.ME

A new method scores contextual Markov networks without assuming chordality.

problem Learning structure in contextual Markov networks is hard due to many possible structures.

method Marginal pseudo-likelihood as a consistent structure estimator.

result Marginal pseudo-likelihood yields a consistent structure estimator.

Markov networks contextual pseudo-likelihood structure learning inference

2021-03-29abs ↗pdf ↗

Online Posterior Sampling with a Diffusion Priorcs.LG

Proposes sampling from reverse diffusion posteriors for contextual bandits.

problem Complex distributions in contextual bandits.

method Approximate posterior sampling with a diffusion model prior using Laplace approximation.

result Empirically consistent and efficient approximations for contextual bandits.

contextual bandits diffusion model posterior sampling Laplace approximation

2024-10-04abs ↗pdf ↗

OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual banditsstat.ML

We consider the stochastic linear (multi-armed) contextual bandit problem with the possibility of hidden simple multi-armed bandit structure in which the rewards are independent of the contextual information. Algorithms that are designed solely for one of the regimes are known to be sub-optimal for the alternate regime…

2019-05-24abs ↗pdf ↗

Low-Rank Contextual Reinforcement Learning from Heterogeneous Human Feedbackstat.ML

LoCo-RLHF models diverse human feedback with contextual information.

problem Heterogeneous human feedback from diverse contexts and preferences.

method Low-rank contextual preference model, PRS policy.

result LoCo-RLHF achieves tighter sub-optimality gap than existing methods.

Reinforcement Learning Human Feedback Low-rank Structure Contextual Information Pessimism in Reduced Subspace

2024-12-27abs ↗pdf ↗

High-dimensional Nonparametric Contextual Bandit Problemstat.ML

New method tackles high-dimensional contextual bandits with flexible kernel models.

problem Maximizing rewards in decision-making scenarios with many features.

method Introduces stochastic assumptions and no-regret learning for Gaussian kernels.

result Achieves no-regret learning even with feature dimensions growing with samples.

contextual bandits kernel methods no-regret learning flexible models recommendation systems

2025-05-20abs ↗pdf ↗

A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecastingcs.LG

Proposes a new model for more accurate demand forecasting considering dynamic contextual information.

problem Traditional methods fail to capture spatio-temporal and dynamic contextual dependencies in demand forecasting.

method Integrates temporal, relational, spatial, and dynamic contextual dependencies using a Context Integrated Graph Neural Network (CIGNN).

result CIGNN outperforms state-of-the-art baselines in multi-step ahead demand forecasting.

demand forecasting spatio-temporal contextual information graph neural networks dynamic contexts

2020-09-25abs ↗pdf ↗

Dealing with Uncertainty in Contextual Anomaly Detectioncs.LG

Novel framework for contextual anomaly detection models uncertainty.

problem Identifying anomalies in target variables influenced by contextual variables.

method Normalcy score (NS) framework using heteroscedastic Gaussian process regression.

result NS outperforms state-of-the-art methods in detection accuracy and interpretability.

contextual anomaly detection uncertainty modeling heteroscedastic Gaussian process cardiology anomaly detection

2025-07-06abs ↗pdf ↗

Contextual Information-Directed Samplingcs.LG

IDS improves reinforcement learning with contextual information.

problem Optimizing IDS for contextual reinforcement learning.

method Investigated contextual bandit problems and proposed a computationally-efficient IDS.

result Contextual IDS outperforms conditional IDS by considering future contexts.

reinforcement learning IDS contextual bandits graph feedback sparse linear

2022-05-22abs ↗pdf ↗

Deep Contextual Multi-armed Banditscs.LG

Contextual multi-armed bandit problems arise frequently in important industrial applications. Existing solutions model the context either linearly, which enables uncertainty driven (principled) exploration, or non-linearly, by using epsilon-greedy exploration policies. Here we present a deep learning framework for cont…

2018-07-25abs ↗pdf ↗

Feel-Good Thompson Sampling for Contextual Dueling Banditscs.LG

New algorithm for contextual dueling bandits achieves nearly optimal regret.

problem Contextual dueling bandits with feedback on preferred options.

method Proposes FGTS.CDB, a Thompson sampling algorithm for linear contextual dueling bandits.

result Achieves nearly minimax-optimal regret of

ilde{\mathcal{O}}(d\sqrt T)

contextual dueling bandits Thompson sampling regret minimization UCB algorithms feel-good exploration

2024-04-09abs ↗pdf ↗

Exploration via Feature Perturbation in Contextual Banditscs.LG

A new exploration strategy for contextual bandits reduces regret and is computationally efficient.

problem Improving exploration in contextual bandits to reduce regret.

method Feature perturbation, injecting randomness directly into feature inputs.

result Achieves

ilde{\mathcal{O}}(d\sqrt{T})

worst-case regret bound, surpassing existing methods.

contextual bandits exploration feature perturbation regret bound non-parametric models

2025-10-20abs ↗pdf ↗

Langevin Monte Carlo for Contextual Banditscs.LG

LMC-TS uses MCMC for efficient posterior sampling in contextual bandits.

problem Efficiency of Thompson sampling for high-dimensional contextual bandits.

method Langevin Monte Carlo for direct posterior sampling.

result LMC-TS achieves sublinear regret bound for linear contextual bandits.

contextual bandits Thompson sampling Langevin Monte Carlo MCMC posterior sampling

2022-06-22abs ↗pdf ↗

Adapting to Misspecification in Contextual Banditscs.LG

New algorithms adapt to model misspecification in contextual bandits.

problem Design efficient algorithms for contextual bandits that handle model misspecification gracefully.

method Oracle-efficient algorithms for ε-misspecified contextual bandits using square loss regression.

result First algorithm achieving optimal regret bound for unknown misspecification level in linear contextual bandits.

contextual bandits misspecification regression oracle optimal regret square loss

2021-07-12abs ↗pdf ↗

A Direct Approach for Handling Contextual Bandits with Latent State Dynamicscs.LG

Direct approach for handling contextual bandits with latent state dynamics.

problem Handling contextual bandits with latent state dynamics, especially when rewards depend on posterior probabilities of hidden states.

method Direct reduction to standard linear contextual bandits, extended analysis of HMM parameters, periodic update of reward-model parameters.

result Periodic update of reward-model parameters allows handling complex dependencies in hidden states.

contextual bandits latent state dynamics hidden Markov model reward functions posterior probabilities

2026-04-09abs ↗pdf ↗

Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithmsstat.ML

A framework for auto-tuning hyper-parameters in contextual bandit algorithms.

problem Auto-tuning hyper-parameters in real-time for contextual bandit algorithms.

method Proposes a Syndicated Bandits framework to learn multiple hyper-parameters dynamically.

result Achieves optimal regret bounds under certain scenarios and handles multiple contextual bandit algorithms.

contextual bandit hyper-parameter tuning auto-tuning Syndicated Bandits regret bounds

2021-06-05abs ↗pdf ↗

Batched Nonparametric Contextual Banditsmath.ST

Study nonparametric contextual bandits with batched updates, achieving optimal regret.

problem Optimal regret in nonparametric contextual bandits with batch constraints.

method Dynamic binning of covariate space, optimal regret achieved.

result Achieves optimal regret (up to logarithmic factors) for nonparametric contextual bandits.

contextual bandits nonparametric batched updates optimal regret

2024-02-27abs ↗pdf ↗

Contextual Active Model Selectioncs.LG

CAMS selects best pre-trained model for unlabeled data points.

problem Efficiently utilizing pre-trained models and unlabeled data.

method Contextual active model selection algorithm with two components: contextual model selection and active query.

result CAMS requires less than 10% labeling effort compared to existing methods, achieving similar or better accuracy.

active learning model selection contextual pre-trained models unlabeled data

2022-07-13abs ↗pdf ↗

CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Designstat.ML

CO-BED optimizes experiments using Bayesian methods and information theory.

problem Optimizing experiments in a context-dependent manner.

method Formalizes contextual optimization with Bayesian experimental design, employing information-theoretic principles and black-box variational methods.

result CO-BED provides a general solution for contextual optimization problems.

Bayesian experimental design information theory contextual optimization black-box variational methods continuous relaxation

2023-02-27abs ↗pdf ↗

On the Optimal Regret of Locally Private Linear Contextual Banditstat.ML

This paper achieves optimal regret bounds for locally private linear contextual bandit.

problem Designing locally private linear contextual bandit algorithms with optimal regret bounds.

method New algorithmic and analytical ideas, including mean absolute deviation analysis and layered principal component regression.

result Achieves an

ilde O(\sqrt{T})

regret upper bound for locally private linear contextual bandit.

locally private linear contextual bandit regret bounds mean absolute deviation principal component regression

2024-04-15abs ↗pdf ↗

Rate-adaptive model selection over a collection of black-box contextual bandit algorithmscs.LG

Master algorithm selects best contextual bandit from a collection.

problem Model selection in stochastic contextual bandit setting.

method Random selection with probability adjustment based on comparison of cumulative rewards.

result Achieves the same regret rate as the best candidate in a collection of black-box algorithms.

contextual bandit model selection black-box algorithms rate-adaptive

2020-06-05abs ↗pdf ↗

Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturingcs.LG

Active learning method improves AI performance by balancing exploration and exploitation.

problem Efficiently acquiring samples for supervised learning in streaming data.

method Ensemble active learning by contextual bandits.

result Improved AI modeling performance through better sample acquisition.

active learning contextual bandits supervised learning AI modeling streaming data

2023-10-10abs ↗pdf ↗

Maximum entropy exploration in contextual bandits with neural networks and energy based modelscs.LG

New algorithms improve contextual bandits with neural networks and energy models.

problem Inefficient exploration in non-linear models for contextual bandits.

method Maximum entropy exploration using neural networks and energy models.

result Both techniques outperform standard algorithms, with energy models best overall.

contextual bandits maximum entropy neural networks energy models exploration-exploitation

2022-10-12abs ↗pdf ↗

Assessing Social and Intersectional Biases in Contextualized Word Representationscs.CL

Social bias in machine learning has drawn significant attention, with work ranging from demonstrations of bias in a multitude of applications, curating definitions of fairness for different contexts, to developing algorithms to mitigate bias. In natural language processing, gender bias has been shown to exist in contex…

2019-11-04abs ↗pdf ↗

Robust Bayesian Classification Using an Optimistic Score Ratiocs.LG

Bayesian classifier improves robustness with optimistic score ratio.

problem Limited information on class-conditional distribution.

method Optimistic score ratio for robust binary classification.

result Bayesian classifier using optimistic score ratio is robust and computationally tractable.

Bayesian classification optimistic score robustness contextual distribution limited information

2020-07-08abs ↗pdf ↗

Contextual Bandits with Sparse Data in Web settingcs.LG

Study identifies methods for handling sparse data in web settings with contextual bandits.

problem Handling sparse data in web settings with contextual bandits.

method Identified and categorized methods for addressing sparse data issues.

result Updated understanding of sparse data problems using contextual bandits in web settings.

sparse data contextual bandits web settings method evaluation policy evaluation

2021-05-06abs ↗pdf ↗

Locally-Contextual Nonlinear CRFs for Sequence Labelingcs.CL

Locally-contextual CRFs improve sequence labeling performance.

problem Improving sequence labeling with contextual embeddings.

method Locally-contextual nonlinear CRFs using deep neural networks.

result Consistently outperforms linear chain CRF and previous state of the art.

CRFs sequence labeling contextual embeddings deep learning

2021-03-30abs ↗pdf ↗

Provable Anytime Ensemble Sampling Algorithms in Nonlinear Contextual Banditscs.LG

Unified framework for ensemble sampling in nonlinear contextual bandits with provable regret bounds.

problem Efficient exploration in nonlinear contextual bandits with unknown feature dimensions.

method Developed GLM-ES and Neural-ES for generalized linear and neural contextual bandits, respectively, using maximum likelihood estimation on randomly perturbed data.

result Unified high-probability frequentist regret bounds for GLM-ES and Neural-ES, matching state-of-the-art results.

ensemble sampling nonlinear contextual bandits regret bounds maximum likelihood estimation randomized exploration

2025-10-12abs ↗pdf ↗

Towards Costless Model Selection in Contextual Bandits: A Bias-Variance Perspectivecs.LG

New algorithm offers costless model selection in contextual bandits.

problem Minimizing cumulative regret in stochastic contextual bandits.

method Gradually increasing class complexity and adapting to the simplest class with dominant estimation variance.

result Costless model selection is feasible under certain conditions, providing improved regret guarantees.

contextual bandits model selection cumulative regret bias-variance trade-off algorithm

2021-06-11abs ↗pdf ↗

Adversarial Rewards in Universal Learning for Contextual Banditsstat.ML

New findings on universal learning in contextual bandits with adversarial rewards.

problem Learning in contextual bandits with time-varying, adversarial rewards.

method Characterization of learnable processes and necessary/sufficient conditions for universal learning.

result Optimistic universal learning for contextual bandits with adversarial rewards is impossible in general.

contextual bandits adversarial rewards universal learning optimistic consistency time-varying rewards

2023-02-14abs ↗pdf ↗

Pricing with Contextual Elasticity and Heteroscedastic Valuationcs.LG

Study online pricing with contextual elasticity and heteroscedastic valuation.

problem Online contextual dynamic pricing with customer decision based on features and price.

method Introduced a novel approach to modeling customer demand with feature-based price elasticity and heteroscedastic noise. Proposed an efficient algorithm called Pricing with Perturbation (PwP).

result Proved an

O(\sqrt{dT\log T})

regret bound for the algorithm, matching a lower bound of

Ω(\sqrt{dT})

dynamic pricing contextual elasticity heteroscedastic valuation regret minimization algorithm

2023-12-26abs ↗pdf ↗

Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Modelstat.ML

Contextual multi-armed bandit (MAB) algorithms have been shown promising for maximizing cumulative rewards in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. However, most of the proposed contextual MAB algorithms assume linear relationships be…

2019-01-31abs ↗pdf ↗