We study offline data poisoning attacks in contextual bandits, a class of reinforcement learning problems with important applications in online recommendation and adaptive medical treatment, among others. We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards i…
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48 results for “contextual data generation”
We improve optimization for data with varying variance.
problem Optimizing data with varying variance.
method Generalized learning and optimization frameworks for data-driven optimization.
result Asymptotic and finite sample guarantees for stochastic programs.
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…
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. In this paper…
Novel approach to contextual bandits using self-supervised learning.
problem Exploiting rich data representations in computer vision without explicit labels.
method Combining contextual bandit objective with self-supervision objective.
result Substantial gains in cumulative reward on computer vision datasets.
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.
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…
A new algorithm for cryo-EM data collection that balances reward and latency.
problem Optimizing data collection in cryo-EM experiments with action delays.
method Latency-aware contextual bandit framework and COAF algorithm.
result The COAF algorithm efficiently maximizes reward over time in cryo-EM experiments.
The performance of machine learning model can be further improved if contextual cues are provided as input along with base features that are directly related to an inference task. In offline learning, one can inspect historical training data to identify contextual clusters either through feature clustering, or hand-cra…
CADR estimator improves inference for contextual bandit data.
problem Valid inference on contextual bandit data.
method CADR estimator for policy value, addressing adaptive data collection challenges.
result CADR provides correct coverage of confidence intervals.
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.
Study contextual online pricing with biased offline data, achieving optimal regret bounds.
problem Contextual online pricing with biased offline data.
method Identify to measure data bias, use OFU policy and robust variant for unknown bias.
result Achieve minimax-optimal regret bounds for contextual pricing.
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.
The article presents methods to select models from behavioral learning data, with applications to contextual bandits.
problem Model selection for behavioral learning data, especially in non-stationary environments.
method Two model selection methods: a general hold-out procedure and an AIC-type criterion, adapted for non-stationary dependent data.
result Theoretical error bounds for these methods are close to those of the standard i.i.d. case.
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.
Paper tackles transfer learning for contextual multi-armed bandits under covariate shift.
problem Nonparametric contextual multi-armed bandits with covariate shift.
method Established minimax rate of convergence, proposed transfer learning algorithm.
result Achieved near-optimal statistical guarantees for learning in target domain.
Contextual bandit algorithms provide principled online learning solutions to balance the exploitation-exploration trade-off in various applications such as recommender systems. However, the learning speed of the traditional contextual bandit algorithms is often slow due to the need for extensive exploration. This poses…
The paper tackles learning varying DAG structures based on contextual features.
problem Learning a single DAG for the entire population from observational data.
method A neural network that maps contextual features to a weighted adjacency matrix of a DAG, with a projection layer to ensure acyclicity.
result The new approach can recover context-specific DAGs where existing methods fail.
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.
Contextual bandits with linear payoffs, which are also known as linear bandits, provide a powerful alternative for solving practical problems of sequential decisions, e.g., online advertisements. In the era of big data, contextual data usually tend to be high-dimensional, which leads to new challenges for traditional l…
Cramming method evaluates learned policies from contextual bandits efficiently.
problem Evaluating final learned policies from contextual bandit algorithms.
method On-policy evaluation using a single pass of data, ensuring consistency and asymptotic normality.
result Cramming method reduces evaluation standard error by approximately 40% compared to off-policy methods.
Improved off-policy selection and learning in contextual bandits with better guarantees.
problem Selecting or training a reward-maximizing policy using data from a fixed behavior policy.
method A betting-based confidence bound applied to an inverse propensity weight sequence for off-policy selection, and a freezing condition for off-policy learning.
result The proposed methods achieve significantly improved guarantees over prior work, especially in small-data regimes.
We describe MELEE, a meta-learning algorithm for learning a good exploration policy in the interactive contextual bandit setting. Here, an algorithm must take actions based on contexts, and learn based only on a reward signal from the action taken, thereby generating an exploration/exploitation trade-off. MELEE address…
Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different "contexts". Bayesian optimization approaches to contextual policy search (CPS) offer data-efficient policy learning that generalize over a context space. We propose to impr…
We study the off-policy evaluation problem---estimating the value of a target policy using data collected by another policy---under the contextual bandit model. We consider the general (agnostic) setting without access to a consistent model of rewards and establish a minimax lower bound on the mean squared error (MSE).…
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.
Faster algorithm reduces contextual bandit regret with fewer offline regression calls.
problem Optimizing reward in contextual bandits with unknown functions.
method Designing a simple algorithm with offline regression calls.
result Achieves statistically optimal regret with minimal offline calls.
CACTI improves tabular data imputation by leveraging missingness patterns and contextual information.
problem Tabular data imputation with improved accuracy and robustness.
method Masked autoencoding approach with median truncated copy masking and contextual information.
result Average gain of 7.8% over the next best method across various datasets and missingness conditions.
New method for online statistical inference in contextual bandits using SGD.
problem Online decision-making in contextual bandits with statistical inference.
method Weighted stochastic gradient descent for adaptive data collection.
result Asymptotic normality of the parameter estimator with improved efficiency.
Blood glucose value prediction is an important task in diabetes management. While it is reported that glucose concentration is sensitive to social context such as mood, physical activity, stress, diet, alongside the influence of diabetes pathologies, we need more research on data and methodologies to incorporate and ev…
The paper improves DR estimators for off-policy evaluation in contextual bandits.
problem Challenges in evaluating policies with adaptively collected data.
method Adaptive weighting to control variance in DR estimators.
result Improved t-statistic based on the estimator is asymptotically normal.
COPP provides reliable intervals for outcomes under a new policy in contextual bandits.
problem Lack of reliable predictive intervals for outcomes under a new policy in contextual bandits.
method Conformal prediction applied to contextual bandits.
result COPP provides finite-sample guarantees without additional assumptions.
GLCB uses Gated Linear Networks for online contextual bandits.
problem Online learning in contextual bandits with uncertainty estimation.
method Gated Linear Networks (GLNs) for prediction and uncertainty estimation.
result GLCB outperforms state-of-the-art methods in online contextual bandits.
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.
A new algorithm avoids worst-case outcomes in risky contexts.
problem Risk-averse behavior in contextual bandits is challenging.
method Developed a first risk-averse contextual bandit algorithm with online regret guarantees.
result First algorithm with an online regret guarantee for risk-averse contextual bandits.
To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavi…
Unified framework for risk-aware policy learning in contextual bandits.
problem Optimizing decision rules in high-stakes domains with adverse outcomes.
method Distributional framework for Lipschitz-continuous risk functionals, with novel empirical concentration inequalities.
result Data-dependent suboptimality bounds with an rate, matching risk-neutral offline policy optimization.
Paper proposes efficient offline neural bandit method.
problem Offline policy learning with neural networks.
method Provable efficient offline contextual bandit with neural network function approximation.
result Method provably generalizes over unseen contexts.
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.
New approach for off-policy learning in contextual bandits with performance guarantees.
problem Improving performance of logging policies in contextual bandits.
method PAC-Bayesian analysis of policy mixtures, providing tighter generalization bounds and tractable optimization algorithms.
result Proved tighter generalization bounds and demonstrated effectiveness in practical scenarios.
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.
Bayesian method infers contextual bandit policies robustly.
problem Inference of contextual bandit policies in small sample sizes.
method Empirical likelihood for Bayesian inference.
result Accurate uncertainty measurements and policy comparison.
Paper improves Thompson Sampling for linear contextual bandits.
problem Empirical Thompson Sampling does not achieve optimal regret bounds.
method Develops a novel estimator with adaptive data augmentation and coupling.
result Achieves nearly minimax optimal performance.
New algorithm reduces contextual bandits to efficient regression.
problem Developing efficient algorithms for contextual bandits with general function classes.
method Reduction from contextual bandits to online regression with oracle.
result First universal and optimal reduction with no overhead.
Adapts to misspecification in contextual bandits using offline regression.
problem Unexpected regret due to misspecified reward models.
method Adapts to misspecification by reverting to a safe policy when necessary.
result Regret guarantees degrade gracefully with misspecification level.
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.
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.
INFERS PDEs from data samples using learned context.
problem Inferring explicit PDEs from unseen dynamics.
method Contextual Finite Differences (CFD) method integrating PDE form and differential scheme.
result Yields a PDE fitting the data sample for signal prediction and explanation.