Paper solves stochastic contextual linear bandits using linear bandit algorithms.
problem Stochastic contextual linear bandits with unknown context distribution.
method Establishes a reduction framework to convert to linear bandit problems.
result Achieves nearly optimal regret bound of O ( d T log T ) O(d\sqrt{T\log T}) O ( d T log T ) . First robust bandit algorithm for contextual bandits with sub-linear regret.
problem Vulnerability of linear contextual bandit algorithms to adversarial attacks.
method Proposes a robust bandit algorithm for stochastic linear contextual bandits under fully adaptive and omniscient attacks.
result Sub-linear regret under various attacks without requiring attack information.
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 i l d e O ( T ) ilde O(\sqrt{T}) i l d e O ( T ) regret upper bound for locally private linear contextual bandit. New algorithm improves learning efficiency in multi-task contextual bandits.
problem Improving learning efficiency in multi-task contextual bandits.
method Alternating projected gradient descent (GD) and minimization estimator for low-rank feature matrix recovery.
result Proved regret bound for multi-task learning algorithm.
New algorithm reduces regret from sqrt(T) to polylog(T) in stochastic contextual linear bandits.
problem Achieving logarithmic regret in stochastic contextual linear bandits.
method Low Regret Stochastic Contextual Bandits ( exttt{LR-SCB}) algorithm, exploiting stochastic contexts and parameter estimation.
result Logarithmic regret (polylog(T)) achieved, improving over sqrt(T) lower bound.
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.
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…
New algorithm for contextual bandits with linear constraints using regression.
problem Contextual bandits with packing and covering constraints.
method Modular Lagrangian approach via regression.
result First vanishing-regret guarantees for CBwLC (or CBwK).
A simple algorithm reduces federated contextual linear bandits' regret efficiently.
problem Solving federated contextual linear bandits with asynchronous agents.
method Proposed a simple algorithm exttt{FedLinUCB} based on optimism principle.
result Proved exttt{FedLinUCB} has bounded regret i l d e O ( d ∑ m = 1 M T m ) ilde{O}(d\sqrt{\sum_{m=1}^M T_m}) i l d e O ( d ∑ m = 1 M T m ) and communication complexity i l d e O ( d M 2 ) ilde{O}(dM^2) i l d e O ( d M 2 ) . 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.
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.
Paper tackles LDP bandits learning with improved results and sub-linear regret.
problem Contextual bandits learning with LDP privacy constraints.
method Simple black-box reduction frameworks for context-free bandits, extended to GLB.
result First result for BCO with multi-point feedback under LDP, sub-linear regret for GLB.
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.
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 develop algorithms for contextual …
New algorithm for partially observable contexts in finance.
problem Decision making based on partially observable, correlated market information.
method EMKF-Bandit algorithm integrating system identification, filtering, and bandit algorithms.
result Sub-linear regret under conditions on filtering.
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…
Modified Meta-TS for linear contextual bandits reduces regret.
problem Optimizing decision-making in dynamic environments with context vectors.
method Meta-TSLB algorithm for linear contextual bandits, analyzing Bayes regret.
result Derives an O ( ( m + log ( m ) ) n log ( n ) ) O((m+\log(m))\sqrt{n\log(n)}) O (( m + log ( m )) n log ( n ) ) bound on Bayes regret. Study on adaptivity constraints in linear contextual bandits with optimal design.
problem Impact of adaptivity constraints on linear contextual bandits.
method Two models of limited adaptivity: batch learning and rare policy switches. Proposed distributional optimal design.
result Achieves minimax-optimal regret with optimal number of policy switches and batches.
New action poisoning attacks improve LinUCB's performance by changing action signals.
problem Improving understanding of adversarial attacks on contextual bandit algorithms.
method Proposed action poisoning attacks in white-box and black-box settings.
result Action poisoning attacks can force LinUCB to pull a target arm frequently with low cost.
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 i l d e O ( d T ) ilde{\mathcal{O}}(d\sqrt T) i l d e O ( d T ) . New algorithm learns optimal exploration parameters for contextual bandits.
problem Learning optimal exploration in contextual bandits.
method Proposes two algorithms that learn optimal exploration parameters online based on context and reward.
result Demonstrates improved performance in learning optimal exploration compared to traditional methods.
The paper tackles minimax optimality in continuum contextual bandits with Hölder continuity.
problem Minimizing regret in a continuum of contexts with Hölder continuity.
method Proves a static-to-contextual regret conversion theorem and analyzes various dependency cases.
result Achieves minimax optimal contextual regret for convex and strongly convex bandits.
Improved online Lasso reduces regret in sparse linear contextual bandits.
problem Sparse linear contextual bandit problem with inefficient sampling.
method Perturbed adversary approach to alleviate sampling inefficiency.
result Online Lasso achieves O ( k T log d ) \mathcal{O}(\sqrt{kT\log d}) O ( k T log d ) regret bound. Paper studies user-level differential privacy in federated linear contextual bandits.
problem Federated learning with user-level differential privacy constraints.
method Unified federated bandits framework, CDP and LDP definitions, ROBIN algorithm.
result Near-optimal learning under user-level CDP with privacy budget and number of clients.
We introduce a new and completely online contextual bandit algorithm called Gated Linear Contextual Bandits (GLCB). This algorithm is based on Gated Linear Networks (GLNs), a recently introduced deep learning architecture with properties well-suited to the online setting. Leveraging data-dependent gating properties of …
A collaborative algorithm reduces regret in federated linear contextual bandits.
problem Optimizing decision-making in federated learning with heterogeneous data.
method Fed-PE algorithm, leveraging geometric structure of rewards, multi-client G-optimal design.
result Achieves near-optimal regrets with logarithmic communication costs.
This paper explores the complexity of learning representations in contextual linear bandits.
problem Understanding the complexity of representation learning in contextual linear bandits.
method Systematic approach to representation learning in contextual linear bandits, focusing on instance-dependent perspective.
result Representation learning is fundamentally more complex than linear bandits, with some cases being arbitrarily harder.
Thompson Sampling bounds for contextual bandits with sub-Gaussian rewards.
problem Improving the performance of Thompson Sampling in contextual bandits with sub-Gaussian rewards.
method Proved comprehensive bounds on Thompson Sampling expected cumulative regret based on mutual information and lifted information ratio for sub-Gaussian rewards.
result Explicit regret bounds for various contextual bandit scenarios.
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.
Study linear contextual bandits with confounded offline data, improving regret bounds.
problem Linear contextual bandits with confounded offline data.
method Construct a linear bandit algorithm that utilizes projected information.
result Proved regret bounds that improve current bounds by a factor related to visible dimensionality.
Linear contextual bandit is an important class of sequential decision making problems with a wide range of applications to recommender systems, online advertising, healthcare, and many other machine learning related tasks. While there is a lot of prior research, tight regret bounds of linear contextual bandit with infi…
Two algorithms for linear contextual bandits with rare updates achieve optimal regret and efficiency.
problem Linear contextual bandits with infrequent parameter updates.
method Two practical algorithms with O ( log log T ) O(\log\log T) O ( log log T ) updates, BLCE-G and BLCE. result Minimax-optimal regret with low computational complexity.
Algorithm reduces regret in misspecified linear contextual bandits.
problem Misspecified linear contextual bandits with bounded misspecification.
method Data selection scheme for online regression, leveraging uncertainty.
result Regret bound of O ~ ( d 2 / Δ ) \tilde O(d^2/Δ) O ~ ( d 2 /Δ ) when ζ ≤ O ~ ( Δ / d ) ζ \leq \tilde O(Δ/\sqrt{d}) ζ ≤ O ~ ( Δ/ d ) . Contextual bandits are widely used in Internet services from news recommendation to advertising, and to Web search. Generalized linear models (logistical regression in particular) have demonstrated stronger performance than linear models in many applications where rewards are binary. However, most theoretical analyses …
A new linear contextual bandit algorithm with improved regret bound.
problem Efficiently solving linear contextual bandit problems with reduced regret.
method Proposes a novel estimator embedded with exploration and a self-normalized bound.
result Regret bound matches lower bound of Ω ( d T ) Ω(\sqrt{dT}) Ω ( d T ) up to logarithmic factors. New approach combines semi-supervised learning and bandits for better predictions.
problem Online semi-supervised learning with bandit feedback for applications like clinical trials and ad recommendations.
method Adjusted Graph Convolutional Network (GCN) for contextual bandits, with semi-supervised missing rewards imputation.
result Developed multi-GCN embedded contextual bandit algorithms verified on real-world datasets.
New method optimizes offline linear bandits using different confidence sets.
problem Optimizing offline learning for linear contextual bandits.
method Introduces a family of pessimistic learning rules based on ℓ p \ell_p ℓ p confidence sets. result The π ^ ∞ \hatπ_\infty π ^ ∞ rule achieves minimax performance and strictly dominates other predictors. Improved Thompson Sampling for high-dimensional sparse bandits.
problem Stochastic linear contextual bandits with high-dimensional features.
method Thompson Sampling with spike-and-slab priors and variational inference.
result Nearly optimal upper bound on expected cumulative regret.
New algorithms for private generalized linear contextual bandits.
problem Private estimation and optimization for generalized linear models under differential privacy.
method Developed algorithms for stochastic and adversarial contexts under shuffle and joint differential privacy.
result Achieved private regret bounds for generalized linear models, differing from non-private rates by factors of d / ε \sqrt{d/\varepsilon} d / ε and d / ε \sqrt{d/\varepsilon} d / ε respectively. Study dynamic batch learning in high-dimensional sparse linear bandits.
problem Dynamic batch learning in high-dimensional sparse linear contextual bandits under batch constraints.
method Characterized fundamental learning limits via regret lower bound and provided matching upper bound.
result Prescribed an optimal scheme for dynamic batch learning in high-dimensional sparse linear contextual bandits.
Unified approach for non-stationary linear bandits with dynamic regret.
problem Non-stationary linear bandits with round-specific feasible actions and drifting reward models.
method Unified misspecification-reduction viewpoint, restarting algorithms with misspecification-dependent regret guarantees.
result Optimal \(T^{2/3}P_T^{1/3}\) dynamic-regret dependence for both linear bandits and contextual linear bandits.
Greedy algorithm achieves sublinear regret for various distributions.
problem Efficient performance of greedy algorithms in linear contextual bandit problems.
method Introduced Local Anti-Concentration (LAC) condition to ensure sublinear regret.
result Greedy algorithm achieves O ( poly log T ) O(\operatorname{poly} \log T) O ( poly log T ) cumulative expected regret. Proposes EE-Net for neural exploration in contextual bandits.
problem Exploitation-Exploration tradeoff in contextual bandits.
method Uses two neural networks: Exploitation and Exploration, to learn reward function and adaptively explore.
result Achieves O ( T log T ) \mathcal{O}(\sqrt{T\log T}) O ( T log T ) regret and outperforms existing methods. 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.
New algorithm tackles non-linear utility in MNL bandits with i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret.
problem Sequential assortment selection with intricate user-item interactions.
method Upper Confidence Bound principle for non-linear parametric utility functions, including neural networks.
result Achieves i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret bound for neural network-based utilities. 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.
COBRA addresses strategic behavior in online platforms by ensuring truthful reporting without monetary incentives.
problem Ensuring truthful reporting from strategic agents in online platforms.
method Proposes COBRA, an algorithm for contextual bandits involving strategic agents that disincentivizes strategic behavior.
result COBRA achieves sub-linear regret guarantee and incentive compatibility without monetary incentives.
New algorithm reduces regret with diverse contexts in bandits.
problem Impact of context diversity on stochastic linear contextual bandits.
method Design of LinUCB-d algorithm and analysis of its regret performance.
result Cumulative expected regret is bounded by a constant under diverse context assumption.