New algorithm for recommending best arms with aggregated feedback.
problem Finding the best arm under aggregated feedback when precise rewards are unavailable.
method Gaussian Process Optimistic Optimisation (GPOO) algorithm with adaptive tree construction.
result The proposed algorithm achieves new simple regret bounds with aggregated feedback.
We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback: partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feed…
Study online learning in MDPs with aggregate bandit feedback, achieving low regret in both stochastic and adversarial settings.
problem Online learning in finite-horizon episodic MDPs with aggregate bandit feedback.
method Best-of-both-worlds (BOBW) algorithms using FTRL over occupancy measures, self-bounding techniques, and new loss estimators.
result First BOBW algorithms for episodic tabular MDPs with aggregate bandit feedback achieving O ( log T ) O(\log T) O ( log T ) regret in stochastic and O ( T ) {O}(\sqrt{T}) O ( T ) regret in adversarial settings. Algorithm for online decision making with unknown dynamics and aggregate feedback.
problem Online decision making with unknown dynamics and aggregate bandit feedback.
method Developed an algorithm based on online mirror descent with a self-concordant barrier regularization and an increasing learning rate schedule.
result Achieved O ( K ) O(\sqrt{K}) O ( K ) regret for the online Markov Decision Process with K K K episodes. We study a variant of the stochastic K K K -armed bandit problem, which we call "bandits with delayed, aggregated anonymous feedback". In this problem, when the player pulls an arm, a reward is generated, however it is not immediately observed. Instead, at the end of each round the player observes only the sum of a number…
Algorithm aggregates rewards from multiple players to learn related tasks in online bandit learning.
problem Learning related but slightly different tasks in an online setting with heterogeneous feedback.
method RobustAgg ( ε ) (ε) ( ε ) algorithm that aggregates rewards from different players. result Achieves instance-dependent regret guarantees and nearly matching lower bounds.
Study non-oblivious adversarial bandits with delayed feedback and propose algorithms with improved regret bounds.
problem Adversarial bandit problem with delayed, composite anonymous feedback.
method Propose wrapper algorithm for non-oblivious delay setting, achieving o ( T ) o(T) o ( T ) policy regret. result Achieve o ( T ) o(T) o ( T ) policy regret for many adversarial bandit problems with bounded memory loss sequences. We explore a novel setting of the Multi-Armed Bandit (MAB) problem inspired from real world applications which we call bandits with "stochastic delayed composite anonymous feedback (SDCAF)". In SDCAF, the rewards on pulling arms are stochastic with respect to time but spread over a fixed number of time steps in the fut…
Sharp sample complexity for multiclass PAC learning with bandit feedback.
problem Multiclass PAC learning with bandit feedback.
method Characterization of optimal sample complexity using a new combinatorial dimension (bandit DS dimension) and a learning algorithm (ListCascade).
result Sharp sample complexity characterization for every concept class up to logarithmic factors.
New algorithm reduces dynamic regret without prior function change knowledge.
problem Non-stationary stochastic optimization with bandit feedback.
method Fixed step sizes combined with multi-scale sampling framework.
result Achieves optimal dynamic regret without prior function change knowledge.
Improves reward bounds for prediction with expert advice using abstention.
problem Prediction with expert advice under bandit feedback with abstention.
method CBA algorithm exploiting abstention to improve reward bounds.
result Achieved significant improvement in reward bounds for general confidence-rated predictors.
Proposes DEXP3.M for unknown delay in multi-arm bandit with multiple play.
problem Unknown delays in adversarial multi-armed bandit with multiple play.
method DEXP3.M algorithm addressing the challenge of associating feedback losses to arms.
result Regret bound is only slightly worse than single play setting.
Study optimal arms in combinatorial bandits with semi-bandit feedback and finite budget.
problem Finding optimal arms in combinatorial bandits with semi-bandit feedback and finite budget constraints.
method Proposes a generic algorithm covering various arm elimination strategies and derives lower bounds.
result Demonstrates sufficient and necessary budget requirements for finding the best arm.
Reduces user feedback needed for accurate recommender systems.
problem Limited user feedback in recommender systems.
method Partial Bandit and Semi-Bandit approach for efficient user feedback retrieval.
result Similar global accuracy and learning efficiency with reduced feedback.
Paper tackles noisy bandit feedback for multiclass classification.
problem Learning multiclass classifier with corrupted feedback.
method Proposes an unbiased estimator technique to estimate noise rates and an end-to-end framework.
result Algorithm achieves mistake bounds of O ( T ) O(\sqrt{T}) O ( T ) in high noise and O ( T i c e f r a c 23 ) O(T^{
icefrac{2}{3}}) O ( T i ce f r a c 2 3 ) in worst case. Unified framework for expert selection with bandit and lower-bound feedback.
problem Selecting the best expert in scenarios with bandit feedback and lower-bound information.
method Introduces a new feedback model combining bandit and lower-bound information, proving optimal regret bounds for modified Exp3 algorithms.
result Optimal regret bounds for modified Exp3 algorithms, generalizing both bandit and full-information settings.
Study online multiclass classification under bandit feedback, extending previous results.
problem Online multiclass classification with bandit feedback, focusing on label space unboundedness.
method Extend Daniely and Helbertal's results, show necessity and sufficiency of Bandit Littlestone dimension for learnability.
result Sequential uniform convergence is necessary but not sufficient for bandit online learnability.
New method uses correlated auxiliary feedback to reduce regret in parameterized bandits.
problem Reducing regret in parameterized bandits with correlated auxiliary feedback.
method Develops a reward estimator using auxiliary feedback with tight confidence bounds.
result Shows significant reduction in regret compared to standard methods.
Algorithm provides online learning guarantees against general comparators in full and bandit feedback.
problem Adversarial online learning with data-dependent regret guarantees.
method Completely online algorithm with data-dependent regret guarantees for full and bandit feedback.
result Algorithm achieves expected performance against arbitrary comparator sequences in full and bandit feedback settings.
Banker-OMD improves online learning with delayed feedback.
problem Handling delayed feedback in online learning.
method Generalized Online Mirror Descent (OMD) framework.
result Achieves nearly-optimal performance in three bandit scenarios.
Paper tackles stochastic k k k -submodular bandits with full feedback, achieving sublinear regret.
problem Online optimization of k k k -submodular functions with full-bandit feedback. method Proposes online algorithms for various k k k -submodular stochastic combinatorial multi-armed bandit problems. result Achieves sublinear α α α -regret bounds for multiple k k k -submodular stochastic combinatorial multi-armed bandit problems. New framework tackles submodular welfare with multi-agent combinatorial bandits.
problem Maximizing total welfare among agents with shared constraints and submodular utilities under bandit feedback.
method Proposes an explore-then-commit strategy with randomized assignments for multi-agent combinatorial bandits.
result Achieves i l d e O ( T 2 / 3 ) ilde{\mathcal{O}}(T^{2/3}) i l d e O ( T 2/3 ) regret, first for partition-based submodular welfare problem under bandit feedback. One-bit feedback suffices for a bandit problem's optimal strategy.
problem Optimal strategy for multi-armed bandit problem with limited feedback.
method Coding and decoding schemes for one-bit feedback to mimic full-reward feedback.
result Regret ratio approaches 1 with one-bit feedback.
New algorithms handle online prediction with bandit and delayed feedback, improving regret bounds.
problem Achieving finite bounds on surrogate regret with limited feedback.
method Proposed algorithms for bandit and delayed feedback, including inverse-weighted gradient and pseudo-inverse matrix estimators.
result Achieved improved surrogate regret bounds of O ( K T ) O(\sqrt{KT}) O ( K T ) and O ( T 2 / 3 ) O(T^{2/3}) O ( T 2/3 ) . New RL algorithm optimizes policies with bandit feedback, matching previous bounds.
problem Optimizing policies with unknown transitions and bandit feedback.
method Optimistic Trust Region Policy Optimization (TRPO) algorithm.
result Sub-linear regret bounds for both stochastic and adversarial rewards.
Efficient boosting method for regression with limited feedback.
problem Online boosting for regression tasks with noisy multi-point bandit feedback.
method Efficient regret minimization method with online boosting algorithm and projection-free online convex optimization.
result Improved state-of-the-art guarantees in efficiency.
New algorithm reduces regret in delayed feedback generalised linear bandits.
problem Regret in delayed feedback generalised linear bandits.
method Adaptation of optimistic algorithm to delayed feedback.
result Achieves a regret bound independent of the horizon's delay penalty.
Neural algorithms optimize arm selection with human preference feedback for complex reward functions.
problem Optimizing arm selection with noisy human preference feedback for complex, non-linear reward functions.
method Neural network to estimate reward function using preference feedback, upper confidence bound and Thompson sampling algorithms.
result Sub-linear regret guarantees for efficient arm selection in contextual dueling bandits.
New algorithms ensure fair selection in combinatorial semi-bandit with unrestricted delays.
problem Fair selection in stochastic combinatorial semi-bandit with delayed feedback.
method Introduced merit-based fairness constraints and new bandit algorithms for reward and fairness.
result Achieved sublinear expected reward and fairness regrets with dependence on delay distribution quantiles.
New algorithms tackle adversarial combinatorial bandits with switching costs.
problem Adversarial combinatorial bandits with switching costs.
method Design algorithms operating in batches to restrict switches, proving lower bounds and achieving upper bounds on regret.
result Achieved upper bounds on regret for both bandit and semi-bandit feedback settings.
We present a study on reinforcement learning (RL) from human bandit feedback for sequence-to-sequence learning, exemplified by the task of bandit neural machine translation (NMT). We investigate the reliability of human bandit feedback, and analyze the influence of reliability on the learnability of a reward estimator,…
We present online boosting algorithms for multiclass classification with bandit feedback, where the learner only receives feedback about the correctness of its prediction. We propose an unbiased estimate of the loss using a randomized prediction, allowing the model to update its weak learners with limited information. …
Multi-armed bandit(MAB) problem is a reinforcement learning framework where an agent tries to maximise her profit by proper selection of actions through absolute feedback for each action. The dueling bandits problem is a variation of MAB problem in which an agent chooses a pair of actions and receives relative feedback…
Algorithm allocates budgets to tasks with semi-bandit feedback, achieving near-optimal regret bounds.
problem Stochastic budget allocation with censored semi-bandit feedback.
method Optimism-based algorithm operating under censored semi-bandit feedback.
result Regret scales polylogarithmically with horizon T in diminishing-returns regimes.
A new algorithm for conversational recommendation systems using dueling bandits in GLMs.
problem Limited user feedback in existing conversational bandit methods.
method Integrates dueling bandits with relative feedback in generalized linear models.
result Theoretical and empirical validation of ConDuel's efficacy.
New algorithm controls linear systems with bandit feedback, achieving optimal regret.
problem Controlling linear systems with bandit feedback under adversarial costs.
method Developed a new algorithm for linear control with memory optimization technique.
result Achieved optimal regret growth proportional to square root of time horizon.
This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus of this paper is the development of new theory and algorithms fo…
Unified framework for analyzing online convex optimization across various settings.
problem Analyzing online convex optimization in different settings and feedback types.
method Unified framework allowing systematic proposal and analysis of meta-algorithms.
result Comparable regret bounds for various feedback types and adversary types.
The paper tackles personalized policy learning from diverse data sources in a federated setting.
problem Learning personalized decision policies from observational bandit feedback across multiple heterogeneous data sources.
method Introduces a novel regret analysis for distinguishing global and local regret, and presents a federated policy learning algorithm using local policies trained with doubly robust offline policy evaluation strategies.
result Establishes finite-sample upper bounds on global and local regret, characterizing them by source heterogeneity and distribution shift.
Algorithm identifies Copeland winners in dueling bandits with ternary feedback.
problem Identifying Copeland winners in dueling bandits with indifferences.
method Proposed POCOWISTA algorithm with a sample complexity close to lower bound.
result Algorithm shows excellent performance, even for conventional dueling bandits.
New algorithm tackles delayed feedback in Lipschitz bandits with sublinear regret.
problem Delayed feedback in Lipschitz bandits.
method Design of algorithms for bounded and unbounded stochastic delays.
result Sublinear regret guarantees for both bounded and unbounded delays.
FedConPE improves conversational recommender systems efficiency and privacy.
problem Efficiently eliciting user preferences in interactive systems with heterogeneous clients.
method Phase elimination-based federated conversational bandit algorithm with adaptive key term construction.
result Minimizes uncertainty across all dimensions in feature space and offers improved efficiency and privacy.
This paper extends combinatorial semi-bandits to graph feedback, improving regret bounds.
problem Adversarial combinatorial semi-bandits with graph feedback.
method Introduced graph feedback in combinatorial semi-bandits, using convexified actions and online stochastic mirror descent.
result Optimal regret scales as S T + α S T S\sqrt{T}+\sqrt{αST} S T + α S T , interpolating between full and semi-bandit feedback. Paper tackles dueling bandits with delayed feedback, revealing preference bias.
problem Real-world dueling bandit applications often face delays in feedback.
method Introduces biased dueling bandit problem with stochastic delayed feedback, presents two algorithms.
result Two algorithms achieve optimal regret bounds for dueling bandit problems with delay.
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…
New algorithms improve on bandit feedback in matrix games with unknown payoff matrices.
problem Improving performance in matrix games with unknown payoff matrices and bandit feedback.
method Regret analyses of variants of UCB and K-learning.
result New algorithms achieve lower regret compared to adversarial bandit algorithms.
Algorithm identifies best arm in combinatorial bandits with semi-bandit feedback.
problem Identifying the best arm in combinatorial bandits with semi-bandit feedback.
method Interpreted as a sequential zero-sum game, developed a CombGame meta-algorithm with finite time guarantees.
result First computationally efficient algorithm that is asymptotically optimal and has competitive empirical performance.
New algorithm minimizes expert selection regret in partial bandit feedback.
problem Minimizing expert selection regret in partial bandit feedback.
method Develops a sequential minimax optimal algorithm for a generalized partial monitoring setting.
result Second order regret bounds against a general expert selection sequence.