Polynomial-time method solves complex combinatorial semi-bandits.
problem Optimal strategies for combinatorial semi-bandits with uncorrelated Gaussian rewards.
method Proposes a polynomial-time method to solve the Graves-Lai optimization problem for various combinatorial structures.
result First known approach to implement asymptotically optimal algorithms in polynomial time for combinatorial semi-bandits.
This paper improves FTPL algorithm for semi-bandit problems with best-of-both-worlds guarantees.
problem Optimizing regret in adversarial and stochastic m m m -set semi-bandit problems. method Extending FTPL with geometric resampling (GR) to m m m -set semi-bandits and analyzing its performance. result FTPL with Fréchet and Pareto distributions achieves O ( m d T ) O(\sqrt{mdT}) O ( m d T ) regret in adversarial setting and logarithmic regret in stochastic setting. Paper analyzes FTPL's effectiveness in combinatorial semi-bandit problems.
problem Optimizing FTPL policy in combinatorial semi-bandit problems.
method Geometric resampling (GR) and conditional geometric resampling (CGR) for FTPL in semi-bandit setting.
result FTPL achieves optimal regret bounds in both Fréchet and Pareto distributions.
Optimal resource allocation in censored semi-bandits with unknown thresholds.
problem Sequential resource allocation with unknown thresholds and hidden parameters.
method Established equivalence to MP-MAB and Combinatorial Semi-Bandits, derived optimal algorithms.
result Validated performance of proposed algorithms on synthetic data.
Study on resource allocation with unknown thresholds in semi-bandit problems.
problem Learning optimal allocation of resources to arms with unknown threshold values.
method Established equivalence to MP-MAB and Combinatorial Semi-Bandits, derived optimal algorithms.
result Developed algorithms validated by experiments on synthetic data.
We develop the first general semi-bandit algorithm that simultaneously achieves O ( log T ) \mathcal{O}(\log T) O ( log T ) regret for stochastic environments and O ( T ) \mathcal{O}(\sqrt{T}) O ( T ) regret for adversarial environments without knowledge of the regime or the number of rounds T T T . The leading problem-dependent constants of our bounds are …
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.
FTPL with Fréchet perturbation achieves near optimal regret bounds for m-set semi-bandit problems.
problem Optimizing regret bounds for m-set semi-bandit problems in adversarial and stochastic settings.
method Follow-the-Perturbed-Leader (FTPL) with Fréchet perturbation.
result Achieves near optimal regret bounds of O ( n m ( d log ( d ) + m 5 / 6 ) ) \mathcal{O}(\sqrt{nm}(\sqrt{d\log(d)}+m^{5/6})) O ( nm ( d log ( d ) + m 5/6 )) in adversarial setting and logarithmic regret in stochastic setting. Optimistic covariance-adaptive algorithms improve combinatorial semi-bandits regret.
problem Optimal regret in stochastic combinatorial semi-bandits with adaptive covariance estimation.
method Design of OLS-UCB-C and COS-V algorithms leveraging online covariance estimation.
result Improved gap-free regret with T^1/2 complexity for COS-V.
New algorithm detects changes in combinatorial semi-bandit rewards.
problem Detecting changes in piecewise-stationary reward distributions in combinatorial semi-bandits.
method Combination of CUCB algorithm and GLRT change-point detector.
result Regret bound of O(√(NKTlogT)) for piecewise-stationary combinatorial semi-bandits.
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.
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.
Improved statistical efficiency of Thompson Sampling for combinatorial semi-bandits.
problem Efficiency of policies in stochastic combinatorial multi-armed bandits with semi-bandit feedback.
method Analysis of Combinatorial Thompson Sampling (CTS) using Beta and Gaussian priors for mutually independent and multivariate sub-Gaussian outcomes.
result CTS provides an efficient policy with optimal asymptotic regret for both mutually independent and multivariate sub-Gaussian outcomes.
Oracle-efficient algorithms reduce combinatorial semi-bandit regret to logarithmic time.
problem Scalability issue in combinatorial semi-bandit problems due to high combinatorial optimization costs.
method Oracle-efficient frameworks that minimize oracle queries while maintaining tight regret guarantees.
result Achieved i l d e O ( T ) ilde{O}(\sqrt{T}) i l d e O ( T ) regret with O ( log log T ) O(\log\log T) O ( log log T ) oracle queries for worst-case linear rewards. 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. New experimental design minimizes regret in bandits.
problem Minimizing regret in online stochastic linear and combinatorial bandits.
method Experimental design-based algorithm balancing information gain and reward.
result State-of-the-art finite time regret guarantees and computational efficiency.
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.
Coop-FTPL algorithm minimizes network regret in semi-bandit settings.
problem Online combinatorial optimization with semi-bandit feedback on a network of agents.
method Cooperative Follow The Perturbed Leader (Coop-FTPL) algorithm with new loss estimation procedure.
result Expected regret of Coop-FTPL is of order Q mkT log(k)(kα1 /Q + m), with a state-of-the-art computational complexity of T^3/2.
New algorithm tackles non-stationary combinatorial semi-bandit problems with optimal regret bounds.
problem Non-stationary combinatorial semi-bandit problems in switching and dynamic environments.
method Developed algorithms for both switching and dynamic cases, achieving nearly optimal regret bounds.
result Achieved nearly optimal regret bounds in both switching and dynamic cases.
Optimizes matching in weighted graphs with semi-bandit sampling.
problem Finding optimal pairings in weighted graphs with sequential sampling.
method Leverages rank-1 assumption on adjacency matrix to reduce sample complexity and regret.
result Achieves linear dependency in the number of vertices for sample complexity and regret.
Paper addresses privacy in combinatorial semi-bandits with improved bounds.
problem Privacy-preserving learning in combinatorial semi-bandits with additional dimension dependence.
method Proposes novel algorithms and proves optimal regret bounds for LDP and DP settings.
result Achieves nearly optimal regret bounds for LDP and DP settings, matching non-private rates.
We improve the efficiency of algorithms for stochastic \emph{combinatorial semi-bandits}. In most interesting problems, state-of-the-art algorithms take advantage of structural properties of rewards, such as \emph{independence}. However, while being optimal in terms of asymptotic regret, these algorithms are inefficien…
New algorithms optimize algorithm parameters in online settings with reduced computational costs.
problem Optimizing algorithm parameters in online settings with volatile and discontinuous losses.
method Developed semi-bandit optimization algorithms that leverage extra information to reduce computational costs.
result Achieved regret bounds as good as full-information feedback with significantly less computational effort.
A new algorithm tackles delayed combinatorial semi-bandit with causal relations.
problem Optimizing decisions in a non-stationary environment with delayed and causally related rewards.
method Formalized as a non-stationary delayed combinatorial semi-bandit problem, the approach models causal relations with a directed graph in a stationary structural equation model. The agent learns these relations from delayed feedback to optimize decisions.
result Proved a regret bound for the proposed algorithm's performance.
Study adapts combinatorial semi-bandit for piecewise stationary, causally related rewards.
problem Nonstationary environment with changing base arms' distributions and causal relationships.
method Upper Confidence Bound (UCB) algorithm with change-point detector and group restart strategy.
result Regret upper bound reflecting effects of structural and distribution changes.
Improved regret bounds for contextual combinatorial semi-bandits with linear payoffs.
problem Maximizing rewards in decision-making problems with feature vectors and constraints.
method Proposed C^2UCB algorithm and modified reward estimates for general constraints.
result Optimal regret bounds of C^2UCB algorithm and modified algorithm for various constraints.
Develops a combinatorial semi-bandit method for electric vehicle charging station selection.
problem Long-distance navigation for BEVs with unknown charging station availability and performance.
method Combinatorial semi-bandit framework, pre-processing road network, Bayesian modeling, Thompson Sampling, BayesUCB, Epsilon-greedy.
result Demonstrates improved navigation performance on long-distance BEV charging station selection.
A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we close the problem of computationally and sample effi…
CMOSS algorithm reduces regret in combinatorial semi-bandits with efficient computation.
problem Efficiently solving combinatorial semi-bandit problems with minimal regret.
method CMOSS algorithm achieves optimal regret bounds with minimal computational overhead.
result CMOSS achieves optimal regret bounds with minimal computational overhead.
Paper tackles safe combinatorial semi-bandits with risk constraints.
problem Safe combinatorial semi-bandits with risk constraints.
method Formulated probably anytime-safe constraint, designed PASCombUCB algorithm.
result PASCombUCB is almost asymptotically optimal in minimizing regret.
We address online linear optimization problems when the possible actions of the decision maker are represented by binary vectors. The regret of the decision maker is the difference between her realized loss and the best loss she would have achieved by picking, in hindsight, the best possible action. Our goal is to unde…
Improved sample complexity for contextual combinatorial semi-bandits with sparse rewards.
problem Optimizing decisions in contexts with many possible actions and sparse rewards.
method Developed an algorithm for ( ε , δ ) (ε,δ) ( ε , δ ) -PAC variant of contextual combinatorial semi-bandits with improved sample complexity. result Achieved an ε ε ε -optimal policy with a sample complexity of i l d e O ( ( p o l y ( K / m ) + s m / ε 2 ) log ( ∣ Π ∣ / δ ) ) ilde{O}((poly(K/m)+sm/ε^2) \log(|Π|/δ)) i l d e O (( p o l y ( K / m ) + s m / ε 2 ) log ( ∣Π∣/ δ )) . New algorithm reduces semi-bandit regret using covariance estimates.
problem Complexity of semi-bandits due to joint distribution of outcomes.
method Develops a new sub-exponential distribution family and an algorithm using covariance estimates.
result Proves a new lower bound on expected regret and constructs an algorithm with asymptotic analysis.
Thompson Sampling fails to perform well in high dimensions.
problem Thompson Sampling's suboptimality in high-dimensional combinatorial semi-bandits.
method Analysis of TS for combinatorial semi-bandits, including non-linear and linear reward functions, with Bernoulli rewards and uniform priors.
result TS's regret scales exponentially in the ambient dimension and minimax regret scales almost linearly in high dimensions.
Thompson Sampling shows polynomial regret for combinatorial semi-bandits with subgaussian rewards.
problem Finding optimal solutions in combinatorial semi-bandits with suboptimal sampling.
method Proposes Thompson Sampling with polynomial regret for linear combinatorial semi-bandits.
result Demonstrates 'mismatched sampling paradox' where knowing distributions can lead to worse performance.
New algorithms improve combinatorial linear semi-bandits for clustered feature vectors.
problem Poor performance of existing algorithms in clustered feature vector cases.
method Arm-wise randomization technique to address the shortcoming.
result Proposed algorithms (PC 2 {}^2 2 UCB and TS) outperform existing algorithms in clustered feature vector cases. Paper introduces a new framework for optimizing non-convex functions.
problem Optimizing non-convex functions, especially DR-submodular and concave functions.
method Developed a general meta-algorithm to convert linear/quadratic optimization to optimization of upper-linearizable/quadratizable functions.
result Unified approach to concave and DR-submodular optimization problems.
New algorithm reduces combinatorial semi-bandit regret efficiently.
problem Optimizing rewards from uncorrelated items in combinatorial semi-bandits.
method Developed an approximate version of ESCB with polynomial complexity.
result Achieved statistically efficient and polynomial time algorithm for combinatorial semi-bandits.
We consider the problem of online combinatorial optimization under semi-bandit feedback, where a learner has to repeatedly pick actions from a combinatorial decision set in order to minimize the total losses associated with its decisions. After making each decision, the learner observes the losses associated with its a…
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.
We propose a sample-efficient alternative for importance weighting for situations where one only has sample access to the probability distribution that generates the observations. Our new method, called Geometric Resampling (GR), is described and analyzed in the context of online combinatorial optimization under semi-b…
Algorithm improves movie recommendation efficiency with fairness constraints.
problem Improving movie recommendation efficiency with fairness constraints in combinatorial semi-bandits.
method Adopted Thompson Sampling with beta priors and Bernoulli likelihoods to handle fairness constraints.
result Time-averaged regret upper bounded by $\frac{N}{2η} + O\left(\frac{\sqrt{mNT\ln T}}{T}
ight)$ , with fairness constraints satisfied.
A stochastic combinatorial semi-bandit is an online learning problem where at each step a learning agent chooses a subset of ground items subject to combinatorial constraints, and then observes stochastic weights of these items and receives their sum as a payoff. In this paper, we consider efficient learning in large-s…
Online learning with one-sided feedback aims to maximize accuracy while ensuring fairness.
problem Maximizing accuracy in online learning with limited feedback and ensuring fairness.
method Extending the framework of Bechavod et al. (2020) to incorporate dynamic panels of auditors, reducing the problem to a contextual combinatorial semi-bandit, and leveraging Exp2 and Context-Semi-Bandit-FTPL algorithms.
result Multi-criteria no regret guarantees for accuracy and fairness are provided.
New algorithm reduces regret in both adversarial and stochastic contexts.
problem Contextual combinatorial semi-bandits with adversarial and corrupted stochastic regimes.
method Follow-the-Regularized-Leader (FTRL) framework with Shannon entropy regularizer, accelerated by Karush-Kuhn-Tucker conditions.
result Achieves O ~ ( T ) \widetilde{\mathcal{O}}(\sqrt{T}) O ( T ) regret in adversarial and O ~ ( ln T ) \widetilde{\mathcal{O}}(\ln T) O ( ln T ) regret in corrupted stochastic regimes. New algorithm optimizes beam and rate allocation in mmWave systems for multiple users.
problem Optimizing beam and rate allocation in mmWave systems for multiple users with limited feedback.
method Introducing SAT-CTS, a combinatorial semi-bandit policy with satisficing objective.
result SAT-CTS achieves finite-time regret bounds and reduces satisficing regret in mmWave systems.
New algorithms for efficient learning with partial information, reducing regret.
problem Online learning with partial observability and semi-bandit feedback.
method Implicit exploration strategy for near-optimal regret guarantees.
result First algorithms with near-optimal regret guarantees without knowing the observation system.
New framework for online influencer selection considering cost constraints.
problem Real-world social advertising budget limitations and cost variability of influencers.
method Introduces a budgeted framework for online influence maximization using an algorithm with semi-bandit feedback.
result Improves the state of the art regret bound for cardinality constraint setting.