A new method improves coordinate descent by adaptively selecting coordinates.
problem Coordinate descent's inefficiency due to checking all coordinates.
method Adaptive multi-armed bandit algorithm to select coordinates.
result Improves convergence of coordinate descent methods.
Paper speeds up IoT device detection and data decoding.
problem Efficiently detect and decode massive IoT devices in grant-free random access.
method Develops multi-armed bandit approaches for more efficient detection via coordinate descent.
result Proposed bandit based algorithms achieve faster convergence rates with lower time complexity.
This paper tackles resource allocation in multi-user communication networks using a coordinated multi-armed bandit approach.
problem Learning unknown stochastic network characteristics and sharing resources efficiently.
method Combines Multi-Armed Bandit learning with a lightweight signalling-based coordination scheme.
result Ensures convergence to a stable allocation of resources with maximal resource utilization.
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 learns coordinated decisions in loosely-coupled multi-agent systems.
problem Learning coordinated decisions in multi-agent systems with sparse interactions.
method Multi-Agent Thompson Sampling (MATS) for multi-agent multi-armed bandits.
result MATS achieves sublinear regret and outperforms MAUCE on synthetic and real benchmarks.
Two non-communicating players minimize regret in a multi-armed bandit game.
problem Optimal regret in non-communicating multi-armed bandit players.
method Proposed a strategy with no collisions, achieving near-optimal regret.
result Near-optimal regret of O ( T log ( T ) ) O(\sqrt{T \log(T)}) O ( T log ( T ) ) with very high probability. New algorithm for nonstationary multi-armed bandits with optimal performance.
problem Nonstationary multi-armed bandits with changing model parameters over time.
method Adaptive Resetting Bandit (ADR-bandit) algorithm using adaptive windowing techniques.
result ADR-bandit achieves nearly optimal performance in both abrupt and gradual changes.
Algorithm reduces regret in multi-player bandits with unknown collision rewards.
problem Reducing regret in multi-player multi-armed bandits with unknown collision rewards.
method Proposes an algorithm that combines a modified successive elimination strategy with a communication protocol to estimate suboptimality gaps and coordinate among players.
result Achieves logarithmic regret for the problem when collision reward is unknown.
Near-optimal regret in distributed bandit learning with efficient communication protocols.
problem Minimizing total regret in collaborative bandit learning with limited communication.
method Proposed communication protocols for distributed multi-armed and linear bandits with near-optimal regret and efficient communication costs.
result Achieved near-optimal regret with communication costs independent of time horizon and number of arms.
Study multiplayer bandits without collision info, achieving regret bounds.
problem Multiplayer bandits without collision info.
method Two feedback models considered; algorithms for both models.
result First theoretical guarantees for second model with square-root regret.
PopArt efficiently solves sparse linear bandits with tighter recovery guarantees.
problem Sparse linear bandits where rewards depend on a few covariates.
method PopArt: a simple, computationally efficient sparse linear estimation method.
result Improved regret bounds compared to state-of-the-art algorithms.
New algorithm balances exploration cost between groups in multi-armed bandits.
problem Balancing exploration cost between groups in multi-armed bandits.
method Introducing Col-UCB algorithm that dynamically coordinates exploration across groups.
result Achieves optimal minimax and instance-dependent collaborative regret up to logarithmic factors.
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.
TOFU-POV tackles partially observed linear bandits, achieving sublinear regret with low-dimensional action vectors.
problem Stochastic linear bandits with partially observed actions in settings like recommendation and healthcare.
method TOFU-POV estimates latent action subspace, imputes missing actions, and runs OFUL in low-dimensional coordinates.
result TOFU-POV achieves T \sqrt{T} T regret scaling with intrinsic subspace dimension, improving upon natural baselines. Optimal algorithm for contextual bandits with unknown context distributions.
problem Designing efficient algorithms for contextual bandits with unknown context distributions.
method Cross-learning setting, novel technique for coordinating multiple epochs.
result Nearly tight regret bound of O ~ ( T K ) \widetilde{O}(\sqrt{TK}) O ( T K ) for learning to bid in first-price auctions and sleeping bandits. The paper tackles robust policy learning in multitask contextual bandits with adversarial users.
problem Learning optimal policies in multitask contextual bandits with a small fraction of adversarial users.
method Developed efficient robust mean estimators for both uni-variate and high-dimensional random variables.
result Lower bound of i l d e Ω ( min ( S , A ) ⋅ α 2 / ε 2 ) ildeΩ(\min(S,A) \cdot α^2 / ε^2) i l d e Ω ( min ( S , A ) ⋅ α 2 / ε 2 ) per-user interactions to learn an ε ε ε -optimal policy for good users. A paper on optimizing ad bidding with multi-agent reinforcement learning.
problem Optimizing ad revenue and ROI in real-time display advertising.
method Multi-agent reinforcement learning with clustering and coordinated bidding.
result Cluster-based bidding outperforms single-agent and bandit approaches.
New method reduces variance in stochastic optimization.
problem High variance in uniform sampling degrades optimization performance.
method Online optimization with bandit feedback for non-uniform sampling.
result First algorithm finds competitive importance sampling distributions.
We consider the problem of learning in single-player and multiplayer multiarmed bandit models. Bandit problems are classes of online learning problems that capture exploration versus exploitation tradeoffs. In a multiarmed bandit model, players can pick among many arms, and each play of an arm generates an i.i.d. rewar…
Develops a new framework for analyzing sequential decision-making problems using information theory.
problem Lack of information-theoretic generalization bounds for sequential decision-making problems.
method Introduces a sequential supersample framework that separates learner filtration from proof-side enlargement, controlling the generalization gap by sequential CMI.
result Establishes a sequential CMI that controls the generalization gap in sequential decision-making problems.
RD-Agent(Q) automates quantitative finance research and development.
problem Challenges in asset return prediction due to high dimensionality and volatility.
method Data-centric multi-agent framework for automated research and development of quantitative strategies.
result Up to 2X higher annualized returns with 70% fewer factors.
New algorithm for multi-player bandits in decentralized, asynchronous systems.
problem Challenges in decentralized, asynchronous multi-player bandits, including coordination and player detection.
method Adaptive exploration-exploitation algorithm that reduces collisions and detects player presence.
result Achieves regret of O ( T log T + log T / Δ 2 ) \mathcal{O}(\sqrt{T \log T} + {\log T}/{Δ^2}) O ( T log T + log T / Δ 2 ) . Improved regret bounds for Thompson Sampling in combinatorial settings.
problem Online combinatorial optimization with prior knowledge of adversary's losses.
method Introducing new information ratio, coordinate entropy, and thresholded Thompson sampling.
result First-order regret bounds of i l d e O ( d L ∗ ) ilde{O}(\sqrt{d L^*}) i l d e O ( d L ∗ ) in semi-bandit scenario. A model for human-machine decision-making with private info and opacity.
problem Optimizing decisions in a human-machine system with private info and opacity.
method Formulated as a two-player learning problem, proved lower and upper bounds on optimality.
result Simple coordination strategy is nearly minimax optimal, efficient learning possible under certain assumptions.
In this paper, we address the general case of a coordinated secondary network willing to exploit communication opportunities left vacant by a licensed primary network. Since secondary users (SU) usually have no prior knowledge on the environment, they need to learn the availability of each channel through sensing techn…
Optimal channel assignment in ad hoc networks with heterogeneous users.
problem Optimal allocation of channels in a decentralized network with unknown and varying channel characteristics.
method Developed algorithms for explore-and-commit and logarithmic regret, leveraging specific user transmissions and sensing.
result Achieved near-optimal allocation with high probability and logarithmic regret, significantly outperforming state-of-the-art methods.
New algorithms tackle RKHS bandits with reduced complexity and improved performance.
problem Adversarial and stochastic RKHS bandit problems with high computational complexity.
method Combining approximation theory with misspecified linear bandit methods.
result First general algorithm for adversarial RKHS bandit problem.
Unified formulation bridges adversarial and nonstationary bandits.
problem Handling time-varying reward distributions in multi-armed bandit problems.
method Unified oracle that switches between adversarial and nonstationary bandit oracles based on window size.
result Optimal regret achieved with matching lower bound.
Paper studies attacks on bandit algorithms and shows how attackers can manipulate data to hijack behavior.
problem Potential attacks on bandit algorithms can cause catastrophic loss in real-world applications.
method Proposes a framework of offline and online attacks on bandit algorithms using convex optimization and adaptive strategies.
result Attackers can force bandit algorithms to pull target arms with high probability by manipulating data.
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 ) . 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.
Algorithm identifies best arm in linked bandits with reduced feedback.
problem Best arm identification in linked bandits with reduced feedback.
method Combines uniform sampling with regular bandit algorithm.
result Almost matching upper and lower bounds on sample complexity.
Graph-Triggered Bandits unify rested and restless bandits with graph-defined arm interactions.
problem Modeling sequential decision-making problems with evolving arm rewards.
method Graph-Triggered Bandits (GTBs) framework that generalizes rested and restless bandits using a graph.
result Rested and restless bandits are special cases of GTBs for suitable graphs.
New definition resolves ambiguity in non-stationary bandit classification.
problem Ambiguity in classifying non-stationary bandits using existing definitions.
method Introducing a formal definition that resolves ambiguity and provides a unified approach.
result Unified approach applicable to both Bayesian and frequentist formulations, resolves classification issues.
Unified approach for non-stationary and clustered bandits.
problem Solving non-stationary and clustered bandits with overlapping solutions.
method Test of homogeneity for seamless integration of non-stationary and clustered bandits.
result Unified solution framework for change detection and cluster identification.
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.
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.
A new method for dueling bandits improves performance.
problem Improving decision-making in dueling bandits.
method Sup-KLUCB method for K-armed dueling bandits, converting Copeland dueling bandits into standard MAB problems.
result Sup-KLUCB outperforms state-of-the-art methods in Copeland dueling bandits.
Study on indexability of restless multi-armed bandits and rollout policy performance.
problem Maximizing discounted rewards in finite state restless multi-armed bandit problems.
method Decouple the problem into single-armed restless bandits, analyze using value iteration, and compare with Whittle index policy.
result Demonstrates conditions for indexability and compares performance of index policy and rollout policy.
Investigates sequential problems on graph structures and large action spaces.
problem Sequential decision-making on graph structures and large action spaces.
method Spectral bandits, side observations, influence maximization, kernel bandits, polymatroid bandits, function optimization, infinitely many-arms bandits.
result Contributions to graph and structured bandits.
New insights into multi-armed bandits with budget constraints.
problem Multi-armed bandits with supply/budget constraints.
method Characterization of logarithmic regret rates, simple regret, and reduction to other bandit problems.
result Full characterization of logarithmic, instance-dependent regret rates for BwK.
Optimal algorithm for identifying best arm in stochastic linear bandits with fixed confidence.
problem Identifying the best arm in stochastic linear bandits with fixed confidence.
method Extending an algorithm designed for Best Arm Identification to the ε ε ε -Thresholding Bandit Problem (TBP). result Asymptotically optimal algorithm for TBP.
A meta-UCB method combines stochastic bandit algorithms.
problem Combining multiple stochastic bandit algorithms efficiently.
method Meta-UCB procedure solving an N-armed bandit problem.
result Final regret depends only on the best base algorithm's regret.
New method for contextual bandits with corrupted context.
problem Contextual bandits with corrupted context in online settings.
method Combining contextual bandit and multi-armed bandit approaches.
result Improved learning from all iterations, including corrupted ones.
A new algorithm for top-k combinatorial bandits with full-bandit feedback.
problem Optimizing decisions among subsets of arms with only aggregate rewards observed.
method CSAR algorithm using Hadamard matrices for estimating individual arm rewards.
result Proved a tight lower bound on sample complexity for k = O ( 1 ) k=O(1) k = O ( 1 ) . A new framework for structured bandits using influence diagrams and variational Thompson sampling.
problem Complex statistical dependencies in structured bandit problems.
method Influence diagram framework, variational Thompson sampling, tracking structured posterior distribution.
result Empirically evaluated algorithms perform as well as or better than existing baselines.
Smooth Contextual Bandits bridge two previously studied extremes of non-differentiable and parametric-response bandits.
problem Nonparametric contextual bandits with Hölder smoothness.
method Developed a novel algorithm that optimally balances between non-differentiable and parametric-response bandits.
result Proved the algorithm achieves rate-optimal regret for all smoothness settings.
Factored bandits model learns with limited feedback using decomposable actions.
problem Limited feedback learning with decomposable actions.
method Introduces factored bandits model, provides anytime algorithm, and matching upper and lower bounds.
result Improves regret bounds for utility-based dueling bandits.