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.
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. IDS improves sparse linear bandits by balancing information and regret.
problem Sparse linear bandits in high-dimensional decision-making.
method Information-directed sampling (IDS) with Bayesian regret bounds and empirical Bayesian sparse posterior sampling.
result IDS nearly matches existing lower bounds and significantly reduces regret.
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.
New bandit algorithms improve sparse reward learning.
problem Sparse rewards hinder learning efficiency in real-world bandit applications.
method Developed algorithms based on Upper Confidence Bound and Thompson Sampling for zero-inflated distributions.
result Empirical performance of new algorithms is superior to existing methods.
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.
A privacy-preserving algorithm for high-dimensional bandits.
problem High-dimensional stochastic contextual linear bandits with sparse parameters under privacy constraints.
method PrivateLASSO algorithm based on sparse hard-thresholding and episodic thresholding.
result Minimax private lower bounds and utility guarantees for PrivateLASSO.
New bounds for high-dimensional sparse linear bandits, balancing information and regret.
problem Stochastic linear bandits with high-dimensional sparse features.
method Derivation of minimax regret lower and upper bounds for explore-then-commit algorithm.
result Optimal rate of Θ ( n 2 / 3 ) Θ(n^{2/3}) Θ ( n 2/3 ) for data-poor regime, complemented by O ( n ) O(\sqrt{n}) O ( n ) under signal magnitude assumption. A two-phase algorithm identifies the best arm in sparse linear bandits with fixed budget.
problem Best arm identification in sparse linear bandits with limited budget.
method Lasso and Optimal-Design (Lasso-OD) based linear best-arm identification.
result Lasso-OD achieves significant performance improvement for sparse and high-dimensional linear bandits.
Paper improves sparse linear bandits by accounting for noise variance.
problem Sparse linear bandits with unknown noise variance.
method Develops a general framework to convert variance-aware algorithms to sparse linear bandits.
result Achieves $\widetilde{\mathcal O}\left(\sqrt{d\sum_{t=1}^T σ_t^2} + 1
ight)$ regret, interpolating between worst-case and benign settings.
New algorithm reduces bandit problem's regret bound to logarithmic in dimension.
problem Sparse linear bandit problem with sparse reward structure.
method Proposes an algorithm that uses compatibility condition on optimal arm.
result Achieves regret bound of O(poly log dT) without additional diversity assumptions.
FLIPHAT addresses joint differential privacy for high-dimensional sparse linear bandits.
problem Efficient sequential decision-making with high-dimensional sparse features and privacy concerns.
method FLIPHAT combines iterative forgetting and N-IHT for sparse linear regression, achieving optimal regret.
result FLIPHAT achieves optimal regret in terms of privacy parameters, context dimension, and time horizon.
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.
Optimal multitask learning method for sparse heterogeneous datasets.
problem Efficiently learning from multiple related datasets with sparse task-specific differences.
method MOLAR estimator, combining weighted median and shrinkage.
result Improves estimation error dependence on data dimension compared to task-wise least squares.
Thresholded Lasso bandit minimizes regret in sparse linear bandits.
problem Sparse stochastic contextual linear bandits with large feature vectors.
method Uses Lasso framework with thresholding to estimate reward function and its sparse support.
result Non-asymptotic regret upper bounds scaling as O ( log d + T ) \mathcal{O}( \log d + \sqrt{T}) O ( log d + T ) . New algorithm learns from sparse data without knowing sparsity index.
problem Sparse bandit problem where only a subset of features affects reward.
method Sparsity-agnostic Lasso Bandit algorithm that doesn't require prior sparsity index knowledge.
result Established tight regret bounds and outperforms existing methods.
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.
A new method for sparse linear bandits reduces exploration-exploitation tradeoff.
problem Sparse linear bandits in high-dimensional settings with finite actions.
method Best subset selection for parameter estimation and doubly growing epochs for regret minimization.
result Achieves nearly dimension-independent regret of i l d e O ( s T ) ilde{\mathcal{O}}(s\sqrt{T}) i l d e O ( s T ) with high probability. Novel algorithm reduces feature inclusion in online decision-making.
problem Optimizing decision-making for personalized user experiences with fairness.
method Online Batched Sequential Inclusion (OBSI) algorithm for sequential feature inclusion.
result OBSI outperforms other algorithms in terms of regret, relevance of features, and compute.
New method for linear bandits with unknown sparsity, improving sparse regret bounds.
problem Sparse regret bounds for unknown sparsity and adversarial action sets.
method Combines online to confidence set conversions with randomized model selection over nested confidence sets.
result First sparse regret bounds for unknown sparsity and adversarial action sets.
New distributions allow greedy arm selection in sparse bandit problems.
problem Sparse contextual bandit problem with sparse parameters and feature distributions.
method Introduced new distribution classes and demonstrated that mixtures of these distributions are also greedy-applicable.
result Greedy algorithm applicable to a wider range of arm feature distributions, including those with origin-asymmetric support.
New algorithms reduce sample complexity for multiclass contextual bandits.
problem Designing efficient algorithms for multiclass contextual bandits with sparse rewards.
method Two complementary approaches: decision-estimation coefficient analysis and low-variance exploration.
result Achieved optimal sample complexity bounds for multiclass contextual bandits.
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 )) . 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.
Paper addresses regret minimization and inference in high-dimensional online decision-making.
problem Regret minimization and statistical inference in high-dimensional online decision-making.
method Integrates ε-greedy bandit algorithm with hard thresholding for sparse bandit parameters and debiasing method for inference.
result Achieves either O ( T 1 / 2 ) O(T^{1/2}) O ( T 1/2 ) regret or O ( T 1 / 2 ) O(T^{1/2}) O ( T 1/2 ) -consistent inference, with trade-off between exploration and exploitation. A novel approach tackles sparse linear bandits with reduced communication costs and minimal cumulative regret.
problem Sparse linear bandits with high-dimensional feature vectors and limited relevant features.
method Cooperative Thresholded Lasso using Lasso and ridge regression for dimension reduction and aggregation.
result Regret bound of O ( s 0 log d + s 0 T ) \mathcal{O}(s_0 \log d + s_0 \sqrt{T}) O ( s 0 log d + s 0 T ) with high probability. Algorithm optimizes collaborative learning among distributed clients using kernel-based bandits.
problem Optimizing personalized objectives in a distributed system with limited global information.
method Kernel-based bandit framework with surrogate Gaussian process models, sparse approximations.
result Order-optimal regret performance (up to polylogarithmic factors) and reduced communication overhead.
Optimal algorithm for high-dimensional stochastic linear bandits with sparse parameters.
problem High-dimensional stochastic linear bandits with sparse parameters.
method Three-stage arm selection algorithm using thresholded Lasso for estimation.
result Achieves exact minimax optimality in cumulative regret.
A new algorithm reduces communication costs for collaborative decision-making across clients.
problem Collaborative decision-making with sparse rewards and heterogeneous contexts.
method Federated Lasso algorithm for sparse linear contextual bandits.
result Achieves near-optimal regret with logarithmic communication costs.
Stochastic zeroth-order (SZO), or gradient-free, optimization allows to optimize arbitrary functions by relying only on function evaluations under parameter perturbations, however, the iteration complexity of SZO methods suffers a factor proportional to the dimensionality of the perturbed function. We show that in scen…
Unified framework for high-dimensional bandit problems with low-dimensional structures.
problem Stochastic high-dimensional bandit problems with low-dimensional structures.
method Proposed a simple unified algorithm and a general analysis framework for the regret upper bound.
result Unified algorithm achieves comparable regret bounds in various high-dimensional bandit problems.
New algorithms for generalized linear bandits with unknown reward functions.
problem Misspecification of reward functions in existing bandit algorithms.
method Introducing single index bandits, proposing STOR, ESTOR, and GSTOR algorithms.
result Achieved nearly optimal regret bound of i l d e O T ( T ) ilde{O}_T(\sqrt{T}) i l d e O T ( T ) . Develops a method to tackle high-dimensional linear bandits with knapsacks using online sparse estimation and dual variables.
problem High-dimensional linear bandits with knapsacks.
method Online hard thresholding algorithm for sparse estimation, integrated with primal-dual scheme.
result Achieves sub-linear regret that scales logarithmically with feature dimension, improving on prior work.
Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. Most of the existing algorithms have regret proportional to a polynomial function of the context dimension, d d d . In many applications ho…
Study sparsity benefits in infinite feature contextual bandits.
problem Minimizing regret in infinite feature contextual bandits.
method Novel reduction to multi-armed bandits, Feel-Good Thompson Sampling algorithm.
result Regret bounds match lower bounds up to logarithmic factors, logarithmic dependence on effective features.
New algorithm tackles multi-agent bandits with heavy-tailed data.
problem Maximizing system performance in multi-agent settings with heavy-tailed data.
method Algorithm exploits hub-like structures and synchronization among clients.
result Regret bound of O ( M 1 − 1 α log T ) O(M^{1 -\frac{1}α} \log{T}) O ( M 1 − α 1 log T ) for homogeneous settings, O ( M log T ) O(M \log{T}) O ( M log T ) for heterogeneous. Algorithm achieves comparable performance to fully dynamic data with only a few batches.
problem High-dimensional multi-armed contextual bandits with batched feedback.
method Provable sample-efficient algorithm using batch allocation method.
result Achieves regret bounds comparable to fully sequential setting with only L = O(log T) batches.
We propose the first reduction-based approach to obtaining long-term memory guarantees for online learning in the sense of Bousquet and Warmuth, 2002, by reducing the problem to achieving typical switching regret. Specifically, for the classical expert problem with K K K actions and T T T rounds, using our framework we dev…
Deep Reinforcement Learning has been shown to be very successful in complex games, e.g. Atari or Go. These games have clearly defined rules, and hence allow simulation. In many practical applications, however, interactions with the environment are costly and a good simulator of the environment is not available. Further…
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 ) . A new algorithm reduces frequentist regret in multi-agent bandit problems with sparse hypergraphs.
problem Deriving a frequentist regret bound for Thompson sampling in multi-agent settings with sparse hypergraphs.
method Proposed ε ε ε -exploring Multi-Agent Thompson Sampling ( ε ε ε -MATS) algorithm that combines exploration and exploitation strategies. result Achieves a worst-case frequentist regret bound sublinear in time horizon and local arm size, optimal up to constants and logarithms for sparse hypergraphs.
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 study a consideration for the expl…
New algorithm minimizes regret in multi-armed bandits with network interference.
problem Minimizing regret in online experiments with network interference.
method Sparse network interference model, discrete Fourier analysis, linear regression-based algorithms.
result Provable low regret algorithms for sparse interference networks.
The paper tackles adaptive targeting in networks with interference effects.
problem Adaptive targeting under network interference in a bandit setting.
method Linear model in a sparse regime, analyzing different levels of knowledge of the interference structure.
result Unified view of how knowledge of the interference structure affects online learning efficiency.
We propose a new algorithm for hyperparameter selection in machine learning algorithms. The algorithm is a novel modification of Harmonica, a spectral hyperparameter selection approach using sparse recovery methods. In particular, we show that a special encoding of hyperparameter space enables a natural group-sparse re…
The stochastic linear bandit problem proceeds in rounds where at each round the algorithm selects a vector from a decision set after which it receives a noisy linear loss parameterized by an unknown vector. The goal in such a problem is to minimize the (pseudo) regret which is the difference between the total expected …
Algorithm reduces regret in distributed kernel bandits with shared randomness.
problem Minimizing regret in collaborative function maximization.
method Uniform exploration at local agents and shared randomness with central server.
result Achieves optimal regret order with sublinear communication cost.
Algorithm optimizes bandit decisions with changing action sets using Gaussian processes.
problem Optimizing decisions in a bandit problem with time-varying action sets.
method Proposes an algorithm called O'CLOK-UCB using Gaussian processes to handle changing action sets and contexts.
result Achieves regret bound of i l d e O ( λ ∗ ( K ) K T γ K T ( ∪ t ≤ T X t ) ) ilde{O}(\sqrt{λ^*(K)KTγ_{KT}(\cup_{t\leq T}\mathcal{X}_t)} ) i l d e O ( λ ∗ ( K ) K T γ K T ( ∪ t ≤ T X t ) ) with high probability.