Deep learning model predicts wind-wave relationship.
problem Characterize ocean wave climate for engineering applications.
method Two-stage deep learning model: CNN for spatial features, LSTM for temporal dependencies.
result Predicts spatio-temporal relationship between wind and significant wave height.
Algorithm identifies best arm in piecewise stationary linear bandits with minimal samples.
problem Identifying the best arm in a piecewise stationary linear bandit model with unknown contexts and changepoints.
method Design of PS ε \varepsilon ε BAI + ^+ + algorithm, consisting of PS ε \varepsilon ε BAI and N ε \varepsilon ε BAI subroutines. result PS ε \varepsilon ε BAI + ^+ + achieves optimal sample complexity up to a logarithmic factor. Algorithm identifies best arm with prior info in structured bandits.
problem Bayesian fixed-budget best-arm identification in structured bandits.
method Prior-dependent allocations based on structure and prior information.
result Improved theoretical bounds and robust performance across diverse models.
UCB exploration improves best arm identification in fixed-budget settings.
problem Best arm identification in fixed-budget scenarios.
method Adaptive allocations based on upper confidence bounds (UCBs) with prior information learning.
result Empirically and theoretically efficient for Bayesian BAI problem with improved performance.
Quantum algorithm solves best arm identification problem faster.
problem Identifying the best option in a set of choices.
method Quantum amplitude amplification algorithm.
result Quadratically faster than classical methods.
Algorithm optimizes two objectives in bandits: minimizing regret and identifying best arm.
problem Balancing exploration and exploitation for optimal performance in multi-armed bandits.
method Design and analysis of BoBW-lil'UCB ( γ ) (γ) ( γ ) algorithm, establishing lower bounds. result BoBW-lil'UCB ( γ ) (γ) ( γ ) achieves optimal performance for RM or BAI under different γ γ γ values. The paper tackles best arm identification with minimal regret in experiments.
problem Identifying the best arm with minimal regret in experiments.
method Information-theoretic techniques and Double KL-UCB algorithm.
result Achieves asymptotic optimality in identifying the best arm with minimal regret.
Improved BAI under DP reduces gap to constant.
problem Fixed-confidence BAI under global DP for Bernoulli distributions.
method New lower bound, stopping rule, and Top Two sampling rule.
result Reduces gap to a small multiplicative constant.
Unified framework for best arm identification and dueling bandits regret minimization.
problem Best arm identification and dueling bandits regret minimization.
method Tree-Guided Identify-Then-Exploit (TG-ITE) framework.
result Unified approach achieving optimal sample complexity and regret guarantees.
New policy minimizes error in finding best arm with privacy constraints.
problem Best arm identification with differential privacy constraints.
method Maximum absolute determinants principle for ε \varepsilon ε -DP policy. result Error probability decays exponentially in T T T with matching exponents. New algorithm improves best arm identification in Bayesian settings.
problem Finding the arm with the highest mean in unknown distributions.
method Developed a variant of successive elimination algorithm.
result Achieved optimal performance in Bayesian setting with logarithmic gap.
New algorithm identifies best target arm with known additive relationship between source and target MAB instances.
problem Identifying the best arm in a target MAB instance when only source arms can be pulled and there's a known additive relationship between the two.
method Proposes an LUCB-style algorithm to identify an ε-optimal target arm with high probability.
result Theoretical analysis highlights aspects of the transfer learning problem and recovers the LUCB algorithm for single domain BAI as a special case.
GNA optimally identifies the best arm with small gaps.
problem Best arm identification in fixed-budget settings.
method Generalized Neyman Allocation (GNA) for asymptotically locally minimax optimal BAI.
result GNA's worst-case bounds match the lower and upper bounds in the small-gap regime.
The paper addresses privacy-preserving BAI in clinical trials and user studies.
problem Privacy-preserving Best Arm Identification in adaptive clinical trials and user studies.
method The paper derives lower bounds on sample complexity for BAI algorithms with differential privacy constraints and proposes private variants of Top Two algorithms.
result Private variants of Top Two algorithms achieve asymptotic optimality in terms of sample complexity for BAI problems under differential privacy constraints.
The paper studies privacy-protected BAI with fixed confidence, deriving lower bounds and proposing an adaptive algorithm.
problem Privacy-protected Best Arm Identification (BAI) in data-sensitive applications.
method Derives lower bounds on sample complexity, proposes AdaP-TT algorithm with Laplace noise, and validates with experiments.
result AdaP-TT matches the sample complexity lower bound up to constants in the high-privacy regime.
Paper tackles efficient BAI in graph-smooth bandits.
problem Best arm identification with graph smoothness constraint.
method Gradient ascent algorithm for sample complexity.
result Asymptotically optimal strategy for BAI.
New algorithm for efficiently identifying the best arm in stochastic bandits.
problem Best arm identification in stochastic multi-armed bandits with fixed confidence.
method Sequential probability ratio tests for arm selection.
result Asymptotically optimal sample complexity and guaranteed δ − δ- δ − PAC performance. Optimized Sharpe Ratio for better risk-adjusted decision-making in multi-armed bandits.
problem Challenging to optimize Sharpe Ratio (SR) in multi-armed bandits (MAB) due to constant regret.
method Proposed UCB-RSSR algorithm for RSSR maximization, derived path-dependent concentration bound and regret guarantees.
result UCB-RSSR outperforms existing algorithms and finds applications in risk-aware portfolio management.
Experiments in particle physics produce enormous quantities of data that must be analyzed and interpreted by teams of physicists. This analysis is often exploratory, where scientists are unable to enumerate the possible types of signal prior to performing the experiment. Thus, tools for summarizing, clustering, visuali…
A new method for identifying the best arm in multi-armed bandits with mediator feedback.
problem Sequential decision-making with unknown mediator policies.
method Best-arm identification under mediators' feedback (BAI-MF) with statistical lower bound and sequential decision-making strategy.
result The proposed algorithm matches the lower bound on sample complexity for identifying the best arm.
New algorithm identifies best arm in semiparametric bandits with near optimal efficiency.
problem Fixed-confidence Best Arm Identification in semiparametric bandits with unknown baseline shift.
method Phase-elimination algorithm based on orthogonalized regression design.
result Nearly optimal high-probability sample-complexity upper bound established.
New algorithm identifies best arm efficiently in stochastic bandits.
problem Efficiently identifying the best arm in stochastic bandits with optimal performance.
method Develops a computationally efficient algorithm for optimal best arm identification.
result Achieves optimal performance with minimal computational complexity.
Study optimal stopping problems with finite-time horizon and proves continuity and strict monotonicity of the boundary.
problem Optimal stopping problems with finite-time horizon and state-dependent discounting.
method Linear diffusion process, time-homogeneous gain function, fine regularity properties, continuity and strict monotonicity proof.
result Proves continuity and strict monotonicity of the optimal stopping boundary under mild assumptions.
Box Thirding identifies the best arm efficiently under limited samples.
problem Efficiently identifying the best arm with limited sampling.
method Iterative ternary comparison of arms, discarding the weakest and exploring the best.
result Achieves comparable performance to Successive Halving with less predefined parameters.
New algorithm identifies best arm in non-stationary linear bandits with improved complexity.
problem Best arm identification in non-stationary linear bandits with adversarial parameters.
method Proposed Adjacent-optimal design and e x t s f A d j a c e n t − B A I extsf{Adjacent-BAI} e x t s f A d j a ce n t − B A I algorithm. result Error probability matches arm-set-dependent lower bound up to constants.
Improved knowledge gradient (iKG) outperforms the original KG algorithm in best arm identification problems.
problem Best arm identification (BAI) problem with limitations of the original KG algorithm.
method Follows the one-step look ahead of KG but chooses the measurement that maximizes the probability of selecting the best arm.
result Improved knowledge gradient (iKG) is asymptotically optimal and easier to extend to variant BAI problems.
New algorithms improve best-arm identification with varying rewards.
problem Identifying the best arm with varying reward variances in fixed budget.
method Proposed two algorithms: SHVar for known variances, SHAdaVar for unknown variances; uses non-uniform budget allocation.
result Bounding misidentification probabilities for both algorithms.
New algorithm identifies best arm in rare event scenarios.
problem Identifying the best arm with tiny reward probability.
method Approximated Compound Poisson process for faster algorithms.
result Improved computational efficiency with minor sample complexity increase.
New algorithm for multi-fidelity bandits reduces costs and improves regret.
problem Optimizing decisions with varying costs and accuracy in multi-fidelity bandits.
method Cost complexity bounds, algorithmic framework, elimination-based algorithm.
result New regret definition and matching upper and lower bounds for elimination-based algorithm.
TRIPLE efficiently optimizes prompts with a budget constraint.
problem Efficiently selecting good prompts from a pool of candidates.
method TRIPLE connects prompt optimization to best arm identification in MAB, leveraging BAI-FB tools.
result TRIPLE outperforms baselines on multiple tasks with limited budget constraints.
Algorithm identifies best arm with biased proxy and selective ground truth audits.
problem Fixed-confidence best-arm identification with biased proxy and selective ground truth.
method Propensity-weighted estimator and adaptive auditing algorithm.
result Plug-in Neyman rule achieves near-oracle audit efficiency.
New policy combines Thompson sampling with best challenger rule for best arm identification.
problem Best arm identification in bandit framework with fixed confidence.
method Combines Thompson sampling with best challenger rule.
result Asymptotically optimal for any two-armed bandit problems, near optimal for general K-armed bandit problems.
Paper shows FB and FC are equally hard up to logarithmic factors.
problem Comparing fixed budget and fixed confidence approaches in best-arm identification.
method Proposes FC2FB, a meta algorithm converting FC to FB.
result FC sample complexity is an upper bound for FB sample complexity up to logarithmic factors.
New strategy optimally identifies best arm in unknown variance Gaussian bandits.
problem Identifying the best arm in two-armed Gaussian bandits with unknown variances.
method Proposes a Neyman Allocation (NA)-Augmented Inverse Probability weighting (AIPW) strategy to estimate variances and draw arms adaptively.
result Demonstrates asymptotic optimality of the proposed strategy in the small-gap regime.
Study on a new family of problems interpolating expert advice and multi-armed bandits.
problem A new family of problems combining expert advice and multi-armed bandits.
method Proved minimax regret bounds and designed optimal PAC algorithms for pure exploration.
result Tight minimax regret bounds and optimal PAC algorithm for m \mathbf{m} m -BAI. Optimal top-2 method improves best arm identification with reduced error.
problem Identifying the arm with the highest mean in a set of arms.
method A novel top-2 algorithm that pulls the empirical best arm with probability β and the challenger arm otherwise.
result The proposed algorithm matches the information theoretic lower bound on sample complexity as δ approaches 0.
We show that representations of the loop braid group arise from Aharonov-Bohm like effects in finite 2-group (3+1)-dimensional topological higher gauge theory. For this we introduce a minimal categorification of biracks, which we call W-bikoids (welded bikoids). Our main example of W-bikoids arises from finite 2-groups…
We study Lie algebras admitting para-Kähler and hyper-para-Kähler structures. We give new characterizations of these Lie algebras and we develop many methods to build large classes of examples. Bai considered para-Kähler Lie algebras as left symmetric bialgebras. We reconsider this point of view and improve it in order…
This paper presents a new clustering algorithm for space-time data based on the concepts of topological data analysis and in particular, persistent homology. Employing persistent homology - a flexible mathematical tool from algebraic topology used to extract topological information from data - in unsupervised learning …
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the finite-dimensional Gaussian approach from Maatouk and Bay (2017) which can satisfy inequality conditions everywhere (either boundedness, monoton…
This paper improves neural network learning by escaping the NTK regime and efficiently learning sparse polynomials.
problem Learning sparse polynomials efficiently using neural networks.
method Spectral analysis of NTK, identifying 'good' directions, and constructing a regularizer.
result Gradient descent on a two-layer neural network can learn sparse polynomials efficiently, improving over the NTK and QuadNTK.
UCB-Advantage learns MDPs with O ( H 2 S A T ) O(\sqrt{H^2SAT}) O ( H 2 S A T ) regret.
problem Model-free reinforcement learning in finite-horizon MDPs.
method Reference-Advantage decomposition for low regret.
result Achieves i l d e O ( H 2 S A T ) ilde{O}(\sqrt{H^2SAT}) i l d e O ( H 2 S A T ) regret, matching best known bounds. A new method reduces the bias in estimating inverse covariance matrices from sketches.
problem Reducing the bias in estimating inverse covariance matrices from sketches.
method Developed a framework for analyzing inversion bias and proposed a new sketching technique called LEverage Score Sparsified (LESS) embeddings.
result The new sketching technique reduces the inversion bias to O ( 1 / d ) O(1/\sqrt d) O ( 1/ d ) for m = O ( d ) m=O(d) m = O ( d ) , significantly smaller than the Θ ( 1 ) Θ(1) Θ ( 1 ) approximation error. Recent progress in separating the speech signals from multiple overlapping speakers using a single audio channel has brought us closer to solving the cocktail party problem. However, most studies in this area use a constrained problem setup, comparing performance when speakers overlap almost completely, at artificially…
Improved NODEs for long-term time series forecasting.
problem Dealing with complex, multi-frequency data.
method Progressive learning paradigm with curriculum learning.
result Performance improved by over 64%.
Conditional gradients constitute a class of projection-free first-order algorithms for smooth convex optimization. As such, they are frequently used in solving smooth convex optimization problems over polytopes, for which the computational cost of orthogonal projections would be prohibitive. However, they do not enjoy …
VA-LUCB identifies best arm with variance constraint, achieving optimal sample complexity.
problem Identifying the best arm with variance constraint under fixed confidence.
method Parameter-free algorithm VA-LUCB, analyzing sample complexity and proving lower bounds.
result Optimal sample complexity up to a logarithmic factor in H V A H_{VA} H V A , demonstrated by experiments. The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.
problem Identifying the treatment that maximizes the expected natural direct potential outcome (NDPO) in causal mediation analysis.
method Developed a fixed-confidence best-arm identification (BAI) algorithm based on the Track-and-Stop (TaS) framework, using a cutting-set method to solve a semi-infinite optimization problem.
result The proposed algorithm achieves sample-efficient identification with a high-probability correctness guarantee and asymptotic optimality.