A new method for active learning works well across all label budgets.
problem Active learning methods perform poorly in both low and high label budgets.
method Uncertainty Herding: a simple, computationally fast method that optimizes uncertainty coverage.
result Uncertainty Herding nearly optimizes distribution-level coverage and performs well across various active learning tasks.
BUOCA optimizes crowdsourcing budgets by assigning more workers to hard tasks.
problem Crowdsourcing budget inefficiency due to uniform worker assignment.
method Optimal worker allocation based on task difficulty and features, without using worker profiles.
result Large budget savings (up to 49%) with minimal accuracy loss.
A heuristic method refines search space for Bayesian optimization with low budget.
problem Efficiently optimize objective function with limited evaluation budget.
method Divide search space into promising regions to refine Bayesian optimization.
result Bayesian optimization with proposed method outperforms standard Bayesian optimization.
Study optimal policies under budget and coverage constraints.
problem Optimal policy learning with budget and coverage constraints.
method Combination of knapsack structure, affine threshold rule, linear programming relaxation, Greedy-Lagrangian (GLC), and rank-and-cut (RC) algorithms.
result GLC closely approximates the optimal solution and achieves near-optimal performance in finite samples; RC is approximately optimal under certain conditions.
Optimizes AI learning with limited human feedback budgets.
problem Optimizing allocation of a fixed annotation budget for AI learning.
method Preference-Calibrated Active Learning (PCAL) using semi-parametric inference.
result Proves asymptotic optimality and robustness of the PCAL estimator.
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
problem Optimizing budget allocation for drivers and riders to maximize business value.
method End-to-end machine learning and optimization procedure using feature store, model training, and ADMM.
result Substantially improved Uber's resource allocation efficiency through high-dimensional optimization.
The paper tackles online optimization with DR-submodular functions and linear budgets.
problem Optimizing points over time with long-term budget constraints and DR-submodular objectives.
method Proposes OSPHG algorithm to achieve sub-linear regret and budget violation bounds.
result Achieves sub-linear bounds for both regret and total budget violation under certain window lengths.
Neural Index Policy for multi-action bandits with heterogeneous budgets.
problem Real-world settings often involve multiple interventions with heterogeneous costs and constraints, breaking classical assumptions.
method Introduces a Neural Index Policy (NIP) that learns to assign budget-aware indices to arm-action pairs using a neural network and differentiable knapsack layer.
result Empirically achieves near-optimal performance while strictly enforcing heterogeneous budgets and scaling to hundreds of arms.
New algorithms for model selection in linear bandits adapt to instance complexity.
problem Adapting to the instance-dependent complexity of the true model in linear bandits.
method Design of algorithms in fixed confidence and fixed budget settings, leveraging experimental design and selection-validation procedures.
result Near instance optimal guarantees for model selection in linear bandits.
New algorithm identifies best arm with optimal budget usage.
problem Identifying the arm with the highest mean reward from multiple options.
method Proposes Almost Tracking, a closed-form algorithm for anytime best arm identification.
result Proven to be rate-optimal and outperforms existing algorithms.
Paper uses Mirror Descent for efficient risk budgeting portfolios.
problem Computing optimal risk budgeting weights for various risk measures.
method Employed Mirror Descent algorithms in deterministic and stochastic settings.
result Established convergence and quantitative rate for averaged Mirror Descent algorithm.
In the present paper, the minimal investment risk for a portfolio optimization problem with imposed budget and investment concentration constraints is considered using replica analysis. Since the minimal investment risk is influenced by the investment concentration constraint (as well as the budget constraint), it is i…
DSA efficiently allocates sparsity across layers for budgeted pruning.
problem Efficiently distributing resources (sparsity) across layers in pruning under resource constraints.
method DSA uses differentiable pruning to find continuous layer-wise pruning ratios via gradient-based optimization.
result DSA achieves superior performance and significantly reduces the time cost of pruning.
Paper proves existence and computation of Risk Budgeting portfolios.
problem Challenges to mean-variance framework sensitivity.
method Mathematical proofs and stochastic algorithms for risk measures.
result Existence and uniqueness of Risk Budgeting portfolios for various risk measures.
SORTE optimizes systemic performance over individual rationality.
problem Systemic risk and optimal risk transfer.
method Endogenous determination of budget constraints through systemic utility maximization.
result Existence, uniqueness, and Pareto optimality of SORTE.
Georgia needs a new budget code to manage fiscal policies effectively.
problem Weak and incomplete law on Budget System hinders fiscal policy implementation.
method Develop and adopt a new Budget Code with equal force as the Tax Code.
result Effective correlation between state, regional, and local budgets is crucial for social-economic development.
This paper extends risk parity to continuous-time, solving risk budgeting problems.
problem Achieving robust risk across different assets in continuous-time.
method Characterizing risk contributions and solving risk budgeting problems using continuous-time terminal variance.
result Risk contributions and risk budgets can be represented as predictable processes in continuous-time.
A new method for calculating risk budgeting portfolios is proposed.
problem Calculating risk budgeting portfolios efficiently and theoretically.
method Defining a Cauchy sequence within the simplex of R^n, leading to a straightforward algorithm.
result The proposed method avoids computational challenges and provides theoretical guarantees.
New method optimizes costly functions with unknown costs and budget constraints.
problem Optimizing functions with unknown and heterogeneous evaluation costs under a budget constraint.
method Budgeted multi-step expected improvement acquisition function.
result Our method outperforms existing approaches in various synthetic and real problems.
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.
We present a dual subspace ascent algorithm for support vector machine training that respects a budget constraint limiting the number of support vectors. Budget methods are effective for reducing the training time of kernel SVM while retaining high accuracy. To date, budget training is available only for primal (SGD-ba…
Bayesian optimization with directionally constrained search improves efficiency within a budget.
problem Optimizing expensive functions with limited computational resources.
method Directionally constrained search to allocate model capability efficiently.
result Our approach outperforms in finding the optimum within a prescribed evaluation budget.
Paper analyzes an algorithm for maximizing non-concave functions with budget constraints.
problem Maximizing non-concave functions with budget constraints under DR-submodularity.
method Generalized Sequential algorithm for online monotone DR-submodular function maximization.
result First competitive ratio bound matches known tight bound for linear objective functions.
Budgeted hyper-parameter tuning algorithm improves performance.
problem Optimizing hyper-parameters with resource constraints.
method Sequential decision making, Bayesian model, action-value function.
result Superior performance across various budgets.
Framework uses optimal transport to quantify model risk in stochastic path laws.
problem Model risk in stochastic path laws.
method Signature-induced optimal transport framework.
result Explicit robust bounds and budget-aware sparse surrogate method.
Improved portfolio optimization reduces sensitivity to neural network initialization.
problem High sensitivity to neural network initialization in portfolio optimization.
method Robust end-to-end framework for risk budgeting portfolios.
result Enhanced stability in portfolio optimization without compromising performance.
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.
The paper improves PCS approximation for ranking and selection under limited simulation budgets.
problem Improving finite sample performance in Ranking and Selection.
method Develops a Bahadur-Rao type expansion for PCS, proposes a novel FCBA policy.
result FCBA policy achieves superior PCS performance compared to traditional methods.
Proposes a method to optimize budget allocation for collecting and analyzing streaming data.
problem Optimizing resource allocation for collecting and analyzing streaming data.
method Formulates optimization problems to allocate budgets for collecting input data and running simulations, characterizes asymptotic behavior of performance estimators, and develops a multi-stage simultaneous budget allocation procedure.
result Demonstrates competitive performance of the proposed procedure through numerical studies.
Efficiently simulates risk budgeting portfolios using novel algorithms.
problem Estimating risk contributions in portfolios efficiently.
method Cutting planes algorithm, specialised SGD for Expected Shortfall, numerical simulations.
result Outperforms standard convex optimisation solvers in estimating risk budgeting portfolios.
Adam is the most practical optimizer, especially in low-budget scenarios.
problem Evaluating optimizers' performance without considering hyperparameter tuning costs.
method Evaluated a variety of optimizers on standard datasets and architectures, accounting for hyperparameter tuning costs.
result Adam is the most practical solution, especially in low-budget scenarios.
We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning. We investigate the near-optimality of greedy algorithms for this problem with both modular and non-modular cost functions. In both case…
Bayesian algorithm improves best-arm identification within fixed budget.
problem Maximizing probability of identifying optimal arm within fixed budget.
method Proposes Bayesian elimination algorithm and derives upper bound on misidentification probability.
result Upper bound on misidentification probability reflects prior quality and matches lower bound.
Optimal strategy found for identifying best arm in bandits with small gap.
problem Best arm identification in two-armed bandits with a fixed budget and small gap.
method Neyman allocation rule augmented with inverse probability weighting.
result Proposed strategy is asymptotically optimal when gap is small.
Optimal probing framework for scalable network monitoring.
problem Efficiently monitor growing cloud networks with limited budgets.
method A- and E-optimal experimental designs, Frank-Wolfe algorithm approximations.
result Significant reduction in probing budget with low estimation errors.
Quantum computing optimizes ESG portfolios efficiently.
problem Optimizing investment portfolios with risk, return, and ESG considerations.
method Formulated discrete Markowitz portfolio theory (DMPT) for quantum annealers, incorporating ESG ratings.
result Discrete portfolios converge to continuous solutions as budgets increase, outperforming traditional methods.
A method learns to solve multilevel combinatorial problems with two players.
problem Multilevel combinatorial optimization problems with multiple players.
method Value-based multi-agent reinforcement learning in a graph neural network framework.
result Close to optimal solutions on graphs up to 100 nodes, with a significant speedup.
A new approach to risk allocation balances asset and factor risks.
problem Challenges in estimating expected returns for portfolio optimization.
method Risk Budgeting framework that allocates risk at the factor level.
result Effective portfolios can be constructed by balancing asset and factor risks.
Develops risk budgeting portfolios with weight constraints.
problem Complex optimization problem with weight constraints.
method Developed algorithms combining CCD, ADMM, proximal operators, and Dykstra's algorithm.
result Found numerical solutions for risk budgeting portfolios.
Open problem: fixed-budget best arm identification complexity.
problem Understanding the complexity of identifying the best arm in a fixed budget setting.
method Analyzing existing results and conjectures in the fixed-confidence setting.
result Open questions remain about the fixed-budget setting.
Optimizes RTB bidding without exploration, improving performance under various budgets.
problem Lack of clear evaluation and generalization issues in RTB systems.
method Maximum entropy principle and conditional independence structures to train a model that generalizes to unseen budget conditions.
result Significantly improved performance under various budget settings compared to baselines.
New OCBA procedures minimize PICS in robust R&S.
problem Selecting the best alternative under input uncertainty.
method Developed new asymptotically optimal OCBA procedures.
result Procedures minimize probability of incorrect selection.
Optimizes bidding strategies for LinkedIn ads across multiple platforms.
problem Optimizing automated bidding agents for dynamic online marketplaces.
method Developed a general optimization framework for buyer's interest, agnostic to auction mechanisms.
result Automatically guarantees the optimality of budget allocation across ad units and platforms.
DARTS optimizes covariate selection in trials with limited data.
problem Limited budget for high-dimensional pretreatment data.
method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).
result DARTS efficiently concentrates budget on informative features.
This paper uses robust optimization to analyze supply chain resilience.
problem Supply chain resilience analysis of multi-modal logistics networks.
method Robust optimization with budget-of-uncertainty.
result Interactive effects of network size, disruption scale, and degree on resilience.
Paper introduces a privacy-preserving line search method for optimization.
problem Optimization performance depends on step size tuning, which is difficult and privacy-sensitive.
method Introduces a stochastic adaptive line search algorithm that satisfies differential privacy.
result The algorithm efficiently uses privacy budget and outperforms existing private optimizers.
Optimizing rewards under budget constraints with correlated costs and rewards.
problem Maximizing total expected reward under a budget constraint on total cost with correlated and potentially heavy-tailed cost-reward pairs.
method Proposes algorithms exploiting correlation between cost and reward via linear minimum mean-square error estimation to achieve tight regret bounds.
result Achieves O(logB) regret for a budget B>0 under certain moment conditions. New algorithms identify Pareto optimal sets in multi-objective bandit problems.
problem Identifying Pareto optimal sets in multi-objective bandit problems.
method Empirical Gap Elimination (EGE) algorithms combining hardness estimation and elimination schemes.
result Two EGE algorithms have exponentially decaying error probabilities with budget.