New method reduces total cost constraints in CBwK to sqrt(T) with fairness application.
problem Maximize rewards while adhering to total cost constraints in CBwK.
method Dual strategy based on projected-gradient-descent updates.
result Total cost constraints reduced to sqrt(T) with poly-logarithmic terms.
Paper tackles investment risk with cost and return constraints using replica analysis.
problem Investment risk minimization under cost and return constraints.
method Replica analysis for portfolio optimization problems.
result Derivation of macroscopic theory for optimal solution.
Optimizes query routing to LLMs under cost and resource constraints.
problem Non-uniform or adversarial batching in per-query routing methods leads to cost inefficiency.
method Batch-level, resource-aware routing framework that jointly optimizes model assignment for each batch.
result Robust routing framework improves accuracy by 1-14% over non-robust methods.
New algorithms minimize costs while adhering to risk constraints in adversarial contextual bandits.
problem Minimizing cumulative cost while satisfying long-term risk constraints in adversarial contextual bandits.
method Developed a meta algorithm using online mirror descent for the full information setting, extended to contextual bandit with risk constraints using expert advice.
result Achieved near-optimal regret in terms of minimizing total cost, with sublinear growth of cumulative risk constraint violation.
We investigate the application of two heuristic methods, genetic algorithms and tabu/scatter search, to the optimisation of realistic portfolios. The model is based on the classical mean-variance approach, but enhanced with floor and ceiling constraints, cardinality constraints and nonlinear transaction costs which inc…
UCRL-CMDP algorithm optimizes RL with constraints on average costs.
problem Optimizing RL in MDPs with average cost constraints.
method Model-based RL algorithms maximizing reward while keeping costs within bounds.
result UCRL-CMDP algorithm's expected regret is upper-bounded by $T^{2\slash 3}$ .
Cost-aware multi-objective Bayesian optimization for non-uniformly expensive functions.
problem Non-uniform cost of function evaluations in Bayesian optimization.
method Introduces cost-aware constraints and a new acquisition function to optimize multi-objective functions with varying costs.
result Demonstrates improved optimization in hyperparameter tuning of neural networks and random forests.
Cost-sensitive learning improves predictive maintenance by reducing costs.
problem Predictive maintenance models often lead to higher costs than expected.
method Propose selecting models based on economic costs rather than traditional metrics.
result Cost reduction and fault-tolerant policies achieved through cost-sensitive learning.
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 policies for MAB with cost constraint developed.
problem Maximizing outcomes in multi-armed bandit with cost constraint.
method Developed asymptotically optimal policies for MAB under cost constraint.
result Constructed feasible uniformly fast (f-UF) convergent policies achieving asymptotic lower bound on regret.
A new method for optimal transport using neural ODEs that preserves marginal constraints.
problem Optimal transport between two continuous distributions with specific cost functions.
method Iterative construction of neural ODEs to minimize transport cost while preserving marginal constraints.
result Monotonic interior approach that decreases transport cost efficiently.
New methods reduce computational cost for Gaussian Markov Random Fields with sparse constraints.
problem Inference and simulation of GMRFs are computationally prohibitive with many constraints.
method Proposes a basis transformation into blocks of constrained and non-constrained subspaces.
result Significantly outperforms existing alternatives in computational cost.
Solves portfolio optimization with costs using numerical methods.
problem Dynamic portfolio optimization with transaction costs and constraints.
method Numerical dynamic programming techniques.
result Problems can now be solved tractably.
Study shows high costs for replicating financial claims with fixed fees.
problem High costs for replicating financial claims in markets with fixed transaction costs.
method Stochastic impulse control problem with terminal state constraint.
result Super--replication prices are prohibitively costly and lead to trivial strategies in continuous models.
Study optimal strategies under uncertain market parameters and borrowing costs.
problem Optimal portfolio-consumption strategies in uncertain markets.
method Robust utility maximization framework with explicit solutions.
result Impacts of uncertain parameters, constraints, and borrowing costs quantified.
New algorithm reduces regret and constraint violation in constrained bandit problems.
problem Optimizing under budget and stochastic constraints in resource-constrained settings.
method Lyapunov optimization methodology, t L y O n { t LyOn} t L y O n algorithm. result Achieves O ( K B log B ) O(\sqrt{K B\log B}) O ( K B log B ) regret and zero constraint-violation for large B B B . Proposes an efficient method to select models under a budget constraint in cost-sensitive learning.
problem Cost-sensitive variable selection in classification problems.
method Ensemble of model schedules to find near optimal models under a budget constraint.
result Our approach outperforms existing methods in benchmark datasets.
Paper improves COCO problem, reducing constraint violation at the cost of slightly more regret.
problem Online Convex Optimization with adversarial constraints.
method Proposes new policies that trade off regret for reduced constraint violation.
result Achieves i l d e O ( d T + T β ) ilde{O}(\sqrt{dT}+ T^β) i l d e O ( d T + T β ) regret and i l d e O ( d T 1 − β ) ilde{O}(dT^{1-β}) i l d e O ( d T 1 − β ) CCV. 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.
Efficiently tests discrete distributions with limited memory and communication.
problem Testing discrete distributions with constraints on memory and communication.
method Developed efficient algorithms for uniformity/identity and closeness testing in streaming and distributed models.
result Nearly-tight lower bounds on sample complexity and communication cost for uniformity testing.
An investor with constant relative risk aversion and an infinite planning horizon trades a risky and a safe asset with constant investment opportunities, in the presence of small transaction costs and a binding exogenous portfolio constraint. We explicitly derive the optimal trading policy, its welfare, and implied tra…
This paper optimizes trading strategies with costs and diversification constraints.
problem Optimizing trading strategies with transaction costs and diversification constraints.
method Historical multi-stage optimal trading with graph generation and search.
result Developed methods for multi-variate multi-stage optimal trading under constraints.
COF algorithm minimizes cost in multi-armed bandits with known costs and reward constraints.
problem Minimizing cost while meeting a minimum reward requirement in uncertain environments.
method COF algorithm that intelligently combines samples from all arms to gauge feasibility and minimize cost.
result COF achieves instance-dependent upper bounds on cumulative cost and quality regret.
Study allocates resources to strategic agents while balancing cost and incentives.
problem Dynamic allocation of reusable resources to strategic agents with private valuations under long-term cost constraints.
method Incentive-aware framework combining epoch-based lazy updates and randomized exploration rounds.
result Achieves i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) social welfare regret, satisfies all cost constraints, and ensures incentive alignment. The paper tackles fair correlation clustering with new algorithms and analysis.
problem Fair variants of correlation clustering under various constraints.
method Introducing a novel combinatorial optimization problem for fairlet decomposition.
result Approximation algorithms for fair correlation clustering under multiple fairness constraints.
New algorithm optimizes online network resource allocation with long-term constraints.
problem Optimal resource reservation in communication networks with job transfers and budget limits.
method Randomized exponentially weighted method for long-term constraints.
result Upper bound for regret and cumulative constraint violations established.
Proposes a novel SVM model for binary classification with different misclassification costs.
problem Real-world classification problems with varying misclassification costs.
method Incorporates performance constraints in SVM formulation to seek a hyperplane with maximal margin and misclassification rates below given thresholds.
result The proposed model gives users control over misclassification rates in one class at the expense of the other.
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.
Proposes an angle-based framework for multicategory cost-sensitive classification.
problem Cost-sensitive multicategory classification challenges.
method Angle-based cost-sensitive classification framework without sum-to-zero constraint.
result Proposed boosting algorithms yield competitive classification performances.
Optimizes insurance profits under regulatory constraints.
problem Maximizing profits while adhering to regulatory and risk policies.
method Developed a formalism for in-force business profit optimisation.
result Identified optimal asset allocation and annual opportunity cost.
Meta-learning control algorithm with finite-time guarantees for unknown systems.
problem Online control of unknown linear systems with constraints.
method Provable regret guarantees for an iterative control algorithm.
result Regret bounds of O ( T 3 / 4 ) O(T^{3/4}) O ( T 3/4 ) for controller cost and constraint violation. This work proposes an online learning approach to tighten constraints in stochastic control problems.
problem Solving chance-constrained stochastic optimal control problems is computationally challenging.
method Reformulate chance constraints as a binary regression problem and use a GP model to learn constraint-tightening parameters online.
result The approach tightens constraints more effectively, leading to lower costs in numerical experiments.
Improved learning from multiple experiments with cost constraints.
problem Learning from multiple experiments with cost constraints.
method Rademacher complexity approach to show gap between training and generalization error.
result The gap between training and generalization error is O ( C − 1 / 2 ) O(C^{-1/2}) O ( C − 1/2 ) . Study on distributed nonparametric function estimation with optimal rate and cost of adaptation.
problem Optimal rate of convergence and cost of adaptation in distributed nonparametric function estimation.
method Distributed minimax estimation and adaptive estimation under communication constraints for Gaussian sequence model and white noise model.
result Established minimax rate of convergence and exact communication cost for adaptation.
New algorithm reduces regret and constraint violation in online convex optimization with predictions.
problem Online convex optimization with time-varying constraints and predictions.
method Primal-dual algorithm combining Follow-The-Regularized-Leader with adaptive steps.
result Achieves O ( T 3 − β 4 ) \mathcal O(T^{\frac{3-β}{4}}) O ( T 4 3 − β ) regret and O ( T 1 + β 2 ) \mathcal O(T^{\frac{1+β}{2}}) O ( T 2 1 + β ) constraint violation bounds. Deep Hedging learns risk-neutral vol dynamics for option pricing.
problem Statistical arbitrage in market dynamics without transaction costs.
method Numerical approach to train market simulator and find risk-neutral density.
result Risk-neutral model for stochastic implied volatility can be used for pricing or Deep Hedging.
Non-negative constraints improve neural network defenses.
problem Effective defenses against adversarial attacks in neural networks.
method Non-negative weight constraints applied to binary and non-binary classification problems.
result Non-negative constraints can improve resistance to adversarial attacks, especially in binary classification with asymmetric costs.
This paper optimizes international portfolios considering currency overlay costs and constraints.
problem Limited risk diversification in multi-currency portfolios due to currency concentration.
method Proposes a model integrating currency overlay and asset allocation optimization, accounting for costs and constraints.
result Inclusion of costs significantly alters optimal portfolio decisions and risk-return trade-offs.
New method reduces deep learning training costs by approximating vector-jacobian products.
problem Efficiently training deep neural networks with reduced computational and memory costs.
method Randomized, unbiased approximations of vector-jacobian products during backpropagation.
result Validated potential for reducing deep learning training costs through unbiased estimates.
Designs a neural network to reduce training cost by mapping to higher dimensions.
problem High training cost in neural networks.
method Maps feature vectors to higher dimensional space, designs weight matrices to reduce cost, uses convex constraints.
result Reduces training cost as the number of layers increases, without cross-validation.
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 ( log B ) O(\log B) O ( log B ) regret for a budget B > 0 B>0 B > 0 under certain moment conditions. Paper addresses distributed optimization with time-varying constraints.
problem Distributed online optimization with time-varying coupled inequality constraints.
method Proposes a distributed online primal-dual dynamic mirror descent algorithm.
result Achieves sublinear dynamic regret and constraint violation under natural stepsize sequences.
Optimizes decision tree ensembles with feature cost constraints.
problem Learning decision rules with a budget on feature usage.
method 0-1 integer program formulation for pruning ensembles, considering feature-sharing and accuracy/cost trade-offs.
result Significant reduction in feature cost with high prediction accuracy.
Greedy algorithms near-optimal for adaptive optimization with budget constraint.
problem Adaptive optimization with budget constraint in AI and ML.
method Investigates two simple greedy algorithms and a combined algorithm for pointwise submodular and cost-sensitive submodular functions.
result Best greedy algorithm is near-optimal with respect to optimal algorithm using half the budget.
Improved machine learning with reduced tensor rank constraints and dropout.
problem Efficiently approximating large tensors in machine learning.
method Tree tensor networks with CP rank constraints and tensor dropout.
result Low-rank TTN classifier achieves 90.3% accuracy in Fashion-MNIST.
Paper derives constraints for Bayesian Knowledge Tracing parameters.
problem Issues with EM algorithm in BKT parameter estimation.
method From first principles, derives constraints on BKT parameter space.
result Novel algorithm respects derived constraints for parameter estimation.
Study examines how business units can benefit from group cohesion under regulatory constraints.
problem Regulatory constraints limit business units' ability to form a single cohesive group.
method Defined and analyzed cohesive risk measures to minimize capital costs.
result Cohesive risk measures allow groups to achieve minimal capital costs without altering individual liabilities.
Study financial contracts pricing in markets with nonproportional costs and constraints.
problem Financial contract pricing in markets with nonproportional transaction costs and portfolio constraints.
method Direct and dual characterization of market-consistent prices with acceptable risk thresholds.
result Extension of the Fundamental Theorem of Asset Pricing to include good deals and scalable good deals.