Framework solves bilevel optimization on manifolds.
problem Solving bilevel optimization problems with manifold constraints.
method Hypergradient estimation strategies on manifolds, convergence and complexity analyses.
result Efficacy demonstrated through various applications.
Extends GENO framework for GPU optimization of constrained ML problems.
problem Constrained optimization in classical machine learning.
method Extends GENO framework to GPU optimization, specifying problems in a modeling language.
result Solvers on GPU outperform state-of-the-art approaches by several orders of magnitude.
We address challenges in collaborative black-box optimization through three frameworks.
problem Challenges in distributed experimentation, heterogeneity, and privacy in black-box optimization.
method Three unifying frameworks: global, local, and predictive.
result Shift from descriptive/predictive to prescriptive federated learning in black-box optimization.
Framework optimizes multiple objectives considering input uncertainty.
problem Efficiently optimizing multiple objectives with input uncertainty.
method Robust Gaussian Process model and two-stage Bayesian optimization process.
result Found a robust Pareto frontier considering input uncertainty.
Framework optimizes expensive manufacturing processes efficiently.
problem Optimizing input parameters for advanced manufacturing methods.
method Bayesian optimization with tailored acquisition function and parallel acquisition.
result Framework efficiently finds optimal parameters with minimal process cost.
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …
RYU framework constructs safe regions for optimization problems.
problem Optimization problems with specific component functions.
method RYU framework for constructing safe regions.
result RYU framework improves upon state-of-the-art methods.
Unified approach for sequence design combining likelihood-free inference and black-box optimization.
problem Designing biological sequences efficiently and accurately.
method Unified probabilistic framework integrating likelihood-free inference and black-box optimization.
result Previous optimization methods can be adapted and new algorithms proposed within this framework.
Network structure optimization is a fundamental task in complex network analysis. However, almost all the research on Bayesian optimization is aimed at optimizing the objective functions with vectorial inputs. In this work, we first present a flexible framework, denoted graph Bayesian optimization, to handle arbitrary …
Unified deep learning framework solves various optimal transport problems.
problem Solving variational problems in optimal transport with computational challenges.
method Unified deep learning framework leveraging dual formulation of Lagrangians.
result Outperforms previous approaches in single-cell trajectory inference.
USeMOC framework reduces expensive simulations for MO optimization with constraints.
problem Efficiently optimizing multi-objective problems with constraints using expensive function evaluations.
method USeMOC framework uses surrogate models to identify promising candidates and selects the best based on uncertainty.
result USeMOC achieves more than 90% reduction in function evaluations for circuit optimization.
FAST optimizes additive segmentation for faster, more interpretable models.
problem Efficiently segmenting and interpreting complex datasets.
method Optimization framework for fast piecewise constant shape functions.
result 2 orders of magnitude faster than state-of-the-art methods.
Bayesian optimization reduces computational effort in aircraft design optimization.
problem High computational cost in industrial aircraft design optimization.
method Constrained Bayesian optimization (Super Efficient Global Optimization with Mixture of Experts)
result Significant computational efficiency improvements over existing Isight optimizers.
There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimization. We focus on two of the most important fields: stochastic optimal control, with its roots in deterministic optimal control, and reinfo…
We consider a new form of reinforcement learning (RL) that is based on opportunities to directly learn the optimal control policy and a general Markov decision process (MDP) framework devised to support these opportunities. Derivations of general classes of our control-based RL methods are presented, together with form…
Unified framework for ESG-inclusive portfolio optimization and pricing.
problem Incorporating ESG ratings into dynamic asset pricing theory.
method Introducing ESG-valued return as a linear transformation of financial and ESG scores, preserving traditional risk aversion with an ESG affinity parameter.
result Developed a more complex portfolio optimization problem in a space governed by reward, risk, and ESG score.
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.
End-to-end portfolio optimization framework bypassing covariance matrix estimation.
problem Optimizing portfolios with large numbers of assets and constraints.
method Deep learning approach that directly optimizes asset distributions without forecasting.
result Framework outperforms classical methods and handles various constraints.
Unified Lagrangian-based methods for nonsmooth nonconvex optimization.
problem Minimizing nonsmooth nonconvex functions with constraints.
method Developed a unified framework for Lagrangian-based methods using subgradient updates.
result Global convergence guarantees for the proposed framework under mild conditions.
A new Bayesian framework simplifies stochastic optimization by focusing on key parameters.
problem Bayesian methods struggle with complex structural constraints.
method Minimalist Bayesian framework that eliminates nuisance parameters via profile likelihood.
result Near-optimal regret guarantees for multi-armed bandits and convex optimization.
Unified framework for optimal liquidation with small market impact and semimartingale strategies.
problem Optimal liquidation under small market impact and portfolio liquidation.
method Semimartingale strategies and convergence results for BSDEs with singular terminal conditions.
result Unified framework for embedding two common liquidation models and microscopic foundation for semimartingale strategies.
Paper proposes a federated learning framework for relative fairness.
problem Traditional fairness in federated learning overlooks performance disparities between client subgroups.
method Uses a minimax problem approach to minimize relative unfairness, introducing a fairness index based on loss ratios.
result Empirical evaluations confirm the framework's effectiveness in maintaining model performance while reducing disparity.
A new framework for optimizing interventions with limited data.
problem Small data, default intervention data, unmodeled objectives, unforeseen consequences.
method Bandit data-driven optimization combining online bandit learning and offline predictive analytics.
result PROOF algorithm achieves no-regret and superior performance in simulations and real-world application.
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.
BADR framework optimizes fairness metrics efficiently.
problem Fairness-inefficient models in machine learning.
method Bilevel Adaptive Rescalarisation procedure.
result BADR framework recovers optimal Pareto-efficient models.
New framework for optimal transport with jumps over intermediate spaces.
problem Optimal transport with mass jumps over intermediate spaces.
method Hierarchical Jump multi-marginal transport (HJMOT) on Polish spaces.
result Existence and uniqueness of Monge solutions under sequential differentiability and twist condition.
A new framework optimizes fMRI and behavioral data for better understanding of Autism.
problem Linking complex fMRI data to behavioral measures is challenging.
method Coupled manifold optimization framework projecting fMRI onto a shared manifold and mapping to behavioral measures.
result Framework outperforms traditional methods in predicting clinical severity of Autism.
New framework for decentralized optimization of upper-linearizable functions with improved regret and complexity.
problem Decentralized optimization of upper-linearizable functions with general constraints.
method Decentralized projection-free optimization with upper-linearizable function framework.
result Regret of O(T1−θ/2) with communication complexity of O(Tθ) and linear optimization calls of O(T2θ). A new framework for predictive clustering and optimization.
problem Finding clusters of data that yield low error on a supervised target.
method Generalized optimization framework using MILP and MM for scalability.
result Models can uncover different interpretable discrete cluster structures.
Proposes a new framework for optimizing utility with state-dependent benchmarks.
problem Various interpretations of benchmarks in utility functions.
method General framework of state-dependent utility optimization with stochastic benchmarks.
result Provides optimal solutions and addresses issues of well-definedness and feasibility.
New framework for conditional risk minimization using optimal transport.
problem High-stakes decisions with side information, especially economic conditions.
method Universal framework based on union-ball formulation in optimal transport.
result Offers interpretability, tractability, and scalability for various risk functionals.
The pathwise coordinate optimization is one of the most important computational frameworks for high dimensional convex and nonconvex sparse learning problems. It differs from the classical coordinate optimization algorithms in three salient features: {\it warm start initialization}, {\it active set updating}, and {\it …
Optimal transport framework for density estimation with constraints.
problem Density estimation under expectation constraints.
method Minimizes Wasserstein distance subject to expected value constraints and regularization.
result Framework effectively addresses non-smooth constraints through annealing-like algorithm.
This paper extends Mirror Descent to Riemannian manifolds for optimization.
problem Optimization on Riemannian manifolds.
method Developed a Riemannian Mirror Descent (RMD) framework and a stochastic variant.
result Established non-asymptotic convergence guarantees for RMD and stochastic RMD.
Proposes a framework to reconcile policy learning and profit maximization in CATE estimation.
problem Aligning CATE estimation with profit maximization for optimal customer treatment decisions.
method Optimizes a novel objective function that concentrates learning capacity near the decision boundary, ensuring consistency with the original profit function.
result Consistent CATE estimates can be recovered from existing profit-maximization pipelines, allowing firms to navigate the trade-off between accuracy and profit.
A novel Bayesian optimization framework tackles multi-objective constrained problems.
problem Multi-objective optimization with constraints in engineering design.
method srMO-BO-3GP framework using three stacked Gaussian processes.
result Demonstrated effectiveness on benchmark functions and real thermomechanical model.
Unified framework improves robust causal inference, overcoming Gaussian barriers and optimization issues.
problem Improving robust causal inference in non-Gaussian settings.
method Combines gamma-Divergence, GNC, and Gatekeeper mechanism.
result Enhanced robustness and global optimization in causal effect estimation.
Paper proposes a new framework for predictive optimization without training data.
problem Prediction in a new domain without training samples.
method Proposes a simple framework for predictive optimization with zero-shot domain adaptation.
result Demonstrates the potential usefulness of the proposed framework through numerical experiments.
Proposes a new framework to optimize portfolios with reduced estimation errors.
problem Estimation errors in multiperiod mean-variance portfolio optimization.
method Reference-regulated multiperiod mean-variance (RRMV) framework.
result Improves portfolio stability and out-of-sample Sharpe ratios.
In this paper, reinforcement learning is applied to the problem of optimizing market making. A multi-agent reinforcement learning framework is used to optimally place limit orders that lead to successful trades. The framework consists of two agents. The macro-agent optimizes on making the decision to buy, sell, or hold…
This paper proposes a novel PGO framework to optimize systemic risk bailouts using neural networks.
problem Optimal bailout strategies for mitigating systemic risk in financial systems.
method Prediction-Gradient-Optimization (PGO) framework, using neural networks to approximate and forecast the objective function.
result The PGO framework effectively manages systemic risk through online optimization.
PCL framework optimizes climate risk management across three clusters.
problem Comprehensive risk management in response to climate change impacts.
method Optimization of preemptive adaptation, contingent arrangements, and loss acceptance.
result Balanced portfolio of actions across three clusters optimized for long-term aggregate outlay.
Framework optimizes model performance and interpretability for tabular data.
problem Balancing model performance and interpretability in machine learning models.
method Model-agnostic multi-objective optimization framework with evolutionary algorithm.
result Framework generates diverse models that trade off performance and interpretability efficiently.
A GPU framework speeds up BnB for discrete optimization problems.
problem Optimizing large-scale discrete problems with GPU limitations.
method Parallel BnB nodes in GPU batches, using padding and custom kernels.
result One to two orders of magnitude speedup and zero optimality gap.
Optimized fuzzy entropy framework improves feature selection and classification performance.
problem Improving feature selection and classification in fuzzy entropy frameworks.
method Implemented and compared combinations of ideal vectors, maximal similarity classifiers, and fuzzy entropy functions.
result Optimized combination of ideal vector, similarity classifier, and fuzzy entropy function achieved the most stable performance for all three datasets.
Optimal hedging framework with variational preferences under convex risk measures.
problem Optimal hedging with variational preferences under convex risk measures.
method Theoretical hedging optimization framework with dual representation of risk measures and utilities.
result Derivation of optimality and indifference pricing conditions.
Many hyperparameter optimization (HyperOpt) methods assume restricted computing resources and mainly focus on enhancing performance. Here we propose a novel cloud-based HyperOpt (CHOPT) framework which can efficiently utilize shared computing resources while supporting various HyperOpt algorithms. We incorporate conven…
Non-parametric bootstrap improves robust portfolio and trading strategy optimization.
problem Mitigating uncertainty in expected returns and covariances in financial decision-making.
method Non-parametric bootstrap framework for robust optimization without distributional assumptions.
result Improved out-of-sample performance with smoother, more stable results.