Optimizes crowdsourced preference-based subjective evaluation with online learning.
problem Large-scale evaluation of generative media using crowdsourcing due to combinatorial explosion.
method Automatic optimization of pair combination selections and evaluation volumes with online learning.
result Optimizes evaluation by reducing pair combinations and allocating optimal evaluation volumes.
The study addresses biases in evaluating molecular optimization methods and proposes methods to reduce these biases.
problem Biases in in silico evaluation of molecular optimization methods.
method Discussion and empirical investigation of bias reduction methods for predictor misspecification and sample reuse.
result Empirical investigation of bias reduction methods for predictor misspecification and sample reuse.
Presents SPEED, an algorithm for optimal policy evaluation in linear bandits with heteroscedastic noise.
problem Optimal data collection for policy evaluation in linear bandits with heteroscedastic reward noise.
method Formulated an optimal design for weighted least squares estimates, derived the optimal sample allocation, introduced SPEED algorithm, and derived regret bounds.
result SPEED leads to policy evaluation with MSE comparable to oracle strategy and significantly lower than random policy execution.
The performance of deep neural networks crucially depends on good hyperparameter configurations. Bayesian optimization is a powerful framework for optimizing the hyperparameters of DNNs. These methods need sufficient evaluation data to approximate and minimize the validation error function of hyperparameters. However, …
Optimizes function networks by selectively evaluating parts of the network, reducing costs.
problem Optimizing expensive function networks where full evaluations are costly.
method Knowledge gradient acquisition function for cost-aware partial evaluations.
result Outperforms existing BOFN methods and benchmarks across various problems.
A new method for faster multi-objective optimization by evaluating objectives separately.
problem Finding the Pareto front of trade-offs between multiple objectives efficiently.
method Knowledge Gradient with decoupled evaluations, accounting for different costs.
result The method significantly outperforms existing approaches in terms of evaluation cost.
Real-world optimization problems often have expensive objective functions in terms of cost and time. It is desirable to find near-optimal solutions with very few function evaluations. Surrogate-assisted optimizers tend to reduce the required number of function evaluations by replacing the real function with an efficien…
Accelerates Bayesian optimization of function networks with partial evaluations.
problem Optimizing expensive-to-evaluate function networks with varying node costs.
method Proposes an accelerated algorithm that uses global Monte Carlo simulations to select node-specific candidate inputs.
result Achieves up to a 16x speedup over the original p-KGFN algorithm while maintaining competitive query efficiency.
We propose a Bayesian optimization algorithm for objective functions that are sums or integrals of expensive-to-evaluate functions, allowing noisy evaluations. These objective functions arise in multi-task Bayesian optimization for tuning machine learning hyperparameters, optimization via simulation, and sequential des…
ECP optimizes expensive functions without knowing Lipschitz constant.
problem Optimizing expensive, non-convex functions with unknown Lipschitz constants.
method ECP minimizes evaluations by focusing on potentially optimal regions, eliminating Lipschitz constant estimation.
result Guaranteed no-regret performance and minimax-optimal regret bounds.
Parallel BO method for multi-objective optimization with constraints.
problem Optimizing multiple objectives under constraints with expensive evaluations.
method PPESMOC, a batch method for simultaneous optimization of black-box functions.
result Empirical evidence shows PPESMOC is effective for multi-objective optimization with constraints.
A new algorithm optimizes time-varying functions with non-constant evaluation times.
problem Optimizing functions that change over time with varying evaluation times.
method Proposes a novel time-varying Bayesian optimization algorithm.
result Establishes a regret bound for the proposed algorithm.
New method optimizes treatment policies to avoid winner's curse.
problem Winner's curse in treatment policy optimization.
method Inference-aware policy optimization.
result Optimizes for both estimated performance and downstream evaluation.
Paper addresses offline policy evaluation in RL, achieving near-optimal bounds for various policy classes.
problem Evaluate all policies in a class simultaneously for offline RL.
method Uniform convergence in OPE for various policy classes, achieving optimal episode complexity.
result Achieves optimal episode complexity of O(H^3/d_mε^2) for identifying ε-optimal policies.
Machine learning algorithms are very sensitive to the hyperparameters, and their evaluations are generally expensive. Users desperately need intelligent methods to quickly optimize hyperparameter settings according to known evaluation information, and thus reduce computational cost and promote optimization efficiency. …
Study optimal portfolio strategies with periodic evaluation under short-selling prohibition.
problem Optimal portfolio strategies with periodic evaluation under short-selling prohibition.
method Reformulate the original problem into an auxiliary one-period optimization problem and introduce dual control problem.
result Derive and verify the value function and optimal constrained portfolio for the original problem.
This paper optimizes portfolio management in incomplete markets with stochastic factors, considering periodic wealth evaluations.
problem Optimizing portfolio performance in an incomplete market model with stochastic factors and periodic wealth evaluations.
method Developed a martingale duality approach to find optimal portfolio processes and dual minimizers.
result Established the existence of optimal portfolio processes and identified dual minimizers as the 'least favorable' market completion.
Study compares DSPy teleprompter algorithms for aligning LLM evaluations with human annotations.
problem Aligning LLM evaluation metrics with human annotations.
method Comparative analysis of five teleprompter algorithms within the DSPy framework.
result Certain teleprompters outperform others in detecting hallucinations.
Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifi…
Cost-aware BO minimizes function evaluations with varying costs.
problem Optimization with varying evaluation costs in expensive functions.
method Cost Apportioned Bayesian Optimization (CArBO) combining initial and cost-cooled phases.
result CArBO finds better hyperparameter configurations with the same cost budget.
BOSH optimizes functions with stochastic evaluations more efficiently and precisely.
problem Optimizing functions with noisy evaluations can lead to suboptimal solutions.
method BOSH uses a hierarchical Gaussian process to generate a growing pool of realizations.
result BOSH provides more efficient and higher-precision optimization than standard BO.
New approach for prudent risk evaluation using model aggregation.
problem Risk evaluation and optimization under uncertainty.
method Model Aggregation (MA) approach based on stochastic dominance.
result Produces robust risk evaluation and distributional models.
In many applications of black-box optimization, one can evaluate multiple points simultaneously, e.g. when evaluating the performances of several different neural network architectures in a parallel computing environment. In this paper, we develop a novel batch Bayesian optimization algorithm --- the parallel knowledge…
DUET optimizes LLM training data mixtures using feedback from unseen tasks.
problem Optimizing LLM performance on unseen tasks without prior data knowledge.
method Interleaves influence function with Bayesian optimization for data selection.
result DUET converges to optimal training data mixture for unseen tasks.
New method optimizes costly evaluations in Bayesian optimization.
problem Costly evaluations in BO methods, especially in hyperparameter tuning.
method Formulated as a CMDP, developed rollout approximation for optimal policy.
result Improved cost efficiency over standard BO methods.
Certified algorithms optimize functions with varying costs, providing error bounds.
problem Optimizing functions with varying evaluation costs and error bounds.
method Formalized as a min-max game, proposed certified MFDOO algorithm with cost complexity bound.
result Proposed certified MFDOO algorithm has near-optimal cost complexity for Lipschitz functions.
A scalable portfolio approach speeds up Bayesian optimization for noisy functions.
problem Efficiently selecting multiple designs in parallel for noisy, expensive black-box optimization.
method A portfolio approach that balances exploration and exploitation, using a scalable allocation strategy.
result Significant speed improvements over existing methods, with similar or better performance.
SOO uses bandit theory to optimize functions with limited evaluations.
problem Optimizing functions with a limited number of evaluations.
method SOO is a machine learning approach that uses domain partitioning and a tradeoff between exploration and exploitation.
result SOO provides guarantees on the returned solution and is numerically efficient.
MESMOC optimizes constrained multi-objective problems efficiently.
problem Constrained multi-objective optimization with expensive function evaluations.
method Max-value Entropy Search in the output space.
result MESMOC selects high-quality Pareto solutions efficiently.
Speech enhancement model is used to map a noisy speech to a clean speech. In the training stage, an objective function is often adopted to optimize the model parameters. However, in most studies, there is an inconsistency between the model optimization criterion and the evaluation criterion on the enhanced speech. For …
Adaptive replication improves stochastic function optimization.
problem Challenges in accurately estimating functions with high variance.
method Trust-region-based Bayesian optimization with adaptive replication.
result Adaptive replication substantially improves solution accuracy and efficiency.
This paper describes Plumbing for Optimization with Asynchronous Parallelism (POAP) and the Python Surrogate Optimization Toolbox (pySOT). POAP is an event-driven framework for building and combining asynchronous optimization strategies, designed for global optimization of expensive functions where concurrent function …
Proposes a new PBO method with theoretical guarantees.
problem Optimizing expensive functions in parallel with noisy evaluations.
method Randomized Kriging Believer (KB) based on KB heuristic.
result Randomized KB achieves Bayesian expected regret guarantees.
Study evaluates new models using human feedback from another model.
problem Evaluate a new model using human feedback collected for another model.
method Formalize problem, propose model-based and model-free estimators, analyze unbiasedness, and empirically evaluate.
result Proposed estimators can predict absolute values, rank, and optimize evaluated policies.
FunBO uses LLMs to discover effective acquisition functions for Bayesian optimization.
problem Designing optimal acquisition functions for Bayesian optimization across diverse problems.
method FunBO leverages FunSearch, an LLM, to learn and evaluate new acquisition functions.
result FunBO discovers acquisition functions that generalize well and outperform existing methods.
This paper optimizes off-policy evaluation in reinforcement learning with function approximation.
problem Estimating cumulative value of a new policy from logged data generated by an unknown policy.
method Regression-based fitted Q iteration method, equivalent to estimating conditional mean embedding of transition operator.
result The method is minimax-optimal, with nearly minimal estimation error.
This paper provides lower bounds on the convergence rate of Derivative Free Optimization (DFO) with noisy function evaluations, exposing a fundamental and unavoidable gap between the performance of algorithms with access to gradients and those with access to only function evaluations. However, there are situations in w…
A new method for optimizing black-box problems with constraints.
problem Optimizing black-box systems with multiple performance criteria and constraints.
method Developed a novel constrained Bayesian optimization approach based on the knowledge gradient method.
result A new acquisition function that balances optimality and feasibility.
The optimization of expensive-to-evaluate black-box functions over combinatorial structures is an ubiquitous task in machine learning, engineering and the natural sciences. The combinatorial explosion of the search space and costly evaluations pose challenges for current techniques in discrete optimization and machine …
We propose practical extensions to Bayesian optimization for solving dynamic problems. We model dynamic objective functions using spatiotemporal Gaussian process priors which capture all the instances of the functions over time. Our extensions to Bayesian optimization use the information learnt from this model to guide…
Paper proposes an efficient bandit-based algorithm for hyperparameter optimization.
problem Efficiently evaluating hyperparameters in deep learning models with large search spaces.
method Sub-Sampling (SS) algorithm combined with Bayesian Optimization (BOSS).
result Theoretical proof of optimality and empirical validation of superior performance.
BIS uses bandits to efficiently sample from expensive-to-evaluate densities.
problem Sampling from computationally expensive target densities.
method Sequential selection through multi-armed bandits, optimizing sample set directly.
result BIS achieves accurate sampling with fewer evaluations than adaptive methods.
A tutorial on optimizing complex functions with partial knowledge.
problem Optimizing functions with limited or partial information.
method Grey-box Bayesian optimization, blending black-box and white-box approaches.
result Improves optimization performance by leveraging internal information.
This paper proposes automatic tuning of Bayesian Optimization's acquisition function.
problem Optimizing black-box functions with noisy, expensive evaluations and hyperparameter tuning.
method Exploring heuristics to automatically tune acquisition functions in Bayesian Optimization.
result Demonstrates effectiveness of heuristics in automatic Bayesian Optimization.
The design of machine learning systems often requires trading off different objectives, for example, prediction error and energy consumption for deep neural networks (DNNs). Typically, no single design performs well in all objectives; therefore, finding Pareto-optimal designs is of interest. The search for Pareto-optim…
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.
NAS helps find best neural network designs.
problem Designing optimal neural network architectures.
method Optimization algorithms and search spaces.
result Introduction to major advances in NAS for CNNs.
The paper proposes a technique to speed up evolutionary algorithms by using lower-cost approximations of the objective function.
problem Evolutionary algorithms require many evaluations to solve computationally expensive black-box optimization problems.
method The paper introduces a technique to choose an appropriate approximate function cost during the execution of the optimization algorithm.
result The proposed approach can reach the same objective value in less than half the time in certain cases.