arXiv research

The paper proposes a method to reliably select design algorithms for machine learning-guided design tasks.

Unified framework for combinatorial and rounding algorithms in experimental design.

Efficient deep learning computing requires algorithm and hardware co-design to enable specialization: we usually need to change the algorithm to reduce memory footprint and improve energy efficiency. However, the extra degree of freedom from the algorithm makes the design space much larger: it's not only about designin…

Unified algorithm for any $p$-norm experimental design problems.

Study evaluates reinforcement learning algorithms for sequential experimental design.

Designs algorithms to assist humans without affecting their decisions.

FTRL algorithm with negative entropy regularizer achieves best-of-three-world results for linear bandits.

We study the optimal design problems where the goal is to choose a set of linear measurements to obtain the most accurate estimate of an unknown vector in $d$ dimensions. We study the $A$-optimal design variant where the objective is to minimize the average variance of the error in the maximum likelihood estimate of th…

QCircuitBench provides a dataset for evaluating AI's ability to design quantum algorithms.

Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. In these domains illumination algorithms, such as MAP-Elites, are promising alternatives to classic optimization algorithms because they produce diverse, high-quality solutions in a single run,…

Solving the RNA inverse folding problem is a critical prerequisite to RNA design, an emerging field in bioengineering with a broad range of applications from reaction catalysis to cancer therapy. Although significant progress has been made in developing machine-based inverse RNA folding algorithms, current approaches s…

PSO improves $G$-optimal designs for up to 5 factors, reducing computation time.

New method designs multilayer nanoparticles using AI.

Estimates funding impact from an algorithmic relief rule, finding little effect on hospital activities.

Multi-layer optical film has been found to afford important applications in optical communication, optical absorbers, optical filters, etc. Different algorithms of multi-layer optical film design has been developed, as simplex method, colony algorithm, genetic algorithm. These algorithms rapidly promote the design and …

Enhances inverse design optimization with machine learning and reduced fidelity simulations.

BoGA combines evolutionary search with Bayesian optimization for efficient protein design.

This paper optimizes portfolios using HRP and CLA algorithms on NIFTY 50 stocks.

Do-AIQ framework evaluates AI algorithms' quality using DOE.

Design-by-Morphing creates radical airfoil designs without geometric constraints.

AR design reduces multi-armed bandit experiment cost.

Study uses machine learning to optimize seismic design parameters.

Gradient GA uses gradient information to improve molecular design.

Paper proposes an unbiased optimization method for Bayesian experimental design.

This paper analyzes OCBA algorithms' convergence rates for DEDS optimization.

Automated HPO design using Bayesian optimization and benchmarking.

This work improves algorithm design for structured Pfaffian settings.

NPOD algorithm improves efficiency in estimating pharmacokinetic parameters.

A neural network and evolutionary algorithm framework designs nonlinear optical molecules.

In this paper, we propose a new framework for designing fast parallel algorithms for fundamental statistical subset selection tasks that include feature selection and experimental design. Such tasks are known to be weakly submodular and are amenable to optimization via the standard greedy algorithm. Despite its desirab…

New method refines model predictions as design evolves.

AIDA designs personalized audio processing algorithms for hearing aids.

Optimizes pure exploration in linear bandits with a new algorithm.

A fast algorithm for counting Markov equivalent DAGs and designing experiments.

Algorithm optimizes system design and control for better rewards.

A new design methodology for neural networks that is guided by traditional algorithm design is presented. To prove our point, we present two heuristics and demonstrate an algorithmic technique for incorporating additional weights in their signal-flow graphs. We show that with training the performance of these networks …

This work introduces benchmarks for evaluating nanophotonic structures in design simulations.

Paper introduces IO-NPF for efficient Bayesian experimental design.

Algorithm learns optimal dynamic mechanisms from data.

Revisits PPO design choices, exposing failure modes and proposing alternatives.

In radar systems, unimodular (or constant-modulus) waveform design plays an important role in achieving better clutter/interference rejection, as well as a more accurate estimation of the target parameters. The design of such sequences has been studied widely in the last few decades, with most design algorithms requiri…

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach th…

New algorithm recovers sparse signals robustly against Gaussian noise and adaptive adversaries.

The goal of data-driven algorithm design is to obtain high-performing algorithms for specific application domains using machine learning and data. Across many fields in AI, science, and engineering, practitioners will often fix a family of parameterized algorithms and then optimize those parameters to obtain good perfo…

The experimental design problem concerns the selection of k points from a potentially large design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed on the selected k design points. Statistical efficiency is measured by optimality criteria, including A(verage), D(eterminant), T(race)…

New algorithm converts data into sub-gaussian designs efficiently.

Algorithm selects optimal experiments in Markov chains to learn unknown quantities.

Paper develops an efficient algorithm for experiment design using synthetic controls.