arXiv research

Optimizes recommender selection online with D-optimal design.

Experimental design is a classical statistics problem and its aim is to estimate an unknown $m$-dimensional vector $β$ from linear measurements where a Gaussian noise is introduced in each measurement. For the combinatorial experimental design problem, the goal is to pick $k$ out of the given $n$ experiments so as to m…

A2D2E designs optimal evaluations for estimating main effects in black-box models.

New algorithm learns human preferences from few comparisons efficiently.

A new algorithm selects data subsets avoiding outliers and high leverage points.

Active learning framework for optimizing human preferences in reinforcement learning.

Algorithm reduces online regret by leveraging offline data in linear bandits.

Gonogo offers tools for sensitivity experiments in R.

Optimal sampling reduces power grid data analysis costs.

We demonstrate how to scalably solve a class of constrained self-concordant minimization problems using linear minimization oracles (LMO) over the constraint set. We prove that the number of LMO calls of our method is nearly the same as that of the Frank-Wolfe method in the L-smooth case. Specifically, our Newton Frank…

Enhanced PC$^2$ improves surrogate modeling for high-dimensional problems.

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…

In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e.g., to obtain labels/responses, for a linear regression task. Many statistical criteria have been proposed for choosing the optimal design, with popular choices including A…

ALMAB-DC optimizes expensive black-box experiments using active learning and distributed computing.

In this paper, we consider an online optimization process, where the objective functions are not convex (nor concave) but instead belong to a broad class of continuous submodular functions. We first propose a variant of the Frank-Wolfe algorithm that has access to the full gradient of the objective functions. We show t…

New active learning methods for Gaussian process improve predictive modeling of composite fuselage.

Online active learning improves model performance in high-volume production processes.

Dual active learning improves RLHF by selecting optimal conversations and teachers.

The paper introduces a framework to select efficient datasets for preserving model rankings.

Study optimizes sensor placement for accurate parameter estimation in complex systems.

A framework for faster, better infographic design by non-experts and experts alike.

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

Unified approach to experimental design using interlacing polynomials.

We propose a new low-cost machine-learning-based methodology which assists designers in reducing the gap between the problem and the solution in the design process. Our work applies reinforcement learning (RL) to find the optimal task-oriented design solution through the construction of the design action for each task.…

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

MO-PaDGAN generates diverse, high-performance designs with multiple metrics.

MCD automates counterfactual design searches for multi-modal tasks.

ANN with GA optimizes flexible disc design for lower mass and stress.

New method for mixed-variable GSA improves material design efficiency.

Deep learning models enhance engineering design automation.

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…

DAD learns to design experiments quickly, outperforming traditional methods.

Enhances scenario approach for certifying design properties post-design.

Deep generative models are proven to be a useful tool for automatic design synthesis and design space exploration. When applied in engineering design, existing generative models face three challenges: 1) generated designs lack diversity and do not cover all areas of the design space, 2) it is difficult to explicitly im…

MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.

Topology design optimization offers tremendous opportunity in design and manufacturing freedoms by designing and producing a part from the ground-up without a meaningful initial design as required by conventional shape design optimization approaches. Ideally, with adequate problem statements, to formulate and solve the…

Generative thermal design learns optimal shapes using multi-agent reinforcement learning.

Develops a prediction method based on sampling design.

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…

Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.

iDAD uses neural networks to quickly adapt experiments without likelihoods.

Unified framework for combinatorial and rounding algorithms in experimental design.

New method refines model predictions as design evolves.

A new method achieves optimal uniformity in designs with minimal flexibility.

RID-Noise improves robust design under noisy conditions using neural networks.

The design of multiple experiments is commonly undertaken via suboptimal strategies, such as batch (open-loop) design that omits feedback or greedy (myopic) design that does not account for future effects. This paper introduces new strategies for the optimal design of sequential experiments. First, we rigorously formul…

Generative systems have a significant potential to synthesize innovative design alternatives. Still, most of the common systems that have been adopted in design require the designer to explicitly define the specifications of the procedures and in some cases the design space. In contrast, a generative system could poten…

Dataset of 4500 bicycle designs aids in design analysis and synthesis.