Improved algorithms for A-optimal design reduce measurement error.
problem Minimizing error in estimating an unknown vector with linear measurements.
method Proportional volume sampling to improve approximation algorithms.
result Improved bounds for A-optimal design in asymptotic regime.
We consider 1-qubit mixed quantum state estimation by adaptively updating measurements according to previously obtained outcomes and measurement settings. Updates are determined by the average-variance-optimality (A-optimality) criterion, known in the classical theory of experimental design and applied here to quantum …
Efficiently designs experiments without integrating posterior distributions.
problem Computational inefficiency in Bayesian experimental design for PDE-based models.
method Likelihood-free approach using ANN to approximate conditional expectation.
result Significant reduction in observation model evaluations.
Adaptive IHS improves sketching for large-scale data.
problem Efficiently modeling large-scale data with iterative Hessian sketch.
method Deterministic A-optimal subsampling for improved IHS.
result A-optimal IHS outperforms existing accelerated IHS methods.
We bridge statistical and worst-case approaches to experimental design for linear regression.
problem Designing efficient experiments for linear regression models with arbitrary responses.
method Propose a new experimental design framework for arbitrary response distributions, combining statistical and worst-case approaches.
result Develop efficient randomized design procedures achieving strong variance bounds for unbiased estimators using few responses.
This work introduces a new sampling method to approximate an optimal design problem in ridge regression.
problem Finding an optimal subset of predictors in ridge regression to minimize prediction error.
method Developed a λ λ λ -regularized proportional volume sampling algorithm with approximation guarantees. result The algorithm provides a ( 1 + ε 1 + λ ′ ) (1+\fracε{\sqrt{1+λ'}}) ( 1 + 1 + λ ′ ε ) -approximation for the optimal design problem. Optimal online sampling strategy for linear regression with varying variances.
problem Designing experiments to estimate a linear model with unknown covariate variances.
method Combining bandit and convex optimization techniques, proposing an active sampling algorithm.
result Achieved an \(\mathcal{O}(T^{-2})\) regret bound in ideal conditions.
Optimal sampling strategy improves prediction accuracy with surrogate variables under measurement constraints.
problem Measurement-constrained datasets and lack of labeled data.
method A-optimality criterion for optimal sampling, leveraging surrogate variables.
result Achieves lower asymptotic variance and reduced empirical mean squared error.
Study on computable online learning with new conditions and complexities.
problem Characterizing optimal online learning under varying optimality requirements.
method Introduced anytime optimal (a-optimal) online learning and explored computational separations.
result Found a computational separation between a-optimal and optimal online learning.
Unpaired deep learning reconstructs MRI images from accelerated data.
problem Difficulty in acquiring matched fully sampled k-space data for supervised deep learning.
method Optimal transport driven cycleGAN architecture.
result Reconstructs high resolution MR images from accelerated k-space data.
AMP algorithms can be efficiently simulated by SDPs even with corrupted data.
problem Optimizing average-case optimization problems with corrupted data.
method Local statistics hierarchy semidefinite programs (SDPs) simulate AMP algorithms robustly.
result Robust guarantees for many AMP algorithms are offered, contrasting with strong lower bounds for SDPs.
This work is motivated by numerical solutions to Hamilton-Jacobi-Bellman quasi-variational inequalities (HJBQVIs) associated with combined stochastic and impulse control problems. In particular, we consider (i) direct control, (ii) penalized, and (iii) semi-Lagrangian discretization schemes applied to the HJBQVI proble…
Study examines various non-linear activation functions for improving neural network performance on MNIST classification.
problem Improving neural network performance on MNIST classification tasks.
method Empirical analysis of non-linear activation functions, including depth and weight initialization effects.
result Optimal neural network architecture with best activation function and weight initialization yields impressive results.
Bayesian framework improves variance component estimation in MET data.
problem Inaccurate estimation of variance components in MET data.
method Proposes a Bayesian updating framework using historical data.
result Stabilizes variance component estimation and quantifies uncertainty.
A new method for automatically aligning and clustering time series data.
problem Challenges in aligning and clustering time series data, especially without a template signal.
method TROUT (Temporal Registration using Optimal Unitary Transformations) method based on a novel dissimilarity measure.
result TROUT outperforms competitors in clustering time series data.
Adaptive algorithm reduces regret in causal bandits.
problem Minimize regret in causal bandits with unknown d-separators.
method Adaptive algorithm exploiting d-separators without prior knowledge.
result Significantly smaller regret than previous methods.
The paper proves a new discrete Laplacian for 3D meshes and shows its superiority over primal construction.
problem Developing a more accurate discrete Laplacian for 3D meshes.
method Proves the Euler-Lagrange equation for the Dirichlet energy using the associated discrete Laplacian of the dual construction.
result The associated discrete Laplacian is optimal in R 3 \mathbb{R}^3 R 3 compared to the primal construction. Optimizes angular velocity transfers for rigid bodies under deadline constraints.
problem Stochastic guidance of spin states of rigid bodies over a hard deadline.
method Structural analysis of Kantorovich optimal coupling formulation for nonlinear dynamics.
result Derives the ground cost for optimal transport of angular velocity.
The paper solves a maximum entropy sampling problem with efficient algorithms and performance guarantees.
problem Selecting the most informative principal submatrix from a covariance matrix.
method Derive a novel convex integer program, develop efficient sampling algorithms with approximation bounds, and analyze local search algorithms.
result Efficient algorithms with near-optimal performance guarantees for solving MESP and A-MESP.
The ∂ ˉ J \bar{\partial}_{_{J}} ∂ ˉ J operator over an almost complex manifold induces canonical connections of type ( 0 , 1 ) (0,1) ( 0 , 1 ) over the bundles of ( p , 0 ) (p,0) ( p , 0 ) -forms. If the almost complex structure is integrable then the previous connections induce the canonical holomorphic structures of the bundles of ( p , 0 ) (p,0) ( p , 0 ) -forms. For p = 1 p=1 p = 1 we can …
New findings show that common optimization algorithms struggle with random problems.
problem Finding near-optimal solutions to random optimization problems.
method Low-degree polynomials, Boolean circuits, and Langevin dynamics.
result These algorithms fail to produce nearly optimal solutions with high probability.
Develops c-GNF for personalized social science policy analysis.
problem Challenges in estimating causal effects and counterfactual inference in social sciences.
method causal-Graphical Normalizing Flow (c-GNF) method.
result c-GNF performs well in estimating causal effects and counterfactual inference.
New algorithm reduces offline RL sample complexity for MDPs.
problem Learning optimal policies from offline data in unknown MDPs.
method Adaptive Pessimistic Value Iteration (APVI) algorithm.
result Suboptimality bound nearly matches theoretical limits.
A framework for faster, better infographic design by non-experts and experts alike.
problem Designing infographics is time-consuming and tedious for non-experts and even professionals.
method Semi-automated infographic framework for structured and flow-based designs, including automatic design ranking and customization options.
result Designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining quality.
A new RL method helps designers solve complex tasks.
problem Design process gap between problem and solution.
method Deep Reinforcement Learning (RL) for task-oriented design.
result Method achieves satisfactory design even with multiple goals.
The paper proposes a method to reliably select design algorithms for machine learning-guided design tasks.
problem Choosing the right design algorithm for machine learning-guided design tasks.
method Combining designs' predicted property values with held-out labeled data to reliably forecast characteristics of the label distributions produced by different design algorithms.
result The method is guaranteed to return design algorithms that yield successful label distributions.
Unified approach to experimental design using interlacing polynomials.
problem Experimental design problems, especially D/A/E-design and E-design.
method Unified deterministic approach using interlacing polynomials.
result Improved approximation guarantees for various experimental design objectives.
Design-by-Morphing creates radical airfoil designs without geometric constraints.
problem Design constraints limit airfoil design novelty and small changes.
method Design-by-Morphing (DbM) creates a search space without geometric constraints.
result DbM generates radical airfoils with remarkable lift-over-drag ratio and stall angle tolerance.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
MCD automates counterfactual design searches for multi-modal tasks.
problem Designing for multi-objective goals and complex constraints.
method Model-agnostic counterfactual search method for multi-modal design modifications.
result MCD streamlines and automates counterfactual search, recommending effective design modifications.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.
New approach designs optimal structures using GANs and CNNs.
problem Topology design optimization requires many iterations and is impractical for real-world applications.
method Integrates Generative Adversarial Networks (GANs) and convolutional neural networks for topology design.
result Optimal structures generated effectively and rapidly.
ANN with GA optimizes flexible disc design for lower mass and stress.
problem Design flexible disc elements for lower mass and stress without compromising torque transmission and misalignment.
method Artificial Neural Network (ANN) coupled with Genetic Algorithm (GA) for design exploration.
result Optimized designs meet specified criteria with minimum mass and stress.
DeepCloud uses machine learning to generate design alternatives without explicit designer input.
problem Designing with explicit specifications limits innovation potential.
method Developed a data-driven generative system combining autoencoder for point clouds and web-based interface.
result DeepCloud learns design alternatives from existing solutions without designer input.
New method for mixed-variable GSA improves material design efficiency.
problem Designing materials with both quantitative and qualitative variables.
method Integrates LVGP with Sobol' analysis for mixed-variable GSA.
result Accelerates exploration of novel MOF candidates in combinatorial design spaces.
Deep learning models enhance engineering design automation.
problem Design optimization and customization across various industries.
method Leveraging deep neural networks, GANs, VAEs, and DRL for design synthesis.
result Recent advances in DGMs show promise in structural optimization, materials design, and shape synthesis.
Design automation optimizes deep learning models for various hardware.
problem Designing efficient deep learning models requires balancing algorithm and hardware.
method Proposes design automation techniques for specialized neural networks, including auto pruning and quantization.
result Learning-based automation achieves superior performance and efficiency compared to human design.
SentRNA improves RNA design by integrating human strategies.
problem Designing sequences for large or complex RNA targets.
method SentRNA uses a neural network trained on human-designed RNA sequences.
result SentRNA solves complex targets previously unsolvable by machines.
Enhances scenario approach for certifying design properties post-design.
problem Certifying additional useful properties in designs not considered during the design phase.
method Two-level framework of appropriateness: baseline and post-design. Distribution-free upper bounds on risk derived.
result Distribution-free upper bounds on the risk of failing to meet post-design appropriateness.
DAD learns to design experiments quickly, outperforming traditional methods.
problem Real-time decision-making in sequential Bayesian experimental design.
method Amortized design network trained with contrastive information bounds.
result DAD outperforms alternative strategies on various problems.
MO-PaDGAN improves multi-objective optimization by generating diverse and high-performing designs.
problem Challenges in parameterizing engineering designs for multi-objective optimization.
method MO-PaDGAN uses a generative adversarial network with a Determinantal Point Processes loss function to address these challenges.
result MO-PaDGAN generates designs with improved performance and coverage, even surpassing training data.
This paper introduces new strategies for optimal design of sequential experiments.
problem Designing optimal experiments in a sequential manner.
method Formulated as a dynamic program, developed numerical approaches for Bayesian parameter inference.
result Demonstrated advantages over batch and greedy design methods.
Generative thermal design learns optimal shapes using multi-agent reinforcement learning.
problem Complex thermal design challenges due to convection-diffusion equation and boundary interactions.
method Cooperative multi-agent deep reinforcement learning with continuous geometric representation.
result Framework learns optimal design strategies without shape derivation or differentiable objectives.
Develops a prediction method based on sampling design.
problem Creating accurate individual predictions.
method Design-based approach using expected cross-validation results.
result Valid inference of unobserved prediction errors defined with respect to sampling design.
Paper proposes NASAIC framework for co-designing neural architectures and heterogeneous ASICs.
problem Designing efficient neural architectures and ASICs for multiple tasks.
method Build ASIC templates and propose NASAIC framework for simultaneous design of architectures and ASICs.
result NASAIC ensures design specifications and maximizes accuracy with minimal performance loss.
Adaptive designs achieve strong Neyman regret guarantees for ATE estimation.
problem Estimating unbiased average treatment effect in sequential experiments.
method Proposed adaptive designs with O ~ ( log T ) \widetilde{O}(\log T) O ( log T ) Neyman regret under boundedness assumptions and O ~ ( T ) \widetilde{O}(\sqrt{T}) O ( T ) multigroup Neyman regret in covariate-based settings. result Adaptive designs outperform non-adaptive designs in terms of Neyman regret, especially in covariate-based settings.
iDAD uses neural networks to quickly adapt experiments without likelihoods.
problem Performing adaptive experiments in real-time with implicit models.
method iDAD learns a design policy network upfront to make quick design decisions.
result iDAD can make design decisions in milliseconds, unlike traditional BOED approaches.
Develops efficient adaptive designs for longitudinal studies with multiple target estimands.
problem Improving estimation efficiency in adaptive clinical trials with multi-stage treatments.
method Semiparametric efficiency framework for backward-recursive optimal design criterion.
result Optimal designs for one stage can compromise efficiency for other stages, highlighting trade-offs.