New BED method handles online inference for partially observed dynamical systems.
problem Optimizing data collection for partially observable, partially online dynamical systems.
method Derived estimators of expected information gain and its gradient for SSMs, using nested particle filters.
result Successfully handles both partial observability and online inference in realistic models.
Develops control and observer methods for complex systems.
problem Controlling and observing infinite-dimensional systems with boundary actuation.
method Energy-Casimir method and port-Hamiltonian system representation.
result Control law and observer designed for Kirchhoff-Love plate example.
Adaptive calibration improves model accuracy with fewer simulations.
problem Inefficient calibration of complex models using fixed designs.
method Bayesian adaptive experimental design to optimize simulation runs.
result The method achieves better parameter estimation with fewer simulations.
The paper optimizes designs for distinguishing between Gaussian process models.
problem Discriminating between two Gaussian process models.
method Sequential and static design criteria, including Kullback Leibler divergences and log-likelihood ratios.
result Necessary conditions for optimal design measures are provided.
New method uses observational data to improve trial design efficiency.
problem Scarce randomized controlled trials; inefficiency of using observational data.
method Active Residual Learning, R-Design framework, R-EPIG criterion.
result Efficiently estimating residuals to correct observational bias improves trial design.
Efficient method for constrained optimization under partial observations with provable convergence.
problem Optimizing under partial and constrained data.
method Improved acquisition functions and Gaussian process embedding for partially observable constraints.
result Empirically validated method outperforms traditional approaches.
Bayesian sOED uses PG reinforcement learning for efficient experiment design.
problem Optimizing sequential experiments for nonlinear models with limited data.
method Formulated as POMDP, solved via PG methods with neural network parameterization.
result Demonstrated advantages over batch and greedy designs in contaminant source inversion.
Modern ML methods show unexpected behaviors that contradict classical statistics.
problem Modern machine learning methods exhibit behaviors at odds with classical statistical intuitions.
method Comparison between fixed and random design settings in ML and statistics.
result Moving from fixed to random designs reveals new insights into bias-variance tradeoffs and overfitting.
Paper designs energy-based controllers and observers for complex systems.
problem Controlling and observing infinite-dimensional systems with in-domain actuation.
method Uses Stokes-Dirac structures and jet-bundle structures to derive controllers and observers.
result Control schemes derived in both frameworks are equivalent.
The spectral energy distribution (SED) is a relatively easy way for astronomers to distinguish between different astronomical objects such as galaxies, black holes, and stellar objects. By comparing the observations from a source at different frequencies with template models, astronomers are able to infer the type of t…
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.
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mech…
Complex autonomous control systems are subjected to sensor failures, cyber-attacks, sensor noise, communication channel failures, etc. that introduce errors in the measurements. The corrupted information, if used for making decisions, can lead to degraded performance. We develop a framework for using adversarial deep r…
Predictive models that generalize well under distributional shift are often desirable and sometimes crucial to building robust and reliable machine learning applications. We focus on distributional shift that arises in causal inference from observational data and in unsupervised domain adaptation. We pose both of these…
Bayesian optimization reduces materials design costs by 10x.
problem Expensive materials design search space with mixed variables.
method Uncertainty-aware machine learning models for mixed numerical and categorical variables.
result Frequentist and Bayesian models perform differently in mixed-variable BO.
Paper develops an efficient algorithm for experiment design using synthetic controls.
problem Designing optimal experiments for treatment effect estimation.
method Solves a phase synchronization problem via a normalized generalized power method.
result First global optimality guarantee for experiment design with pre-treatment data.
Adaptive IP approach optimizes intervention design for causal graph recovery.
problem Designing efficient interventions to recover causal relationships from data.
method Iterative integer programming approach for optimizing information gain.
result Adaptive IP approach achieves full causal graph recovery with fewer interventions.
Several new estimation methods have been recently proposed for the linear regression model with observation error in the design. Different assumptions on the data generating process have motivated different estimators and analysis. In particular, the literature considered (1) observation errors in the design uniformly …
Design interventions to learn causal structure from observational data.
problem Identify causal structure from observational data, especially when direction of edges is unknown.
method Design non-adaptive interventions to maximize the number of identifiable edges, formulate as optimization problems.
result Proposed algorithms can efficiently identify causal structure from interventions, even for general causal structures.
Study learns linear system dynamics from noisy bilinear data.
problem Learning linear dynamics from bilinear observations with process and measurement noise.
method Regression with Kronecker product design, data-dependent and independent error bounds.
result Upper bounds on statistical error rates and sample complexity for learning dynamics matrices.
Computer model calibration typically operates by choosing parameter values in a computer model so that the model output faithfully predicts reality. By using performance targets in place of observed data, we show that calibration techniques can be repurposed to wed engineering and material design, two processes that ar…
Gaussian process (GP) models have become a well-established frameworkfor the adaptive design of costly experiments, and notably of computerexperiments. GP-based sequential designs have been found practicallyefficient for various objectives, such as global optimization(estimating the global maximum or maximizer(s) of a …
New design detects confounders from treatment intent in ICU data.
problem Unobserved confounding in observational ICU data.
method Querying human experts to identify unobserved confounders.
result Demonstrates feasibility of detecting confounders from EHRs.
The paper analyzes the stability of an observer error in a vibrating string system.
problem Stability analysis of observer error in a vibrating string system.
method Abstract Cauchy problem reformulation and application of LaSalle's invariance principle for infinite-dimensional systems.
result The observer error is asymptotically stable.
A method uses CG to create efficient channels for ideal observers.
problem Computational intractability of ideal observers for high-dimensional image data.
method Conjugate gradient (CG) method for constructing efficient channels.
result CG-based channels approximate IO and HO performance efficiently.
New findings show some designs can't be robustly recovered in linear regression.
problem Robust linear regression with adversarial corruption.
method Investigated well-spreadness of design matrices for robust recovery.
result Certified well-spreadness of random matrices efficiently, but hardness for small observations.
PIED optimizes experimental design for inverse problems using physics-informed neural networks.
problem Optimizing experimental design for inverse problems with limited budget and constraints.
method PIED uses physics-informed neural networks (PINNs) for continuous optimization of design parameters in one-shot deployments.
result PIED significantly outperforms existing ED methods in solving inverse problems, including unknown functions.
The field of precision medicine aims to tailor treatment based on patient-specific factors in a reproducible way. To this end, estimating an optimal individualized treatment regime (ITR) that recommends treatment decisions based on patient characteristics to maximize the mean of a pre-specified outcome is of particular…
Flow models recover causal transformations from observational data and a valid ordering.
problem Causal inference with only observational data and a valid causal ordering.
method Flow models that can recover component-wise, invertible transformations of exogenous variables.
result Flow models outperform previous methods and deliver consistent performance across various structural causal models.
This study shows how DDPM can be represented by the OU process.
problem Designing optimal noise schedules for DDPM.
method Formal equivalence between DDPM and OU process, heuristic designs based on Fisher Information.
result Fisher-Information-motivated schedule corresponds to cosine noise schedule.
PoPPy is a Point Process toolbox based on PyTorch, which achieves flexible designing and efficient learning of point process models. It can be used for interpretable sequential data modeling and analysis, e.g., Granger causality analysis of multi-variate point processes, point process-based simulation and prediction of…
In this paper, we consider the classic measurement error regression scenario in which our independent, or design, variables are observed with several sources of additive noise. We will show that our motivating example's replicated measurements on both the design and dependent variables may be leveraged to enhance a spa…
New method refines model predictions as design evolves.
problem Designing objects with desired properties using data-driven methods.
method Formalized as a game, developed autofocusing strategy for model retraining.
result Autofocusing improves model predictions in design space.
Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training environment. We propose structuring this process as a series of queries asking the user to compare between different reward functions. Thus…
A new bandit algorithm observes arm rewards before playing, reducing regret.
problem Balancing exploration and exploitation with pre-observation costs.
method Design of OBP-UCB for single-player and C-MP-OBP for multi-player settings.
result Proved regret bounds for both single-player and multi-player settings.
Efficient algorithms learn causal graphs with minimal interventions.
problem Learning causal relationships between observed variables in the presence of latents.
method Bi-criteria approximation goal combining intervention design and graph property testing.
result Achieve intervention cost within a small constant factor of the optimal.
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…
ESS-Flow guides flow models without retraining, using Bayesian inference in source space.
problem Training flow models on paired data for conditional generation or sample production.
method Gradient-free Bayesian inference in source space using Elliptical Slice Sampling.
result Effective in diverse tasks including material design and protein structure prediction.
Matrix completion is a classical problem in data science wherein one attempts to reconstruct a low-rank matrix while only observing some subset of the entries. Previous authors have phrased this problem as a nuclear norm minimization problem. Almost all previous work assumes no explicit structure of the matrix and uses…
Optimizes intervention design for causal discovery using integer programming.
problem Identifying causal structures from observational data due to confounding variables.
method Uses integer programming to design minimal intervention sets for causal structure identifiability.
result Provides exact and modular solutions adaptable to various experimental settings and constraints.
Novel radar waveform design for autonomous vehicles.
problem Efficient radar waveform design in time-varying environments.
method Hybrid model-driven and data-driven architecture.
result Adaptive unimodular waveform design for real-time scenarios.
Bayesian optimal design of experiments (BODE) has been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE method…
Overfitting occurs when RL agents correlate rewards with spurious observation features.
problem Overfitting in reinforcement learning due to correlation with spurious observation features.
method Developed a framework to analyze and design synthetic benchmarks from modified observation spaces.
result Agents can overfit to different observation spaces even if the MDP dynamics are fixed.
A new framework designs experiments for better decision-making.
problem Suboptimal experimental designs for downstream decision-making.
method Amortized decision-aware Bayesian Experimental Design (BED) with Transformer Neural Decision Process (TNDP).
result TNDP effectively designs experiments and facilitates accurate decision-making.
New method identifies how platforms can influence consumer behavior.
problem Estimating the causal effect of digital platforms on consumption.
method General causal inference problem, focusing on observational designs, and explicitly modeling consumption dynamics.
result Exogenous variation in consumption and responsive algorithmic control actions are sufficient for identifying steerability of consumption.
In this paper, we propose a design of a model-free networked controller for a nonlinear plant whose mathematical model is unknown. In a networked control system, the controller and plant are located away from each other and exchange data over a network, which causes network delays that may fluctuate randomly due to net…
Estimates causal effect of managed care plans on NYC Medicaid spending.
problem Generalizing causal estimates to a target population not well-represented by randomized studies.
method Conditional cross-design synthesis estimators combining randomized and observational data.
result Estimates causal effect of managed care plans on health care spending.
We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adapt…