New method uses diffusion models for Bayesian inverse problems.
problem Solving Bayesian inverse problems with linear-Gaussian models.
method Decoupled Diffusion Sequential Monte Carlo (DDSMC) method.
result Asymptotically exact solution demonstrated on various data types.
New method reduces memory usage for Bayesian inverse problems on large grids.
problem Solving large-scale linear inverse problems with Gaussian process priors.
method Implicit representation of posterior covariance matrices, sequential disintegrations of Gaussian measures.
result Significant reduction in uncertainty for high-density regions estimation.
This study proposes an efficient surrogate for Darcy flow inverse problems.
problem Efficiently constructing accurate surrogate models for high-dimensional complex inverse problems.
method Sequential Bayesian design strategy to acquire a locally accurate surrogate model focusing on high-probability regions.
result The proposed method accelerates inversion accuracy and computational speed.
Paper develops a framework for learning interpretable representations of sequential decision behavior.
problem Obtaining a transparent description of existing behavior.
method Inverse decision modeling framework, formalizing both forward and inverse problems.
result Learning interpretable representations of behavior, including suboptimal actions, biased beliefs, and imperfect knowledge.
The paper presents a method to estimate subsurface fracture networks using geophysical and flow data.
problem Complexity and uncertainties in subsurface parameters estimation.
method Sequential inversion framework integrating geophysical and flow data.
result The method successfully estimates fracture orientations and lengths.
MCGDiff uses SGM to guide SMC for solving ill-posed linear inverse problems.
problem Solving ill-posed linear inverse problems in Bayesian settings.
method Exploiting SGM structure, defining a sequence of intermediate problems, and using SMC methods.
result MCGDiff outperforms competing methods in Bayesian ill-posed inverse problems.
New neural networks improve inference in graphical models.
problem Efficient inference in complex graphical models.
method Learning neural networks to approximate stochastic inverses as proposal distributions.
result Automatically learned neural networks accelerate sequential Monte Carlo.
Gradient-free framework for Bayesian experimental design in complex systems.
problem Optimal experimental design in systems where gradient information is unavailable.
method Combines EKI and ALDI for optimization and sampling, with approximations for scalable utility estimation.
result Demonstrates robust, accurate, and efficient experimental design in various complex systems.
Bayesian method for estimating inputs leading to specific probability outputs.
problem Estimating inputs for specific probability outputs of uncertain functions.
method Bayesian strategy using Gaussian process modeling and SUR principle.
result Surpassed performance of existing methods through numerical experiments.
Machine teaches IRL with minimal demonstrations.
problem Finding the minimum set of demonstrations for IRL.
method Formalized as a machine teaching problem, reduced to set cover, approximated efficiently.
result Efficient algorithm for determining maximally informative demonstrations.
Study designs for estimating treatment effects in adaptive experiments.
problem Estimating treatment effects under adaptive treatment assignment.
method Propose and analyze IPW and AIPW estimators, establish CLTs under design stability.
result Central limit theorems for IPW and AIPW estimators under design stability.
The paper addresses estimating long-term treatment effects with monotone missing data.
problem Estimating long-term treatment effects with missing data, especially monotone missing.
method The paper introduces the sequential missingness assumption for identification and proposes three novel estimation methods: inverse probability weighting, sequential regression imputation, and SeqMSM. It also introduces a balancing-enhanced approach, BalanceNet, to improve estimation accuracy.
result The proposed methods, including BalanceNet, effectively estimate long-term treatment effects with monotone missing data.
Framework for inverting complex software simulators using probabilistic programming.
problem Inverting complex software simulators is difficult and requires custom algorithms.
method Formulation of inversion as approximate inference in a simple probabilistic model; four inference strategies implemented.
result Framework implemented in under 20 lines of probabilistic code.
Proposes a real-time anomaly detection system using IRL.
problem Real-time anomaly detection in sequential data.
method Uses inverse reinforcement learning to infer reward function and evaluate anomalies.
result Effective in identifying anomalies in real-world data.
Bayesian method refines surrogate models for accurate full waveform inversion.
problem Complex input/output relations in full waveform inversion make accurate surrogate models difficult.
method Iterative refinement of surrogate models using MCMC samples and progressively expanding frequency bandwidth.
result Highly accurate surrogate model across full bandwidth enables accurate final MCMC inversion.
New method disentangles perceptual uncertainty and behavioral costs in partially observable systems.
problem Tackles inverse optimal control for non-linear partially observable systems.
method Probabilistic approach using maximum causal entropy formulations and local linearization.
result Disentangles perceptual factors and behavioral costs in sequential decision-making.
SKT improves EKI for Bayesian inverse problems with non-Gaussian targets.
problem Efficiently solving Bayesian inverse problems with expensive forward models and non-Gaussian posterior distributions.
method Embedding EKI and FAKI within a Bayesian annealing scheme to adapt tpCN sampler.
result Significant improvements in convergence rate compared to standard SMC and pCN.
Bayesian approach improves rain field reconstruction using CMLs and DMs.
problem Challenges in accurately reconstructing ground-level rainfall from CML path-integrated measurements.
method Bayesian inverse problem with Diffusion Models as priors.
result Improved performance in rainfall estimation compared to existing methods.
New diagnostic method detects misspecified models in inverse PDE problems.
problem Misleading residual-norm diagnostics in inverse PDE problems.
method Structure-sensitive sequential diagnostic using e-processes.
result Rejects fitted models that produce biased predictions.
Improved inference efficiency for complex simulations.
problem Challenges in performing inference under resource-intensive stochastic simulators.
method Active sequential neural posterior estimation (ASNPE) integrating active learning into posterior estimation.
result Improved sample efficiency with low computational overhead.
New approach transfers rewards learned in one environment to reinforcement learning in a new environment.
problem Transfer of rewards learned using inverse reinforcement learning from one environment to a new, different environment.
method Formulate the problem as a joint system of Bellman equations, develop minimax estimators for the target soft-q-function, solve the source and target system of equations jointly. result The coupled approach removes the first-order influence of source Bellman residual error compared to the sequential approach.
Review of diffusion priors for solving imaging inverse problems.
problem Solving inverse problems in imaging using diffusion priors.
method Categorizes approaches into explicit approximation and variational inference, sequential monte carlo, and decoupled data consistency.
result Systematic comparison of performance trade-offs across inverse problems.
The paper proposes an efficient method for estimating ATEs using adaptive experiments.
problem Estimating average treatment effects (ATEs) with minimal sample size and high accuracy.
method The paper defines and uses the efficient treatment-assignment probability to sequentially assign treatments, estimating ATEs using an Adaptive Augmented Inverse Probability Weighting (A2IPW) estimator.
result The proposed experimental design and A2IPW estimator achieve the minimized semiparametric efficiency bound and provide anytime valid confidence intervals for early stopping.
SigGPDE scales sparse Gaussian processes for sequential data.
problem Predicting and quantifying uncertainty in sequential data.
method Sparse variational inference framework for Gaussian Processes, leveraging GP signature kernel gradients as PDE solutions.
result Significant computational gains and state-of-the-art performance on large sequential datasets.
Paper introduces a new method for Gaussian Processes that improves prediction and hyper-parameter optimization.
problem Efficiently predicting unknown functions and optimizing hyper-parameters in Gaussian Processes.
method Sequential randomized low-rank matrix factorization for incremental predictions and hyper-parameter optimization.
result The proposed method outperforms existing approaches in terms of accuracy and computational efficiency.
Recently, a lot of effort has been paid to the efficient computation of Kriging predictors when observations are assimilated sequentially. In particular, Kriging update formulae enabling significant computational savings were derived in Barnes and Watson (1992), Gao et al. (1996), and Emery (2009). Taking advantage of …
Proposes DSW for unbiased ITE estimation with dynamic confounders.
problem Estimating ITE from dynamic observational data with time-varying confounders.
method Deep Sequential Weighting (DSW) infers hidden confounders using current treatment assignments and historical information.
result DSW generates unbiased and accurate treatment effects.
New neural network model reduces complexity of geological media sampling.
problem Efficient and high-fidelity sampling of complex binary geological media.
method Variational autoencoder-based deep neural network for low-dimensional base model parameterization.
result Our DR approach outperforms PCA, OPCA, and DCT in probabilistic inversion.
The paper develops a method to learn cost-optimal sequential testing policies from retrospective data.
problem Learning cost-optimal sequential decision policies from retrospective data with missing test results.
method Doubly robust Q-learning framework with path-specific inverse probability weights.
result The method reduces testing cost without compromising predictive accuracy.
TSNPE improves SBI efficiency and scalability.
problem Efficient and scalable simulation-based inference for complex models.
method Sequential inference with truncated proposals.
result TSNPE performs on par with previous methods and scales to complex models.
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.
AC-State discovers minimal latent state for control.
problem Discover minimal latent state from sensory information.
method Multi-step inverse model with information bottleneck.
result Guaranteed discovery of control-endogenous latent states.
Deep learning speeds up real-time emission monitoring.
problem Real-time greenhouse gas emission monitoring under transient conditions.
method Bayesian inference with deep learning surrogate of CFD outputs.
result Near-real-time predictions with orders-of-magnitude faster runtimes.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
problem Estimating excursion sets of vector-valued Gaussian processes.
method Clarifying the connection between continuous Gaussian processes and Gaussian measures in Banach spaces, extending concepts and properties from scalar-valued settings to vector-valued settings.
result Consistency results for sequential design strategies can be applied to vector-valued Gaussian processes.
New method for model inversion attacks on deep networks.
problem Extracting private information from trained deep learning models.
method Leveraging generative adversarial networks to find realistic class representations.
result Efficiency of model inversion attack within a connected lower-dimensional manifold.
The paper solves IRL for Bayesian stopping time problems.
problem Identifying optimal actions in Bayesian stopping time problems.
method Novel IRL framework using Bayesian revealed preferences.
result Identifies optimality and constructs cost function estimates.
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.
New approach uses inverse reinforcement learning to improve language model training.
problem Training large language models using imitation learning methods.
method Developed a new method of inverse reinforcement learning to optimize sequences directly.
result IRL-based fine-tuning leads to better performance and diversity in language generation.
The paper proposes a machine learning approach for production forecasting without model calibration.
problem Generating accurate production forecasts for reservoir development.
method Sequential model aggregation using machine learning algorithms without model calibration.
result The proposed method provides robust multi-step-ahead production forecasts.
PQR estimates reward functions from actions and states without assuming state-only rewards.
problem Estimating reward functions from actions and states without state-only assumptions.
method Deep learning approach that sequentially estimates policy, Q-function, and reward.
result PQR uniquely recovers true reward with known transitions and bounds error with unknown transitions.
Interactive teaching speeds up IRL learning with adaptive demonstrations.
problem Tackles the challenge of accelerating IRL learning with teacher assistance.
method Interactive teaching framework where a teacher adapts demonstrations based on the learner's policy.
result Teaching algorithms converge in the omniscient setting, speeding up learning.
New method uses diffusion models for inverse problems without approximations.
problem Solving complex inverse problems in high dimensions.
method Ensemble-based algorithm using diffusion models without approximations.
result Empirically validated method gives more accurate reconstructions.
OnAIR reconstructs dynamic images from sparse measurements online.
problem Reconstructing dynamic images from limited or corrupted measurements.
method Online adaptive reconstruction using sparsity and low-rank models with dictionary learning.
result Memory-efficient online algorithms for sequential estimation of dictionary and images.
AceIRL learns expert reward from active exploration.
problem Learning reward function from expert demonstrations in unknown environments.
method Active exploration to infer reward function and identify good policy.
result First approach to active IRL with sample-complexity bounds that doesn't require a generative model.
In their standard form Gaussian processes (GPs) provide a powerful non-parametric framework for regression and classificaton tasks. Their one limiting property is their O(N3) scaling where N is the number of training data points. In this paper we present a framework for GP training with sequential sele…
Causal deep learning tackles causal inference using tensor factor analysis.
problem Addressing causal questions in data using neural networks.
method Tensor factor analysis and neural network architectures (causal capsules, tensor transformer, multilinear projection algorithm).
result Derives deep neural networks for causal inference with tensor factor analysis.
DARTS optimizes covariate selection in trials with limited data.
problem Limited budget for high-dimensional pretreatment data.
method Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS).
result DARTS efficiently concentrates budget on informative features.
Paper develops a new method for solving IBVPs on star-shaped domains.
problem Solving Inverse Boundary Value Problems (IBVP) for parallel transport equations.
method Covariant tomography, integrating geometric decomposition with specific interior extensions.
result Formal solvability criterion for higher-order IBVPs, validated through examples.