This work frames active inference through control as inference, offering robust control algorithms.
problem Active inference framework lacks practical sensorimotor control algorithms.
method Frame active inference through control as inference, presenting trajectory optimization as inference.
result AI may be framed as partially-observed CaI when the cost function is defined in observation states.
Derives time-averaged active inference from control principles.
problem Finite-horizon or discounted-surprise problems in active inference.
method Derives infinite-horizon, average-surprise active inference from optimal control principles.
result Unified objective functional for sensorimotor control.
New method infers causal effects without knowing control variables.
problem Inference errors when control variables are unknown.
method Proposes a method for inferring causal effects when control variables are unknown.
result Proves method yields asymptotically valid confidence intervals for average causal effects.
New method reduces inference variance for faster optimization.
problem High variance in black-box variational inference.
method Joint control variate addressing both data subsampling and Monte Carlo noise.
result Significantly reduced gradient variance, leading to faster optimization.
New comparison shows differences in how value is incorporated in AIF and CAI.
problem Clarifying the relationship between Active Inference and Control-as-Inference.
method Formal comparison of AIF and CAI frameworks.
result Primary difference is how value is incorporated into generative models.
Paper introduces RCaI, a risk-sensitive control method using Rényi divergence.
problem Risk-sensitive control in reinforcement learning.
method RCaI extends CaI using Rényi divergence variational inference.
result Risk-sensitive optimal policy can be obtained by solving a soft Bellman equation.
RNE provides a flexible framework for diffusion models, enabling inference-time control and energy-based training.
problem Insufficient knowledge of marginal densities in diffusion models.
method Introduces Radon-Nikodym Estimator (RNE) to reveal the connection between marginal densities and transition kernels.
result RNE delivers strong results in inference-time control and energy-based diffusion training.
CAD-DA controls anomaly detection under domain adaptation.
problem Valid statistical inference after domain adaptation.
method Conditional Selective Inference to handle domain adaptation effects.
result Valid statistical inference under domain adaptation achieved.
Optimal control of stochastic nonlinear dynamical systems is a major challenge in the domain of robot learning. Given the intractability of the global control problem, state-of-the-art algorithms focus on approximate sequential optimization techniques, that heavily rely on heuristics for regularization in order to achi…
EHR-MPC optimizes sepsis treatment using digital twins and inference-time control.
problem Optimal sepsis treatment policies are contested and difficult to adapt during inference.
method EHR-MPC decouples learning patient dynamics from treatment optimization, enabling inference-time control over learned digital twins.
result EHR-MPC achieves comparable off-policy performance and improved simulation performance compared to RL baselines.
Variational inference is increasingly being addressed with stochastic optimization. In this setting, the gradient's variance plays a crucial role in the optimization procedure, since high variance gradients lead to poor convergence. A popular approach used to reduce gradient's variance involves the use of control varia…
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
This work improves variational inference by reducing gradient variance.
problem Hard optimization of flexible variational distributions.
method Control variate based on quadratic approximation of the model's mean and covariance.
result Significant improvement in gradient variance and optimization convergence.
Agents learn and control complex mechanical systems through shared memories.
problem Controlling multi-joint dynamical systems.
method Coupled autoregressive active inference agents using Bayesian filtering and minimizing expected free energy.
result Demonstrated learning and control of a double mass-spring-damper system.
Controller-Augmented Hidden Markov Models (CHMMs) are a framework for constrained sequential inference.
problem Hidden Markov models fail under pathwise constraints like precedence, visitation, or monotonic state progression.
method CHMMs compile constraints into finite-state controllers, then use standard forward-backward and Viterbi recursions to compute exact constrained posteriors and paths.
result CHMMs provide exact constrained inference, monotone ascent in constrained EM, and linear complexity in controller cardinality.
Existing model-based reinforcement learning methods often study perception modeling and decision making separately. We introduce joint Perception and Control as Inference (PCI), a general framework to combine perception and control for partially observable environments through Bayesian inference. Based on the fact that…
High-dimensional models can outperform simpler ones in causal inference.
problem Estimating average treatment effects with many covariates.
method High-dimensional linear regression and synthetic control with many control units.
result Adding more control units can improve imputation performance even when pre-treatment fit is perfect.
Selective inference framework for CART trees to control error rates and coverage.
problem Inference on CART trees does not control Type 1 error rates and coverage.
method Selective inference framework conditioning on tree estimation, efficient algorithms.
result Proposes tests and intervals for CART trees with selective error control.
Unified reinforcement learning methods using hybrid inference.
problem Combining model-based and model-free reinforcement learning approaches.
method Control as Hybrid Inference (CHI) framework.
result CHI algorithm balances model-based and model-free learning.
New framework for Bayesian inference using neural Schrödinger-Föllmer flows.
problem Approximate Bayesian inference in large datasets.
method Stochastic control, Schrödinger bridges, SDE-based models.
result Advocates stochastic control as a finite time and low variance alternative to SGLD.
Framework for estimating treatment effects using external control data.
problem Improving efficiency in estimating average treatment effects (ATE) in hybrid trials.
method Developed a formal causal inference framework based on exchangeability assumptions and graphical criteria. Proposed estimators and efficient doubly-robust methods.
result Established finite-sample performance and demonstrated application to spinal muscular atrophy trial.
The framework of reinforcement learning or optimal control provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables effective reasoning about uncertainty, the connection between reinforcement learni…
DriftLite improves inference quality of diffusion models without retraining.
problem Adapting pre-trained diffusion models to new target distributions without retraining.
method Lightweight, training-free particle-based approach that steers inference dynamics with optimal stability control.
result Consistently reduces variance and improves sample quality over existing methods.
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.
New method uses differential equations for better counterfactual analysis.
problem Estimating counterfactual outcomes for policy analysis.
method Continuous-time approach to synthetic controls using controlled differential equations.
result Improves counterfactual estimation for irregularly aligned multivariate time series.
MFRL-BI controls manufacturing processes without needing accurate models.
problem Model inaccuracies in complex manufacturing systems.
method Model-free reinforcement learning with Bayesian inference.
result Demonstrated to perform well in a nonlinear CMP process.
Recent studies have shown that information disclosed on social network sites (such as Facebook) can be used to predict personal characteristics with surprisingly high accuracy. In this paper we examine a method to give online users transparency into why certain inferences are made about them by statistical models, and …
A new pricing controller handles resource constraints to infer target prices effectively.
problem Resource constraints prevent fixed-price inference, leading to support exclusion.
method Formalizes support-exclusion failure, designs a target-aware controller, and uses a realized information clock.
result The controller can certify feasible target bands and log continuous local densities, leading to polynomial rates of inference.
Unified approach to path planning using probabilistic inference on factor graphs.
problem Path planning problems using probabilistic inference.
method Unified framework using probabilistic factor graphs and message composition rules.
result Unified approach includes various algorithms like Sum-product, Max-product, Dynamic programming, and mixed criteria.
The paper controls false discovery rate in link prediction using conformal inference.
problem Identifying true edges in a graph while controlling false discoveries.
method Proposes a novel method based on conformal inference to control false discovery rate (FDR) in link prediction.
result Empirically demonstrates FDR control for both simulated and real data.
This work proposes using zero-variance control variates to reduce variance in pathwise gradient estimators for variational inference.
problem Pathwise gradient estimators in variational inference have high variance, leading to inefficient optimization.
method Apply zero-variance control variates to pathwise gradient estimators.
result Zero-variance control variates can significantly reduce the variance of pathwise gradient estimators without requiring complex assumptions.
Machine learning boosts RCT efficiency by controlling type I error and improving statistical power.
problem Improving statistical efficiency in RCTs with complex covariate adjustments.
method Machine learning-assisted adjustment under Rosenbaum's framework for exact tests.
result The proposed method robustly controls type I error and significantly boosts statistical efficiency.
We present a representation learning algorithm that learns a low-dimensional latent dynamical system from high-dimensional \textit{sequential} raw data, e.g., video. The framework builds upon recent advances in amortized inference methods that use both an inference network and a refinement procedure to output samples f…
We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic optimal control problem …
Risk control improves EENNs to make faster predictions without sacrificing accuracy.
problem Determining safe times for EENNs to exit early without degrading performance.
method Adapting risk control frameworks to EENNs to tune their exiting mechanism.
result Risk control enables EENNs to make faster predictions while maintaining user-specified performance goals.
ACSSM models irregular time series with continuous dynamics.
problem Modeling irregular time series data.
method ACSSM uses a multi-marginal Doob's h-transform and variational inference with stochastic optimal control.
result ACSSM outperforms in tasks like classification, regression, interpolation, and extrapolation.
TASC improves synthetic control for time-series data with trends.
problem Inability of existing SC methods to fully utilize temporal structure in time-series data.
method TASC uses a state-space model with a constant trend and Kalman filter for counterfactual inference.
result TASC offers advantages in settings with strong temporal trends and high observation noise.
Spatially relaxed inference tackles high-dimensional linear models with correlated covariates.
problem Accurate inference is challenging in high-dimensional settings with spatially correlated covariates.
method Proposes ensembled clustered inference algorithms that control the δ-FWER under standard assumptions. result Ensembled clustered inference algorithms control the δ-FWER and achieve decent power. NICE learns a representation to avoid bad controls in causal inference.
problem Avoiding bad controls in causal inference from observational data.
method Uses invariant risk minimization (IRM) to learn a representation of covariates that avoids bad controls.
result NICE outperforms adjusting for all covariates in cases with unknown collider variables and bad controls.
Automates infectious disease policy-making via inference in epidemiological models.
problem Improving policy-making for infectious diseases during pandemics.
method Performing inference in existing epidemiological models using a probabilistic programming language.
result Automated inference leads to better disease progression outcomes and policy prescriptions.
Algorithm reduces variance in causal effect estimation from multiple datasets.
problem Unidentifiable average treatment effect in observational data due to selection bias.
method Constructs control variates using datasets where ATE is not identifiable to reduce variance.
result Significant reduction in variance of ATE estimate using control variates.
Estimates treatment effects in time series data with always-missing controls.
problem Lack of control group in time series data, especially during specific events.
method Recover control group in event period, account for confounders and temporal dependencies.
result Robust estimation of control group's potential outcome and accurate predicted holiday effect.
This paper develops scalable control variates for Monte Carlo methods using stochastic optimization.
problem Reducing variance in Monte Carlo estimators for large-scale problems.
method Control variates based on Stein operators, optimized through stochastic optimization.
result Novel theoretical results and empirical validations show effective variance reduction.
The paper reclassifies RL algorithms using inference concepts.
problem To provide a unified perspective on RL algorithms.
method Using the control as inference framework, the paper classifies RL algorithms as amortised and iterative inference.
result A wide range of RL algorithms can be classified in this new manner.
CausalMix generates synthetic data with causal controls for mixed-type tables.
problem Synthetic data for causal inference with mixed-type and multimodal tabular data.
method CausalMix combines Gaussian latent priors with data-type-specific decoders for control over overlap, confounding, and treatment effect heterogeneity.
result CausalMix achieves state-of-the-art distributional metrics and stable causal control.
A method for logistic regression inference using both internal and external data.
problem Inability to estimate intercept and marginal case proportion in case-control logistic regression.
method Empirical likelihood approach integrating internal and external data.
result Intercept parameter becomes identifiable with external information, and all parameters are estimable consistently.
New approach uses negative controls to estimate causal parameters without completeness conditions.
problem Estimating causal parameters when not all confounders are observed.
method Identification strategy based on minimax learning formulations for general function classes.
result Avoids completeness conditions and uniqueness assumptions on bridge functions.
Autonomous agents can learn by imitating teacher demonstrations of the intended behavior. Hierarchical control policies are ubiquitously useful for such learning, having the potential to break down structured tasks into simpler sub-tasks, thereby improving data efficiency and generalization. In this paper, we propose a…