New method corrects active learning for distribution shifts and outliers.
problem Conventional active learning methods fail to account for test-time distribution.
method JEPIG, a hybrid of BALD and EPIG, maximizes expected predictive information gain.
result JEPIG outperforms conventional methods in active learning with distribution shifts.
Proposes EPIG for active learning to improve predictive performance.
problem Suboptimal predictive performance of traditional active learning methods.
method Introduces EPIG, a new acquisition function measuring information gain in the space of predictions.
result EPIG leads to stronger predictive performance compared to BALD across various datasets and models.
We propose a novel information-theoretic approach for Bayesian optimization called Predictive Entropy Search (PES). At each iteration, PES selects the next evaluation point that maximizes the expected information gained with respect to the global maximum. PES codifies this intractable acquisition function in terms of t…
Active inference minimizes expected free energy for optimal behavior.
problem Understanding and optimizing behavior in complex systems.
method Combines Bayesian decision theory, optimal Bayesian design, and the free energy principle.
result Active inference emerges as a unified framework for information-seeking, utility maximization, and goal-directed behavior.
Active inference selects actions to maximize information gain, aiding structure learning.
problem Learning the structure of underlying world models.
method Active inference selects actions based on expected free energy, which includes information gain and value.
result Actions that maximize information gain help disambiguate among alternative models.
Expands Bayesian experiment design framework to account for model discrepancies.
problem Model misspecification in Bayesian optimal experiment design.
method Introduces Expected General Information Gain and Expected Discriminatory Information criteria.
result Demonstrates improved robustness and detection capabilities in experiment design.
Estimating arbitrary quantities of interest (QoIs) that are non-linear operators of complex, expensive-to-evaluate, black-box functions is a challenging problem due to missing domain knowledge and finite budgets. Bayesian optimal design of experiments (BODE) is a family of methods that identify an optimal design of exp…
New method improves robustness of Bayesian experimental design.
problem Bayesian experimental design's sensitivity to prior distribution changes.
method Introduces robust expected information gain (REIG) and uses KL-divergence ambiguity sets.
result REIG stabilizes sampling-based EIG estimation and compensates for prior variability.
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design wi…
C-IP improves LLMs' query selection for interactive tasks by estimating uncertainty robustly.
problem Minimizing the number of queries for interactive LLMs.
method Conformal Information Pursuit (C-IP) using conformal prediction sets.
result C-IP achieves better predictive performance and shorter query-answer chains.
Estimates expected information gain using density approximations and dimension reduction.
problem Estimating expected information gain in nonlinear and non-Gaussian settings.
method Flexible transport-based schemes for EIG estimation, optimal sample allocation, and gradient-based upper bounds on mutual information.
result Optimal sample allocation and dimension reduction schemes improve EIG estimation accuracy and convergence rate.
This paper treats prediction markets as Bayesian inverse problems to quantify uncertainty and identify event outcomes.
problem Uncertainty and identifiability in prediction market outcomes from price-volume histories.
method Formulates prediction markets as Bayesian inverse problems, introduces a log-odds observation model, and derives posterior uncertainty quantification and identifiability criteria.
result Explicit diagnostics for informative and stable inference regimes, and validation through synthetic data experiments.
Paper proposes an unbiased optimization method for Bayesian experimental design.
problem Maximizing expected information gain in Bayesian experimental design.
method Randomized multilevel Monte Carlo (MLMC) method combined with stochastic gradient descent.
result An unbiased estimator for the gradient of expected information gain.
We approximate differential entropy for efficient Bayesian experimental design.
problem Efficiently estimating expected information gain in large-scale inference problems.
method Approximate differential entropy using Monte Carlo or quasi-Monte Carlo surrogates.
result Our approach achieves comparable or better convergence rates than state-of-the-art methods.
Transformers for binary decisions are sensitive to evidence order, leading to unreliable outcomes.
problem Order sensitivity in Transformers for binary decisions leads to unreliable outcomes.
method Formalized an expectation-realization gap and developed QMV and EDFL bounds.
result Uniform permutation mixtures reduce dispersion and improve reliability.
Decision trees algorithms use a gain function to select the best split during the tree's induction. This function is crucial to obtain trees with high predictive accuracy. Some gain functions can suffer from a bias when it compares splits of different arities. Quinlan proposed a gain ratio in C4.5's information gain fu…
Experimental design is crucial for inference where limitations in the data collection procedure are present due to cost or other restrictions. Optimal experimental designs determine parameters that in some appropriate sense make the data the most informative possible. In a Bayesian setting this is translated to updatin…
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…
EAGLE improves reproducibility and stability of model explanations.
problem Creating reliable explanations for opaque machine learning models.
method Formulates perturbation selection as an information-theoretic active learning problem.
result EAGLE learns a linear surrogate model with feature importance scores and uncertainty estimates.
Information theory provides a mathematical foundation to measure uncertainty in belief. Belief is represented by a probability distribution that captures our understanding of an outcome's plausibility. Information measures based on Shannon's concept of entropy include realization information, Kullback-Leibler divergenc…
Develops methods to estimate gradient of EIG for Bayesian Experimental Design.
problem Optimizing Bayesian inference through efficient experimental design.
method Introduces UEEG-MCMC and BEEG-AP methods for estimating EIG gradient.
result Both methods improve upon existing benchmarks in EIG optimization.
GO-OED maximizes predictive information gain on nonlinear QoIs.
problem Maximizing information gain on nonlinear predictive quantities.
method Nested Monte Carlo estimator, Markov chain Monte Carlo, kernel density estimation, Bayesian optimization.
result GO-OED outperforms conventional OED in nonlinear settings.
Enhances robustness in experimental design through Generalised Bayesian inference.
problem Poor inference and estimates of information gain when statistical model is incorrectly specified.
method Generalised Bayesian (Gibbs) inference framework applied to experimental design.
result GBOED enhances robustness to outliers and incorrect assumptions about noise distribution.
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.
A new method models financial returns by separating sign and magnitude, improving forecasting accuracy.
problem Capturing nonlinear predictability in financial return dynamics.
method Decomposes returns into sign and magnitude components, using a joint distribution model.
result Significantly outperforms traditional linear models in forecasting U.S. stock market returns.
Bayesian calibration for BCP self-assembly models using image data and measure transport.
problem Calibrating models of BCP self-assembly from image data with aleatory uncertainty.
method Likelihood-free inference via measure transport and summary statistics.
result Expected information gains can be computed efficiently for model calibration.
Upper bound derived for informed traders' gains in a model, akin to thermodynamics.
problem Informed traders' gains in a financial model with finite horizon.
method Bayesian inference and entropic inequality.
result Upper bound for expected gain, analogous to thermodynamics.
We propose a cost-effective framework for preference elicitation and aggregation under the Plackett-Luce model with features. Given a budget, our framework iteratively computes the most cost-effective elicitation questions in order to help the agents make a better group decision. We illustrate the viability of the fram…
Maximizes robustness in Bayesian experimental design under model uncertainty.
problem Brittleness of Bayesian experimental design under model misspecification.
method Formulates as a max--min game, uses Sibson's α-MI, and adopts PAC-Bayes framework.
result Establishes robust belief update and conditional information gain measure.
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We present new online algorithms for path learning with non-additive count-based gains for the three settings of full information, semi-bandit and…
ICYM2I corrects missingness bias in multimodal learning.
problem Missingness patterns between source and target environments affect multimodal learning performance.
method ICYM2I uses inverse probability weighting to correct missingness bias in predictive performance and information gain.
result ICYM2I improves multimodal learning performance by accounting for missingness.
BED-LLM uses Bayesian experimental design to improve LLMs' information gathering.
problem Improving LLMs' ability to gather information adaptively.
method Iteratively choosing questions to maximize expected information gain using a probabilistic model.
result BED-LLM achieves substantial performance gains compared to other adaptive design strategies.
Semi-supervised learning benefits from informative missing labels, improving classifier performance.
problem Missing labels in semi-supervised learning can be informative, improving classifier performance.
method Formulates missingness as a mixture model problem and uses EM algorithm for fitting.
result Modelling informative missingness can yield a classifier with smaller expected error than a completely labelled sample.
Ensembles of classification and regression trees remain popular machine learning methods because they define flexible non-parametric models that predict well and are computationally efficient both during training and testing. During induction of decision trees one aims to find predicates that are maximally informative …
Active sampling algorithm improves accuracy of inferred scores from pairwise comparisons.
problem Inference of accurate scores from time-consuming pairwise comparisons.
method Approximate message passing and expected information gain maximization.
result ASAP offers the highest accuracy of inferred scores compared to existing methods.
As Computer Vision moves from a passive analysis of pixels to active analysis of semantics, the breadth of information algorithms need to reason over has expanded significantly. One of the key challenges in this vein is the ability to identify the information required to make a decision, and select an action that will …
New method optimizes resource allocation for uncertain tasks.
problem Optimal resource allocation for uncertain tasks with limited capacity.
method Formulated as an assignment problem, optimized using learning to rank with net discounted cumulative gain.
result Achieves higher expected profit and precision compared to classification methods.
Framework improves CATE estimation by aligning active learning with causal objectives.
problem High cost of outcome measurements limits CATE estimation.
method Causal-EPIG framework, targeting unobservable causal quantities.
result Strategies outperform standard baselines, revealing context-dependent optimal approaches.
Controller seeks informative system observations to predict nonlinear dynamics.
problem Predicting nonlinear dynamics with uncertain parameters.
method Expected free energy minimization for balancing goal state and informative observations.
result Controller improves performance in uncertain parameter scenarios.
We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with respect to the current model, are maximally informative about the underlying causal structure. Unli…
Unified framework for hybrid learning and optimization via active inference.
problem Sequential decisions in black-box evaluations requiring both task improvement and uncertainty reduction.
method Pragmatic Curiosity (PraC) framework that evaluates queries by balancing information gain and pragmatic value.
result Unified approach reduces decision risk and improves coverage of critical regions without task-specific rules.
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.
The paper decomposes probabilistic scores into reliability, uncertainty, and information loss.
problem Understanding the reliability and uncertainty of probabilistic predictions.
method Developed decomposition identities for proper losses, quantifying reliability, residual uncertainty, and information gain.
result A three-term identity for classification scores, revealing miscalibration, grouping term, and feature-level uncertainty.
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment. Acquiring more relevant infor…
In this article we will propose a completely new point of view for solving one of the most important paradoxes concerning game theory. The solution develop shifts the focus from the result to the strategy s ability to operate in a cognitive way by exploiting useful information about the system. In order to determine fr…
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several …
PASOA optimizes Bayesian design by improving SMC samplers and EIG.
problem Sequential design optimization for accurate parameter inference.
method Sequential optimization using contrastive estimation, SMC samplers, and tempering.
result PASOA optimizes design and inference with improved consistency.
Study asset pricing with reference-dependent preferences, finding matching equity premia.
problem Understanding asset pricing under reference-dependent preferences.
method Discrete-time consumption-based capital asset pricing model with reference-dependent preferences.
result Models can generate equity premia matching empirical estimates, showing procyclical price-dividend ratio and countercyclical equity premium.