Turing Learning infers system behavior without metrics, outperforming traditional methods.
problem Inferring complex behaviors of systems without predefined metrics.
method Simultaneously optimizes model and classifier populations using behavior data.
result Successfully infers behaviors of physical robot swarms and simulated systems.
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.
Machine learning infers fluid dynamics from data without prior knowledge.
problem Inferring complex fluid behavior from limited data.
method Reservoir computing for partial and full inference of fluid variables and energy functions.
result Reservoir systems can infer long-term fluid dynamics and energy spectra from past data.
New method infers intent from suboptimal behavior by modeling incorrect internal beliefs about dynamics.
problem Inferring intent from suboptimal human behavior using traditional methods assumes near-optimality, which is not always valid.
method Model suboptimal behavior as internal model misspecification, estimating incorrect beliefs about dynamics.
result Accurately models human intent by accounting for internal model inaccuracies.
Automated model predicts C. elegans escape behavior.
problem Complex escape behavior of C. elegans due to rising temperature.
method Phenomenological modeling using Sir Isaac dynamical inference platform.
result Inferred model accurately predicts worm behavior and is biologically interpretable.
Transformers infer tasks from context via two modes, geometrically shaped task vectors explain their behavior.
problem Understanding how transformers infer tasks from context and the geometric properties of task vectors.
method Synthetic setting to train small transformers, mathematical characterization of task-vector geometry and inference modes.
result Task-vector geometry shapes in-distribution and out-of-distribution behavior of transformers.
We infer constraints that explain expert behavior in MDPs.
problem Estimating constraints alongside rewards in IRL.
method Maximum Entropy IRL framework for inferring constraints.
result Maximum Likelihood Constraints improve model likelihood of expert behavior.
New game design method for better trait inference.
problem Inferring latent psychological traits from human behavior.
method Formulated as a mutual information maximization problem, solved using variational lower bound optimization.
result Designed games successfully distinguish among players with different traits, outperforming traditional methods.
Dual-view mixture models cluster users with features and latent behaviors inferred from actions.
problem Clustering users based on features and latent behavioral functions inferred from indirect observations.
method Dual-view mixture models with non-parametric Dirichlet Process for automatic cluster number inference.
result Dual-view models outperform single-view models when one view lacks information.
Develops a method to predict system behavior under disruptions.
problem Predicting changes in system behavior due to external perturbations.
method Counterfactual distribution regression for structured inference.
result Generalizes behavior predictions from natural to disrupted states.
ABC improves cognitive model parameter estimation from behavioral data.
problem Estimating cognitive model parameters from human behavioral data.
method Approximate Bayesian Computation (ABC) for parameter conditioning.
result ABC improves parameter estimates and supports individual user fitting.
Bayesian neural networks ignore data in infinite units limit.
problem Pathological behavior of posterior in over-parameterized networks.
method Mean-field variational inference in infinite hidden units limit.
result Posterior mean converges to zero, ignoring data.
This paper introduces a new approach to active inference using constrained Bethe Free Energy.
problem Tackling the limitations of existing epistemic behavior models in active inference.
method Introducing a constrained Bethe Free Energy (CBFE) perspective to optimize epistemic behavior in generative models.
result CBFE optimization leads to more robust and flexible epistemic behavior compared to existing methods.
New method for valid and exact statistical inference of multi-dimensional change-points.
problem Statistical inference of change-points in multi-dimensional sequences.
method Proposes a method to guarantee the statistical reliability of both location and components of detected changes.
result Demonstrates the effectiveness of the method in genomic abnormality identification and human behavior analysis.
Bayesian framework infers personalized embeddings for diverse human demonstrations.
problem Lack of personalized models for diverse human behaviors.
method Bayesian LfD framework inferring human-specific embeddings.
result Outperforms state-of-the-art techniques on synthetic and real-world data.
Neural network approximates Bayesian decision-making parameters.
problem Analytical intractability of Bayesian decision-making in naturalistic tasks.
method Neural amortization of Bayesian actor model.
result Efficient gradient-based inference of Bayesian actor model parameters.
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series. Our model discovers a latent set of dynamical behaviors shared among the sequences, and segments each time series into regions defined by a subset of these behaviors. Using a beta process prior, the size of the…
New model accounts for continuous human trajectories in robotics.
problem Inaccurate probabilistic models of human behavior in robotics.
method Developed a new probabilistic model that considers distances between continuous trajectories.
result The new model outperforms existing models in explaining human behavior and improving robot inference.
We provide a rigorous definition of the visual cause of a behavior that is broadly applicable to the visually driven behavior in humans, animals, neurons, robots and other perceiving systems. Our framework generalizes standard accounts of causal learning to settings in which the causal variables need to be constructed …
New method infers human sensorimotor costs from behavior.
problem Inferring human sensorimotor costs from observed behavior.
method Inverse optimal control with signal-dependent noise.
result Recovering costs and benefits in sensorimotor behavior.
FOCAL tackles offline meta-reinforcement learning with efficient task inference and behavior regularization.
problem Efficiently adapt RL algorithms to unseen tasks without interactions, addressing bootstrapping errors and robust task inference.
method FOCAL combines behavior regularization, a deterministic context encoder, and a negative-power distance metric for efficient task inference.
result FOCAL outperforms prior algorithms on meta-RL benchmarks, demonstrating computational efficiency.
Bayesian method infers local rules for collective animal movement.
problem Learn local rules governing long-term group behaviors.
method Bayesian Inverse Reinforcement Learning with Linearly-Solvable Markov Decision Process.
result Recover true costs and find value of collective movement.
The paper connects neural network ensembles to Bayesian inference using variational methods.
problem Explaining the behavior of ensemble methods in neural networks.
method Deriving conditions for ensemble optimization to reduce divergence to the posterior distribution.
result Ensemble methods can be a valid alternative to approximate Bayesian inference.
New method infers causality from short memory-less transition data.
problem Inferring causality from short time series data.
method Composition of Transitions (COT) and machine learning models.
result Highly accurate in inferring causal relationships from short data.
Pyro enables scalable AI models using probabilistic programming.
problem Developing complex probabilistic models for large datasets.
method Stochastic variational inference, PyTorch, Poutine.
result Pyro supports scalable AI models with high-dimensional data.
A new framework for adaptive behavior using reusable value profiles.
problem Adaptive behavior in changing environments requires switching among value-control regimes, but maintaining separate parameters for each situation is impractical.
method Introduces value profiles: reusable bundles of parameters assigned to hidden states, allowing for state-conditional strategy recruitment without independent parameters for each context.
result Profile-based models outperform simpler alternatives in probabilistic reversal learning, suggesting belief-dependent control of adaptive behavior.
Bayesian inference models power-law graphs with efficient algorithms.
problem Modeling networks with heavy-tailed degree distributions.
method Constructs graphs using BFRY random variables and applies variational Bayesian inference.
result Automatic selection of power law behavior from data.
CAFL breaks feedback loops in recommender systems using causal inference.
problem Feedback loops in recommender systems compromise recommendation quality and homogenize user behavior.
method Causal Adjustment for Feedback Loops (CAFL) algorithm that breaks feedback loops using causal inference.
result CAFL improves recommendation quality compared to prior correction methods.
Paper models MOOC users behavior using MDP for easier classification.
problem Difficulty in defining and classifying MOOC users based on their behavior.
method Uses Markov Decision Process (MDP) to model user behavior from log data.
result Classifies users based on their intentions inferred from MDP rewards.
FlowVAT improves variational inference for multi-modal distributions.
problem Mode-seeking behavior and collapse in variational inference for complex posteriors.
method Conditional tempering approach for normalizing flow variational inference.
result FlowVAT outperforms traditional and adaptive annealing methods in multi-modal distributions, finding more modes and achieving better ELBO values.
Models for recommender systems use latent factors to explain the preferences and behaviors of users with respect to a set of items (e.g., movies, books, academic papers). Typically, the latent factors are assumed to be static and, given these factors, the observed preferences and behaviors of users are assumed to be ge…
Method infers internal model from rational foraging behavior.
problem Discover how the brain represents and computes dynamic beliefs.
method POMDP model, EM algorithm, maximum likelihood estimation.
result Successfully recovered internal model and reward function of foraging agents.
Mixup inference improves adversarial robustness by mixing inputs with clean samples.
problem Adversarial examples can fool deep networks due to local non-linearity.
method Develops mixup inference, which mixes inputs with clean samples to shrink adversarial perturbations.
result Mixup inference enhances adversarial robustness for mixup-trained models.
Adapts IRL for dual-system agents, correcting goal inference errors.
problem Inferring goals from dual-system decision-making behaviors.
method Generalized dual-system framework, optimal plan computation, adapted IRL algorithm.
result Correct goal inference for dual-system agents improves overall utility.
Develops variable-lag Granger causality for more accurate time series analysis.
problem Fixed time delay assumption in Granger causality does not fit many real-world applications.
method Variable-lag Granger causality, inferring with arbitrary time delays.
result Performs better than existing methods in coordinated collective behavior studies.
A new algebra for probabilistic programming improves tail behavior accuracy.
problem Inaccurate tail behavior in probabilistic models based on neural networks.
method Developed a three-parameter tail asymptotics algebra based on the generalized Gamma distribution.
result Inference algorithms using the heavy-tailed algebra achieve superior performance.
Econometric framework integrates heavy-tailed distributions with behavioral probability weighting for better asset pricing.
problem Underestimation of Value-at-Risk by traditional models in asset pricing.
method Developed an econometric framework combining heavy-tailed Student's t distributions with behavioral probability weighting. result Student's t specifications outperform Gaussian models in 88.4% of cases, reducing underestimation of Value-at-Risk by 16.5 percentage points. The paper examines how updates to probabilistic models influence behavior based on evidence.
problem Understanding how updates to probabilistic models influence behavior based on evidence.
method Study of KL-regularized soft updates as Bayesian posterior updates within a single probabilistic model.
result Posterior updates determine relative incentives but not absolute rewards, which are ambiguous up to context-specific baselines.
AIF reformulated as convex MDP for adaptive behavior.
problem Adaptive behavior and policy optimization.
method Formulating AIF as convex MDP, deriving mirror descent algorithm.
result EFE minimization in AIF is equivalent to reward maximization in latent MDP, with epistemic component.
Study finds users mostly use recent market and decision information to guess market direction.
problem Limited ability to model and predict human decision-making in stock markets.
method Used networks inference with stochastic block models (SBM) to find most predictive model of unobserved decisions.
result Users mostly use recent information to guess market direction, and their decision-making strategies are analogous to behaviors in other contexts.
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.
Method trains multi-modal policy from unlabeled mixed demonstrations.
problem Training policies from unlabeled mixed demonstrations.
method Variational autoencoder with categorical latent variable to discover latent factors of variation.
result Policy can reproduce specific behaviors by conditioning on categorical vectors.
Paper proposes Imitative Models combining IL and planning for flexible goal achievement.
problem Difficult to direct IL to arbitrary goals and specify reward functions for goal-directed planning.
method Imitative Models are probabilistic predictive models that plan interpretable expert-like trajectories.
result Imitative Models outperform IL and planning approaches in a dynamic autonomous driving task.
Improved student engagement detection using contextual and visual data.
problem Detecting students' behavioral engagement in real-world settings.
method Two-phase approach: contextual logs for active use, appearance information for engagement inference.
result Improved F1-scores from 0.77 to 0.82 with contextual information.
Framework for mapping unknown environments using biobot agents and topological data analysis.
problem Mapping unknown environments with minimal sensing and localization constraints.
method Local interactions among biobot agents create encounter graphs, which are used for manifold learning and topological data analysis.
result The proposed metric converges to geodesic distances in the underlying manifold, and the topological features are robust.
High-dimensional adjustment reduces bias in estimating peer effects from observational data.
problem Estimating peer effects from observational data is challenging due to confounding variables and high bias.
method Used high-dimensional adjustment with propensity score models to estimate peer effects.
result High-dimensional adjustment produces estimates of peer effects statistically indistinguishable from randomized experiments.
A new model captures car-following and lane-changing behaviors in traffic.
problem Modeling stochastic microscopic traffic behaviors.
method Physics regularized Gaussian process (PRGP) approach.
result The proposed model outperforms previous methods in estimation precision.
Paper proposes variational inference for piecewise-linear systems.
problem Intractability of switching dynamical systems.
method Variational approximation and expectation-maximization algorithms.
result Parameters can be estimated off-line, including the number of linear modes.