Paper proves Jeffrey's update rule minimizes relative entropy.
problem Improving Bayesian learning algorithms.
method More concise proof of Jeffrey's update rule.
result Jeffrey's update rule reduces relative entropy.
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48 results for “Bayesian update rule”
New learning rule simplifies Bayesian updates for deep learning.
problem Bayesian learning rule's complexity and manifold constraints.
method Lie-group approach to simplify Bayesian updates.
result New algorithm learns sparse features in deep learning.
Rule-State Inference (RSI): A Bayesian Framework for Compliance Monitoring in Rule-Governed Domainscs.LG
RSI uses Bayesian inference to monitor compliance in rule-governed domains.
problem Structural obstacles in compliance monitoring, including unlabeled outcomes and selective withholding of evidence.
method Rule-State Inference (RSI) treats formalized rules as Bayesian priors and infers compliance states through mean-field variational inference.
result RSI delivers formal guarantees of adaptability, consistency, and convergence, validated on a synthetic enterprise benchmark.
When Is Generalized Bayes Bayesian? A Decision-Theoretic Characterization of Loss-Based Updatingstat.ME
New decision-theoretic characterization separates belief and decision posteriors.
problem Understanding the conditions under which loss-based updating coincides with Bayesian updating.
method Decision-theoretic approach to distinguish belief and decision posteriors.
result Generalized Bayes coincides with ordinary Bayesian updating only if the loss is proportional to negative log-likelihood.
A novel Bayesian computation method using importance weighting improves numerical stability and performance.
problem Bayesian computation stability and performance issues.
method Nonparametric approach via feature means, importance weighting, and kernel Bayes' rule.
result Importance weighted kernel Bayes' rule yields superior numerical stability and performance.
Bayesian neural networks update beliefs with soft evidence, improving accuracy and calibration.
problem Updating neural network weights with uncertain or soft evidence.
method Developed two algorithms to approximate Jeffrey's rule for updating neural network weights.
result Jeffrey-based methods outperform traditional approaches in accuracy and calibration, especially in noisy data.
Bayesian unlearning uses Bayes' rule to remove data from a model, but faces challenges in obtaining the exact posterior.
problem Removing data from a trained model while maintaining model accuracy.
method Uses Laplace approximation and Variational Inference to approximate the updated posterior.
result Insights on the applicability of Bayesian unlearning in practical scenarios for neural networks.
Test-time training adapts a pretrained model to each prompt via parameter updates, improving accuracy under pretraining-to-test distribution shifts.
problem Improving accuracy of pretrained models under distribution shifts.
method Explaining TTT behavior through a decision-theoretic lens.
result TTT reduces prediction error when updates are spectrally matched to the prompt's signal-to-noise ratio and aligned with query-relevant eigen-directions.
Proposes a probabilistic optimization method for large-scale problems.
problem Large-scale regularized optimization problems.
method Develops a probabilistic interpretation of the incremental proximal gradient algorithm and uses Bayesian filtering.
result Makes it possible to solve large-scale problems using well-known Bayesian filters.
Proposes an alternative method to train RBMs with binary synapses using Bayesian learning rule.
problem Training RBMs with binary synapses is challenging due to discrete nature of synapses.
method Proposes an alternative optimization method using the Bayesian learning rule, updating natural parameters instead of expectation parameters.
result No additional clipping is needed as natural parameters take values in the entire real domain.
This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [Kivinen & Warmuth, 1994]. We provide a unified framework for parameter estimation that encompasses both on-line learning, where the model is c…
We propose a generative model of a group EEG analysis, based on appropriate kernel assumptions on EEG data. We derive the variational inference update rule using various approximation techniques. The proposed model outperforms the current state-of-the-art algorithms in terms of common pattern extraction. The validity o…
Improved Bayesian inference for neuronal ensemble inference reduces computational cost.
problem Efficient inference of neuronal ensembles from activity data.
method Modified MCMC algorithm with simulated annealing for hyperparameter control.
result Our method reduces computational cost while maintaining or improving inference accuracy.
Bayesian framework for policy learning in decision problems.
problem Maximizing expected welfare in decision-making problems.
method Loss-based Bayesian updating and squared-loss surrogate for welfare maximization.
result General Bayes posterior over decision rules with Gaussian pseudo-likelihood interpretation.
We propose a Bayesian methodology for one-mode projecting a bipartite network that is being observed across a series of discrete time steps. The resulting one mode network captures the uncertainty over the presence/absence of each link and provides a probability distribution over its possible weight values. Additionall…
Develops a Bayesian framework for portfolio choice with a new posterior distribution.
problem Estimation risk in parametric portfolio policies.
method Generalized Bayesian framework with Gibbs posterior, utility maximization, and KNEEDLE algorithm.
result Optimal scaling parameter controls the balance between prior and data.
Paper proposes VAE-BPTF for better tensor factorization of sparse, imbalanced count data.
problem Inference of Bayesian Poisson-Gamma models for sparse and imbalanced count data is challenging.
method Variational auto-encoder framework with multi-layer perceptron networks for complex update information sharing and reweighting.
result VAE-BPTF outperforms current models in reconstruction errors and latent factor coherence across real-world datasets.
Lifelong Generative Modelingstat.ML
This work tackles lifelong unsupervised generative modeling.
problem Learning multiple tasks sequentially with knowledge retention.
method Student-Teacher Variational Autoencoder architecture with cross-model regularizer.
result Model mitigates catastrophic interference in sequential learning.
A new update rule for deep reinforcement learning reduces learning variance and variance in reference signals.
problem Learning variance and incorrect reference signals in deep reinforcement learning.
method t-soft update method inspired by student-t distribution, which reduces extreme updates and accelerates similar updates.
result The t-soft update method outperforms conventional methods in terms of return and variance in PyBullet robotics simulations.
A new memory system learns and generates like new data.
problem Training generative models on new data.
method Hierarchical conditional generative model with distributed memory.
result The memory system significantly improves generative models.
This paper introduces a new probabilistic model for online learning which dynamically incorporates information from stochastic gradients of an arbitrary loss function. Similar to probabilistic filtering, the model maintains a Gaussian belief over the optimal weight parameters. Unlike traditional Bayesian updates, the m…
Adaptive Bayesian learning aggregates experts to improve performance.
problem Bayesian online learning's performance depends on inferential choices.
method Treat Bayesian update rules as experts and aggregate them based on sequential predictive losses.
result The aggregate competes with the best expert in hindsight at a low aggregation cost.
Bio-inspired neural networks use predictive coding for efficient weight updates.
problem Training artificial neural networks efficiently and biologically plausibly.
method Predictive Coding (PC) updates weights locally using only local information.
result PC provides theoretical advantages like automatic gradient scaling.
A Bayesian factor graph reduced to normal form consists in the interconnection of diverter units (or equal constraint units) and Single-Input/Single-Output (SISO) blocks. In this framework localized adaptation rules are explicitly derived from a constrained maximum likelihood (ML) formulation and from a minimum KL-dive…
Enhances Bayesian learning with rule-based evolutionary techniques.
problem Improving Bayesian inference with expert knowledge and data patterns.
method Combines Bayesian inference with rule-based systems and grammatical evolution.
result Automatically derives rules from data, improving point predictions and uncertainty quantification.
Robust Kalman filtering method for outlier detection.
problem Outliers and misspecified measurement models in state-space models.
method Combines generalised Bayesian inference with Kalman filters for robustness and efficiency.
result Matches or outperforms other robust filtering methods at lower computational cost.
Meta-learning improves unsupervised representation learning for later tasks.
problem Discovering useful data representations without supervised labels.
method Meta-learning an unsupervised weight update rule to target semi-supervised classification performance.
result Meta-learned update rule produces useful features and sometimes outperforms existing techniques.
Bayesian optimization improves Monte-Carlo tree search for better state value estimation.
problem Slow convergence in Monte-Carlo tree search due to averaging in backpropagation.
method Softmax MCTS and Monotone MCTS, using Bayesian optimization with Gaussian process prior.
result Our framework outperforms previous methods in computer Go.
The paper analyzes when credal sets stabilize under iterative updates in machine learning.
problem When do credal sets stabilize under iterative updates in machine learning?
method Fixed-point theorems for credal set updates.
result The paper provides the first analysis of credal set stability.
Agents learn state without recalling private signals in networks.
problem Agents learn unknown state from private signals in networks.
method Memoryless update rules that replicate Bayesian agents' beliefs.
result Exponential learning rate similar to Bayesian agents.
Improved NMF using variance-reduced MU rule.
problem Slow convergence of multiplicative update in NMF.
method Introduces variance-reduced stochastic multiplicative update.
result Robustly outperforms state-of-the-art algorithms.
PROWL uses robust reward estimates to improve ITR selection.
problem Reward uncertainty in ITR estimation leads to inflated performance.
method PAC-Bayesian framework with reward uncertainty certificates.
result PROWL achieves better robust treatment regime estimation.
MTL2L learns to adapt optimisation rules for unseen data.
problem Learners need to adapt to unseen data domains.
method Introduces MTL2L, a context-aware neural optimiser.
result MTL2L can adapt optimisation rules for unseen data.
Bayesian sparse learning method improves deep neural network efficiency.
problem Sparse learning in deep neural networks with complex geometry.
method Preconditioned stochastic gradient Langevin Dynamics (PSGLD) for sampling and adaptive optimization of hyperparameters.
result The proposed algorithm achieves asymptotic convergence with controlled bias.
Proper-score observation-driven filters: local geometry, estimation, and continuous-time limitsmath.ST
Improves observation-driven filters using proper scoring rules for better parameter estimation.
problem Improves parameter estimation in observation-driven filters.
method Replaces likelihood score with negative parameter derivative of a proper scoring rule.
result Establishes consistency and asymptotic normality for estimation.
Proposes a new method for nonlinear Bayesian updates using ensemble kernel regression.
problem Nonlinear and non-Gaussian Bayesian updates for complex systems.
method Combines Kalman filtering for observed components and kernel density estimation for unobserved components, with subsampling and clustering.
result Reduces estimation errors in highly nonlinear scenarios compared to standard linear updates.
We analyze a model of learning and belief formation in networks in which agents follow Bayes rule yet they do not recall their history of past observations and cannot reason about how other agents' beliefs are formed. They do so by making rational inferences about their observations which include a sequence of independ…
This study analyzes adversarial training on linearly separable data and finds that gradient updates can achieve large margins in polynomial iterations.
problem Ensuring robustness in machine learning models trained on linearly separable data.
method Analysis of adversarial training with gradient updates on linearly separable data.
result Gradient updates in adversarial training can achieve large margins in polynomial iterations, whereas non-smooth methods require exponentially many iterations.
Paper proposes a stable update rule in hyperbolic space for better network modeling.
problem Complex network modeling in hyperbolic space.
method Explicit geodesic update rule in hyperbolic space with theoretical convergence guarantees.
result Algorithm convergence rate is better than Euclidean gradient descent and avoids bias.
New method for density estimation without approximating posterior distributions.
problem Challenges in non-smooth data distributions for Bayesian density estimation.
method Autoregressive likelihood decomposition and Gaussian process prior in a quasi-Bayesian framework.
result Achieves state-of-the-art results in small-data regimes.
New method reduces forecasting error by up to 67% in various data types.
problem Outliers and model misspecification in online infinite hidden Markov models.
method Batched Robust iHMM (BR-iHMM) with bounded posterior influence function.
result Reduces one-step-ahead forecasting error by up to 67% in various data types.
Bayesian method decomposes ITR value into direct and indirect effects.
problem Assessing how clinical benefit of an ITR is generated.
method Causal mediation framework using nested potential outcomes and Bayesian causal mediation forests.
result Identification and estimation of natural direct and indirect effects.
The study learns neural update rules by remembering past experiences.
problem Developing efficient online learning rules for neural networks.
method Representing neurons with vectors, using meta-neural networks for updates, and training for remembering past experiences.
result The approach reveals insights into learning rules and could be used for complex tasks like episodic memory.
Bayesian model updating uses VAEs to approximate likelihood with small data.
problem Approximating likelihood for small data sets in structural analysis.
method Uses multimodal VAEs to approximate likelihood, suitable for high-dimensional correlated observations.
result Demonstrates computational efficiency and accuracy compared to original VAE approach.
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processesstat.ML
Unified framework for efficient Gaussian process inference.
problem Efficient inference in non-conjugate Gaussian process models.
method Combines expectation propagation with linearization for improved efficiency.
result Unified view of various inference schemes, including classical smoothers and EP.
New approach separates VAE and GP for better molecular optimisation.
problem Optimizing complex structured domains like molecular spaces using VAEs.
method Decouples VAE for structure generation and GP for predictive modelling, combining them with a Bayesian update rule.
result Improves identification of high-potential candidates in molecular optimisation.
BAM integrates new data while selectively remembering past observations.
problem Slow adaptation and convergence to incorrect parameter values in non-stationary environments.
method Bayes' theorem with adaptive memory selection.
result BAM generalizes and demonstrates continuous adaptation in changing environments.
Study on incentivizing truthfulness in federated learning with heterogeneous data.
problem Manipulated updates in federated learning due to data heterogeneity.
method Formulated a game-theoretic approach to prevent clients from misreporting their gradient updates.
result Developed a payment rule that provably disincentivizes sending modified updates in federated learning.