SAVE combines Q-learning and MCTS with amortized value estimates for improved performance.
problem Combining model-free Q-learning and model-based MCTS for efficient learning and planning.
method SAVE uses a learned prior to guide MCTS, which estimates improved state-action values. These estimates are used to update the prior, creating a cooperative relationship between learning and search.
result SAVE achieves higher rewards with fewer training steps and strong performance with small search budgets.
New method uses neural networks to efficiently approximate Bayesian inference for complex models.
problem Efficiently approximating Bayesian inference for complex models with varying temperatures.
method Fully amortized neural posterior estimator trained on a single forward pass.
result Achieves competitive posterior approximations across various temperatures and benchmarks.
Neural processes approximate Gaussian process inference, revealing three key costs.
problem Approximating Gaussian process inference with neural processes.
method Bounding KL divergence into three components: label contamination, information bottleneck, and amortization error.
result Characterization of three costs of amortizing Gaussian process inference with neural processes.
FastSHAP speeds up Shapley value estimation for black-box models.
problem Efficiently calculating Shapley values for complex models.
method Uses a learned explainer model in a single forward pass.
result Generates high-quality explanations with significant speedup.
The study compares Bayesian and frequentist approaches in deep learning.
problem Comparing Bayesian and frequentist inference in deep learning.
method Conducts a comparative analysis of point and posterior estimators across various settings.
result Amortized point estimators generally outperform posterior inference, though posterior inference remains competitive in some low-dimensional problems.
ACE improves GBI for simulators by approximating cost functions, making inference more efficient.
problem Inference for misspecified simulators is overly restrictive.
method Amortized cost estimation (ACE) for Generalized Bayesian Inference (GBI).
result ACE provides accurate cost predictions and more efficient inference.
Proposes an amortized variational framework for Deep Q Networks.
problem Efficient exploration in deep reinforcement learning.
method Amortized variational inference for action value function approximation.
result Significantly less learning parameters and better performance.
The variational autoencoder (VAE) is a popular model for density estimation and representation learning. Canonically, the variational principle suggests to prefer an expressive inference model so that the variational approximation is accurate. However, it is often overlooked that an overly-expressive inference model ca…
This paper compares amortized methods for Bayesian posterior estimation.
problem Bayesian inference difficulties with iterative routines.
method Amortized in-context Bayesian posterior estimation using transformers and normalizing flows.
result Reverse KL estimator superior for predictive problems.
Self-consistency improves the accuracy of model comparison methods.
problem Improving the accuracy of model comparison methods when simulation models are misspecified.
method Supplement traditional simulation-based training with a self-consistency loss on unlabeled real data.
result Self-consistency training improves model comparison accuracy, especially in open-world scenarios.
Efficiently predicts optimal transport plans using sliced potentials.
problem Predicting optimal transport plans across multiple measure pairs efficiently.
method Regression-based and objective-based amortization strategies using sliced optimal transport potentials.
result Efficient and accurate prediction of optimal transport plans for various tasks.
Naive approaches to amortized inference in probabilistic programs with unbounded loops can produce estimators with infinite variance. This is particularly true of importance sampling inference in programs that explicitly include rejection sampling as part of the user-programmed generative procedure. In this paper we de…
Adversarial robustness of amortized Bayesian inference is studied, showing it can be improved.
problem Adversarial robustness of amortized Bayesian inference.
method Simulation-based estimation, regularization scheme based on Fisher information.
result Adversarial robustness can be improved with a regularization scheme.
Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions - a computational pipeline which is inefficient when the target function(s) are known upfront. In this …
Direct neural ratio estimator for likelihood-free inference.
problem Efficient likelihood estimation for complex models.
method Amortized likelihood ratio estimation using neural networks.
result DNRE often outperforms previous ratio estimators.
CogFormer trains a transformer to estimate parameters across various cognitive models.
problem Difficulty in fitting complex cognitive models and iterating over varying assumptions.
method Meta-amortized framework using a transformer to estimate parameters across multiple models.
result CogFormer accurately estimates parameters across different model families with minimal retraining.
Simformer uses transformer models to perform flexible Bayesian inference.
problem Current simulation-based inference methods are inflexible and require fixed priors.
method Trains a probabilistic diffusion model with transformer architectures.
result Outperforms state-of-the-art methods on various benchmarks.
A new recursive mixture estimation algorithm improves VAE inference efficiency and accuracy.
problem Inaccurate posterior approximation in traditional VAEs.
method Recursive mixture estimation algorithm using functional gradient approach for iterative component selection.
result Significantly higher test data likelihood compared to state-of-the-art methods on benchmark datasets.
New method improves causal structure discovery with Prior-Fitted Networks.
problem Errors in likelihood estimation limit proper causal structure discovery.
method Amortized causal discovery with Prior-Fitted Networks.
result Significant gains in structure recovery compared to baselines.
Amortizes MIPS by training neural networks to predict optimal keys.
problem Efficiently solving Maximum Inner Product Search for repeated queries.
method Regression-based SupportNet and KeyNet models trained on support functions.
result Significant improvement in IVF match rates for document embeddings.
A new SBI framework for trawl processes efficiently estimates parameters from large datasets.
problem Challenges in estimating parameters of complex stochastic processes.
method Telescoping ratio estimation, Chebyshev polynomial approximations, amortized posterior inference.
result Accurate and efficient inference for intractable stochastic processes, even with limited data.
Improved state estimation in nonlinear models using amortized backward variational inference.
problem State estimation in general state-space models.
method Amortized backward variational inference with neural network parameters.
result Linear growth of variational approximation error in number of observations.
BayesFlow trains neural networks for fast Bayesian inference.
problem Fast Bayesian inference for complex models.
method Amortized neural networks for intractable posterior distributions.
result Fast inference through pre-trained neural networks.
FalconBC improves patient-specific cardiovascular modeling by estimating boundary conditions efficiently.
problem Efficiently estimating boundary conditions in patient-specific cardiovascular models, especially in open-loop models and anatomies with lesions.
method A general amortized inference framework based on probabilistic flow that treats clinical targets and anatomies as conditioning variables.
result Demonstrated on two patient-specific models, FalconBC improves efficiency and accuracy in estimating boundary conditions.
JANA trains networks to approximate Bayesian models efficiently.
problem Intractable likelihood functions and posterior densities in Bayesian models.
method End-to-end training of three networks: summary, posterior, and likelihood networks.
result JANA provides accurate amortized marginal likelihood and posterior predictive estimation.
New methods make Bayesian inference feasible for complex cognitive models.
problem Bayesian inference is hard for complex, unknown models.
method Amortized neural network estimators for simulation-based inference.
result Effort in training networks amortizes over multiple uses.
Probabilistic programming languages (PPLs) are a powerful modeling tool, able to represent any computable probability distribution. Unfortunately, probabilistic program inference is often intractable, and existing PPLs mostly rely on expensive, approximate sampling-based methods. To alleviate this problem, one could tr…
UA-SABI uses surrogates to speed up Bayesian inference for expensive models.
problem Inference for computationally expensive models is slow and uncertain.
method Combines surrogate modeling with Amortized Bayesian Inference (ABI) to propagate uncertainties.
result Reliable, fast, and repeated Bayesian inference for expensive models is achieved.
New method improves variational inference for better posterior approximation.
problem Challenges in minimizing inclusive KL divergence for amortized variational inference.
method Likelihood-tempered sequential Monte Carlo samplers to estimate inclusive KL gradient.
result SMC-Wake method fits variational distributions more accurately than existing methods.
A neural network estimates sampling distributions for hard problems where classical methods fail.
problem Bootstrap failure in estimating sampling distributions for specific statistics.
method Neural network trained on simulated datasets using pinball loss.
result Neural network attains 95% nominal coverage and 97% improvement over classical methods on four bootstrap-failure problems.
Paper presents a fast method for estimating hidden states in Bayesian models.
problem Estimating hidden states in Bayesian state space models efficiently.
method Amortized simulation-based inference with pretraining.
result The method achieves sufficient accuracy and fast inference times.
After the release of the final accounting standards for impairment in July 2014 by the IASB, banks will face the next significant methodological challenge after Basel 2. In this paper, first methodological thoughts are presented, and ways how to approach underlying questions are proposed. It starts with a detailed disc…
Amortized VI for DGPs learns efficient inference.
problem Expressive limitations in GP approximations.
method Amortized variational inference for DGPs.
result Improved expressive prior and posterior for DGPs.
Inference models are a key component in scaling variational inference to deep latent variable models, most notably as encoder networks in variational auto-encoders (VAEs). By replacing conventional optimization-based inference with a learned model, inference is amortized over data examples and therefore more computatio…
JADAI optimizes design and inference for parameter estimation.
problem Parameter estimation with active optimization of design variables.
method Jointly trains a policy, history network, and inference network to minimize posterior error.
result Achieves superior or competitive performance across benchmarks.
A new method decouples set representation learning from posterior modeling for efficient amortized inference.
problem Efficient inference for large sets of observations with shared factors.
method Train a mean-pool Deep Set on sets of size at most two, then finetune the inference head on pre-aggregated embeddings.
result Matches or outperforms standard baselines at a fraction of the compute cost for large N.
SurvivalPFN simplifies survival analysis through amortized Bayesian inference.
problem Selecting appropriate survival analysis methods requires expertise and can be time-consuming.
method SurvivalPFN uses a prior-data fitted network for in-context Bayesian inference.
result SurvivalPFN achieves strong predictive performance across diverse datasets.
Plan2Vec learns image representations without labels, improving control tasks.
problem Learning image representations without labeled data.
method Constructs a weighted graph using near-neighbor distances and extrapolates to global embedding.
result Plan2Vec achieves accurate long-term value estimates in control tasks with reduced computational and memory costs.
Neural networks speed up statistical inference.
problem Efficient statistical inference for complex models.
method Neural networks for learning complex mappings.
result Amortized inference speeds up inference processes.
BayesFlow 2 speeds up Bayesian inference for complex models.
problem Slow Bayesian inference for complex models.
method Amortized Bayesian inference with neural networks.
result Streamlined workflow supports broad adoption.
ALINE optimizes Bayesian inference and data acquisition by strategically querying informative data.
problem Strategic acquisition of informative data for Bayesian inference in challenging tasks.
method Unified framework combining amortized Bayesian inference and active data acquisition using a transformer architecture trained via reinforcement learning.
result ALINE delivers both instant and accurate inference along with efficient selection of informative points.
New method improves ABI for sequential data, reducing forgetting and improving accuracy.
problem Performance degradation of ABI under model misspecification and distribution shifts.
method Decouples simulation-based pre-training from unsupervised SC fine-tuning, using memory buffer and elastic weight consolidation.
result Significant mitigation of forgetting and improved posterior estimates compared to standard simulation-based training.
A new method for fast Bayesian mixture model estimation.
problem Estimating Bayesian mixture models is computationally challenging.
method Amortized Bayesian Inference (ABI) framework for mixture models.
result The method provides fast inference for mixture models.
A-GPS learns to generate Pareto sets efficiently with user preferences.
problem Online discrete multi-objective optimization with user preferences.
method Generative model with class probability estimator (CPE) for non-dominance and preference alignment.
result Amortized generative model for efficient Pareto set approximation.
ABI adapts to graph data for fast, scalable inference.
problem Challenges in inference on graph-structured data.
method Amortized Bayesian Inference (ABI) framework for graph data.
result ABI successfully addresses challenges in graph data inference.
SC improves robustness in model comparison for misspecified models.
problem Model misspecification challenges in amortized Bayesian inference.
method Parameter posterior-based methods augmented with SC training.
result SC improves robustness under model misspecification.
This paper reviews recent advancements in amortized Variational Inference.
problem Scalability and efficiency issues in traditional Variational Inference.
method Systematic review of various Variational Inference techniques, focusing on amortized approaches.
result Amortized Variational Inference improves scalability and efficiency for generative modeling tasks.
Paper introduces IO-NPF for efficient Bayesian experimental design.
problem Efficient Bayesian experimental design in non-exchangeable settings.
method Inside-Out Nested Particle Filter (IO-NPF) for non-Markovian state-space models.
result IO-NPF achieves O(T2) computational complexity, improving efficiency.