Cover Image

Calibrated Test-Time Guidance for Bayesian Inference

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Geyfman, Daniel, Draxler, Felix, Groeneveld, Jan, Lee, Hyunsoo, Karaletsos, Theofanis, Mandt, Stephan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Variational Control for Guidance in Diffusion Models
by: Pandey, Kushagra, et al.
Published: (2025)

Hierarchical Variational Policies for Reward-Guided Diffusion
by: Pandey, Kushagra, et al.
Published: (2026)

Parallel Token Prediction for Language Models
by: Draxler, Felix, et al.
Published: (2025)

Modelling Cellular Perturbations with the Sparse Additive Mechanism Shift Variational Autoencoder
by: Bereket, Michael, et al.
Published: (2023)

Channel Vision Transformers: An Image Is Worth 1 x 16 x 16 Words
by: Bao, Yujia, et al.
Published: (2023)

Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?
by: Adler, Coen, et al.
Published: (2025)

Adjusting Pretrained Backbones for Performativity
by: Demirel, Berker, et al.
Published: (2024)

Lookahead Sample Reward Guidance for Test-Time Scaling of Diffusion Models
by: Kim, Yeongmin, et al.
Published: (2026)

Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers
by: Fanariotis, Anastasios, et al.
Published: (2025)

A Scale-Adaptive Framework for Joint Spatiotemporal Super-Resolution with Diffusion Models
by: Defez, Max, et al.
Published: (2026)

SCATR: Simple Calibrated Test-Time Ranking
by: Shyamal, Divya, et al.
Published: (2026)

Early-Exit Neural Networks with Nested Prediction Sets
by: Jazbec, Metod, et al.
Published: (2023)

Inference-Time Alignment Control for Diffusion Models with Reinforcement Learning Guidance
by: Jin, Luozhijie, et al.
Published: (2025)

Toward Identifiable Sparse Autoencoders
by: Nelson, Walter, et al.
Published: (2026)

Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance
by: He, Yufei, et al.
Published: (2025)

Calibrating Bayesian Learning via Regularization, Confidence Minimization, and Selective Inference
by: Huang, Jiayi, et al.
Published: (2024)

LRT-Diffusion: Calibrated Risk-Aware Guidance for Diffusion Policies
by: Sun, Ximan, et al.
Published: (2025)

Generative Uncertainty in Diffusion Models
by: Jazbec, Metod, et al.
Published: (2025)

Compute Aligned Training: Optimizing for Test Time Inference
by: Ousherovitch, Adam, et al.
Published: (2026)

MahaVar: OOD Detection via Class-wise Mahalanobis Distance Variance under Neural Collapse
by: Kim, Donghwan, et al.
Published: (2026)

Mitigating the Likelihood Paradox in Flow-based OOD Detection via Entropy Manipulation
by: Kim, Donghwan, et al.
Published: (2026)

Unveiling Imitation Learning: Exploring the Impact of Data Falsity to Large Language Model
by: Cho, Hyunsoo
Published: (2024)

GP-MoLFormer-Sim: Test Time Molecular Optimization through Contextual Similarity Guidance
by: Navratil, Jiri, et al.
Published: (2025)

Decocted Experience Improves Test-Time Inference in LLM Agents
by: Shen, Maohao, et al.
Published: (2026)

Correctness-Optimized Residual Activation Lens (CORAL): Transferrable and Calibration-Aware Inference-Time Steering
by: Miao, Miranda Muqing, et al.
Published: (2026)

Efficient Test-Time Scaling via Self-Calibration
by: Huang, Chengsong, et al.
Published: (2025)

Scalable Simulation-Based Model Inference with Test-Time Complexity Control
by: Gloeckler, Manuel, et al.
Published: (2026)

Learning to Discover at Test Time
by: Yuksekgonul, Mert, et al.
Published: (2026)

Test-Time Adaptation with Binary Feedback
by: Lee, Taeckyung, et al.
Published: (2025)

Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference
by: Schmitt, Marvin, et al.
Published: (2023)

Why the Counterintuitive Phenomenon of Likelihood Rarely Appears in Tabular Anomaly Detection with Deep Generative Models?
by: Kim, Donghwan, et al.
Published: (2026)

Information-Theoretic Safe Bayesian Optimization
by: Bottero, Alessandro G., et al.
Published: (2024)

Machine Learning as Iterated Belief Change a la Darwiche and Pearl
by: Aravanis, Theofanis
Published: (2025)

Collapsed Inference for Bayesian Deep Learning
by: Zeng, Zhe, et al.
Published: (2023)

Geometric Scaling of Bayesian Inference in LLMs
by: Agarwal, Naman, et al.
Published: (2025)

On Sequential Bayesian Inference for Continual Learning
by: Kessler, Samuel, et al.
Published: (2023)

Bayesian Inference of Training Dataset Membership
by: Huang, Yongchao
Published: (2025)

When Test-Time Guidance Is Enough: Fast Image and Video Editing with Diffusion Guidance
by: Ghorbel, Ahmed, et al.
Published: (2026)

ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized Experiments
by: Cha, Taehun, et al.
Published: (2024)

Automatic Calibration for Membership Inference Attack on Large Language Models
by: Zade, Saleh Zare, et al.
Published: (2025)