Cover Image

Know When to Abstain: Optimal Selective Classification with Likelihood Ratios

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Heng, Alvin, Soh, Harold
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Generative Modeling with Flow-Guided Density Ratio Learning
by: Heng, Alvin, et al.
Published: (2023)

Out-of-Distribution Detection with a Single Unconditional Diffusion Model
by: Heng, Alvin, et al.
Published: (2024)

Interpretable and Fair Mechanisms for Abstaining Classifiers
by: Lenders, Daphne, et al.
Published: (2025)

When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning
by: Hao, Chenjie, et al.
Published: (2026)

Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
by: Zhang, Bowen, et al.
Published: (2024)

Multi-Step Likelihood-Ratio Correction for Reinforcement Learning with Verifiable Rewards
by: Yoon, Deokgyu, et al.
Published: (2026)

Implicit Statistical Inference in Transformers: Approximating Likelihood-Ratio Tests In-Context
by: Chaudhry, Faris, et al.
Published: (2026)

Epistemic Deep Learning: Enabling Machine Learning Models to Know When They Do Not Know
by: Manchingal, Shireen Kudukkil
Published: (2025)

Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty
by: Machcha, Sravanthi, et al.
Published: (2026)

Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation
by: Baan, Joris, et al.
Published: (2026)

Conformal Inference under High-Dimensional Covariate Shifts via Likelihood-Ratio Regularization
by: Joshi, Sunay, et al.
Published: (2025)

Know What You Don't Know: Selective Prediction for Early Exit DNNs
by: Bajpai, Divya Jyoti, et al.
Published: (2025)

Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees
by: Gui, Yu, et al.
Published: (2024)

CaRT: Teaching LLM Agents to Know When They Know Enough
by: Liu, Grace, et al.
Published: (2025)

Approximated Likelihood Ratio: A Forward-Only and Parallel Framework for Boosting Neural Network Training
by: Zhang, Zeliang, et al.
Published: (2024)

Learning the Optimal Stopping for Early Classification within Finite Horizons via Sequential Probability Ratio Test
by: Ebihara, Akinori F., et al.
Published: (2025)

Base Models Know How to Reason, Thinking Models Learn When
by: Venhoff, Constantin, et al.
Published: (2025)

Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks
by: Feng, Zeyu, et al.
Published: (2024)

Don't Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion
by: Chen, Kaiqi, et al.
Published: (2024)

When In Doubt, Abstain: The Impact of Abstention on Strategic Classification
by: Alkarmi, Lina, et al.
Published: (2025)

Text Knows What, Tables Know When: Clinical Timeline Reconstruction via Retrieval-Augmented Multimodal Alignment
by: Kumar, Sayantan, et al.
Published: (2026)

When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
by: Zeng, Siliang, et al.
Published: (2023)

Anytime Optimal Decision Tree Learning with Continuous Features
by: Kiossou, Harold, et al.
Published: (2026)

Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
by: Persiianov, Mikhail, et al.
Published: (2024)

Class Incremental Learning via Likelihood Ratio Based Task Prediction
by: Lin, Haowei, et al.
Published: (2023)

When Models Know More Than They Say: Probing Analogical Reasoning in LLMs
by: McGovern, Hope, et al.
Published: (2026)

The Triangle of Similarity: A Multi-Faceted Framework for Comparing Neural Network Representations
by: Sirikova, Olha, et al.
Published: (2026)

A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio
by: Xi, Ningyuan, et al.
Published: (2024)

The Majority Vote Paradigm Shift: When Popular Meets Optimal
by: Purificato, Antonio, et al.
Published: (2025)

Predicting Classification Accuracy When Adding New Unobserved Classes
by: Slavutsky, Yuli, et al.
Published: (2020)

Know What You Don't Know: Uncertainty Calibration of Process Reward Models
by: Park, Young-Jin, et al.
Published: (2025)

Towards Universal Neural Likelihood Inference
by: Brahmavar, Shreyas Bhat, et al.
Published: (2025)

Half-order Fine-Tuning for Diffusion Model: A Recursive Likelihood Ratio Optimizer
by: Ren, Tao, et al.
Published: (2025)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
by: Liu, Vincent, et al.
Published: (2023)

AbstainGNN: Teaching Graph Neural Networks to Abstain for Graph Classification
by: Lin, Xixun, et al.
Published: (2026)

Bounding Evidence and Estimating Log-Likelihood in VAE
by: Struski, Łukasz, et al.
Published: (2022)

Demystifying the Optimal Fair Classifier in Multi-Class Classification
by: Zhang, Li, et al.
Published: (2026)

Deep Neural Network Benchmarks for Selective Classification
by: Pugnana, Andrea, et al.
Published: (2024)

On Evaluation of Unsupervised Feature Selection for Pattern Classification
by: Kim, Gyu-Il, et al.
Published: (2026)

Conjunction Subspaces Test for Conformal and Selective Classification
by: He, Zengyou, et al.
Published: (2024)