Vector quantization induces a structured partition of the representation space for composing heterogeneous multiclass calibration maps via shared codeword-dependent Dirichlet factors.
Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods.Advances in large margin classifiers, 10(3):61–74
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Task calibration aligns LLM distributions in latent task spaces to make MBR decoding provably optimal and improve generation quality.
Mochi aligns pre-training with inference via meta-learning for efficient graph foundation models, matching or exceeding prior models on 25 datasets with 8-27x less training time.
GrACE is a fine-tuned generative method that uses similarity to a special token embedding for real-time calibrated confidence in LLMs and enables efficient confidence-based test-time scaling.
A supervision construction procedure generates explicit support and controlled non-support examples (counterfactual and topic-related negatives) without manual annotation, producing verifiers that demonstrate genuine evidence dependence in radiology tasks.
citing papers explorer
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Divide et Calibra: Multiclass Local Calibration via Vector Quantization
Vector quantization induces a structured partition of the representation space for composing heterogeneous multiclass calibration maps via shared codeword-dependent Dirichlet factors.
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Task-Aware Calibration: Provably Optimal Decoding in LLMs
Task calibration aligns LLM distributions in latent task spaces to make MBR decoding provably optimal and improve generation quality.
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Mochi: Aligning Pre-training and Inference for Efficient Graph Foundation Models via Meta-Learning
Mochi aligns pre-training with inference via meta-learning for efficient graph foundation models, matching or exceeding prior models on 25 datasets with 8-27x less training time.
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GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models
GrACE is a fine-tuned generative method that uses similarity to a special token embedding for real-time calibrated confidence in LLMs and enables efficient confidence-based test-time scaling.
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Case-Grounded Evidence Verification: A Framework for Constructing Evidence-Sensitive Supervision
A supervision construction procedure generates explicit support and controlled non-support examples (counterfactual and topic-related negatives) without manual annotation, producing verifiers that demonstrate genuine evidence dependence in radiology tasks.