A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.
Journal of machine learning research , volume=
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Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
SPIN lets weak LLMs become strong by self-generating training data from previous model versions and training to prefer human-annotated responses over its own outputs, outperforming DPO even with extra GPT-4 data on benchmarks.
citing papers explorer
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A Unified Theory of Conditional Coverage in Conformal Prediction with Applications
A unified framework derives non-asymptotic bounds on conditional miscoverage in conformal prediction via pointwise and L_p routes and gives a common view of existing methods.
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Structure from Strategic Interaction & Uncertainty: Risk Sensitive Games for Robust Preference Learning
Risk-sensitive preference games using convex risk measures produce policies that are robust across data strata and match or exceed standard Nash learning performance without added cost.
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Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
SPIN lets weak LLMs become strong by self-generating training data from previous model versions and training to prefer human-annotated responses over its own outputs, outperforming DPO even with extra GPT-4 data on benchmarks.