Introduces bounded discrete graphical models and the BRIDGE regularized score matching estimator with nonasymptotic error bounds and exact support recovery for high-dimensional discrete data.
arXiv preprint arXiv:1910.01619 , year=
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Establishes convergence guarantees for overparameterized 2-layer ReLU networks in flow matching, generalization bounds for the velocity-field objective, and Wasserstein guarantees for generated samples, using multi-task representation learning bounds.
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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Estimation of High Dimensional Bounded Discrete Graphical Models via Regularized Generalized Score Matching
Introduces bounded discrete graphical models and the BRIDGE regularized score matching estimator with nonasymptotic error bounds and exact support recovery for high-dimensional discrete data.
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A Theory on Flow Matching with Neural Networks
Establishes convergence guarantees for overparameterized 2-layer ReLU networks in flow matching, generalization bounds for the velocity-field objective, and Wasserstein guarantees for generated samples, using multi-task representation learning bounds.
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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.