Summary of Fine-grained Dynamic Framework For Bias-variance Joint Optimization on Data Missing Not at Random, by Mingming Ha et al.
Fine-Grained Dynamic Framework for Bias-Variance Joint Optimization on Data Missing Not at Randomby Mingming Ha,…
Summary of Is Algorithmic Stability Testable? a Unified Framework Under Computational Constraints, by Yuetian Luo and Rina Foygel Barber
Is Algorithmic Stability Testable? A Unified Framework under Computational Constraintsby Yuetian Luo, Rina Foygel BarberFirst…
Summary of Towards Better Understanding Of In-context Learning Ability From In-context Uncertainty Quantification, by Shang Liu et al.
Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantificationby Shang Liu, Zhongze Cai,…
Summary of Quantifying the Gain in Weak-to-strong Generalization, by Moses Charikar et al.
Quantifying the Gain in Weak-to-Strong Generalizationby Moses Charikar, Chirag Pabbaraju, Kirankumar ShiragurFirst submitted to arxiv…
Summary of What Variables Affect Out-of-distribution Generalization in Pretrained Models?, by Md Yousuf Harun et al.
What Variables Affect Out-of-Distribution Generalization in Pretrained Models?by Md Yousuf Harun, Kyungbok Lee, Jhair Gallardo,…
Summary of Ceebert: Cross-domain Inference in Early Exit Bert, by Divya Jyoti Bajpai and Manjesh Kumar Hanawal
CEEBERT: Cross-Domain Inference in Early Exit BERTby Divya Jyoti Bajpai, Manjesh Kumar HanawalFirst submitted to…
Summary of Bimix: a Bivariate Data Mixing Law For Language Model Pretraining, by Ce Ge et al.
BiMix: A Bivariate Data Mixing Law for Language Model Pretrainingby Ce Ge, Zhijian Ma, Daoyuan…
Summary of Mallowspo: Fine-tune Your Llm with Preference Dispersions, by Haoxian Chen et al.
MallowsPO: Fine-Tune Your LLM with Preference Dispersionsby Haoxian Chen, Hanyang Zhao, Henry Lam, David Yao,…
Summary of A Rescaling-invariant Lipschitz Bound Based on Path-metrics For Modern Relu Network Parameterizations, by Antoine Gonon et al.
A rescaling-invariant Lipschitz bound based on path-metrics for modern ReLU network parameterizationsby Antoine Gonon, Nicolas…
Summary of Analysis Of Atom-level Pretraining with Quantum Mechanics (qm) Data For Graph Neural Networks Molecular Property Models, by Jose Arjona-medina and Ramil Nugmanov
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property…