Hunter Lang

Han Guo

Yoon Kim

CoRR, February, 2025

When One LLM Drools, Multi-LLM Collaboration Rules.

[BibT_eX]

[DOI]

CoRR, February, 2025

2024

Theoretical Analysis of Weak-to-Strong Generalization.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Learning to Decode Collaboratively with Multiple Language Models.

[BibT_eX]

[DOI]

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Who Should Predict? Exact Algorithms For Learning to Defer to Humans.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

TabLLM: Few-shot Classification of Tabular Data with Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Large Language Models are Zero-Shot Clinical Information Extractors.

[BibT_eX]

[DOI]

CoRR, 2022

Training Subset Selection for Weak Supervision.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Co-training Improves Prompt-based Learning for Large Language Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2022

Large language models are few-shot clinical information extractors.

[BibT_eX]

[DOI]

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Leveraging Time Irreversibility with Order-Contrastive Pre-training.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning.

[BibT_eX]

[DOI]

Hoifung Poon

Hai Wang

Proceedings of the Neuro-Symbolic Artificial Intelligence: The State of the Art, 2021

Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning.

[BibT_eX]

[DOI]

Hoifung Poon

Hai Wang

CoRR, 2021

Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch).

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances.

[BibT_eX]

[DOI]

Aravind Reddy

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic.

[BibT_eX]

[DOI]

Hoifung Poon

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Graph cuts always find a global optimum (with a catch).

[BibT_eX]

[DOI]

CoRR, 2020

Statistical Adaptive Stochastic Gradient Methods.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Using Statistics to Automate Stochastic Optimization.

[BibT_eX]

[DOI]

Lin Xiao

Pengchuan Zhang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Understanding the Role of Momentum in Stochastic Gradient Methods.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Block Stability for MAP Inference.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Optimality of Approximate Inference Algorithms on Stable Instances.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Alpha-expansion is Exact on Stable Instances.

[BibT_eX]

[DOI]