Alpha-expansion is Exact on Stable Instances. - Hunter Lang; David A. Sontag; Aravindan Vijayaraghavan (CoRR, 2017)

Optimality of Approximate Inference Algorithms on Stable Instances. - Hunter Lang; David A. Sontag; Aravindan Vijayaraghavan (AISTATS, 2018)

Block Stability for MAP Inference. - Hunter Lang; David A. Sontag; Aravindan Vijayaraghavan (AISTATS, 2019)

Understanding the Role of Momentum in Stochastic Gradient Methods. - Igor Gitman; Hunter Lang; Pengchuan Zhang; Lin Xiao (NeurIPS, 2019)

Using Statistics to Automate Stochastic Optimization. - Hunter Lang; Lin Xiao; Pengchuan Zhang (NeurIPS, 2019)

Statistical Adaptive Stochastic Gradient Methods. - Pengchuan Zhang; Hunter Lang; Qiang Liu 001; Lin Xiao (CoRR, 2020)

Graph cuts always find a global optimum (with a catch). - Hunter Lang; David A. Sontag; Aravindan Vijayaraghavan (CoRR, 2020)

Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic. - Hunter Lang; Hoifung Poon (AAAI, 2021)

Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances. - Hunter Lang; Aravind Reddy; David A. Sontag; Aravindan Vijayaraghavan (AISTATS, 2021)

Graph Cuts Always Find a Global Optimum for Potts Models (With a Catch). - Hunter Lang; David A. Sontag; Aravindan Vijayaraghavan (ICML, 2021)

Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning. - Hoifung Poon; Hai Wang; Hunter Lang (CoRR, 2021)

Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning. - Hoifung Poon; Hai Wang; Hunter Lang (Neuro-Symbolic Artificial Intelligence, 2021)

Leveraging Time Irreversibility with Order-Contrastive Pre-training. - Monica N. Agrawal; Hunter Lang; Michael Offin; Lior Gazit; David A. Sontag (AISTATS, 2022)

Large language models are few-shot clinical information extractors. - Monica Agrawal; Stefan Hegselmann; Hunter Lang; Yoon Kim; David A. Sontag (EMNLP, 2022)

Co-training Improves Prompt-based Learning for Large Language Models. - Hunter Lang; Monica N. Agrawal; Yoon Kim; David A. Sontag (ICML, 2022)

Training Subset Selection for Weak Supervision. - Hunter Lang; Aravindan Vijayaraghavan; David A. Sontag (NeurIPS, 2022)

Large Language Models are Zero-Shot Clinical Information Extractors. - Monica Agrawal; Stefan Hegselmann; Hunter Lang; Yoon Kim; David A. Sontag (CoRR, 2022)

TabLLM: Few-shot Classification of Tabular Data with Large Language Models. - Stefan Hegselmann; Alejandro Buendia; Hunter Lang; Monica Agrawal; Xiaoyi Jiang 001; David A. Sontag (AISTATS, 2023)

Who Should Predict? Exact Algorithms For Learning to Defer to Humans. - Hussein Mozannar; Hunter Lang; Dennis Wei; Prasanna Sattigeri; Subhro Das; David A. Sontag (AISTATS, 2023)

Learning to Decode Collaboratively with Multiple Language Models. - Zejiang Shen 001; Hunter Lang; Bailin Wang; Yoon Kim; David A. Sontag (ACL (1), 2024)

Theoretical Analysis of Weak-to-Strong Generalization. - Hunter Lang; David A. Sontag; Aravindan Vijayaraghavan (NeurIPS, 2024)

When One LLM Drools, Multi-LLM Collaboration Rules. - Shangbin Feng; Wenxuan Ding 001; Alisa Liu; Zifeng Wang 002; Weijia Shi; Yike Wang 002; Zejiang Shen 001; Xiaochuang Han; Hunter Lang; Chen-Yu Lee; Tomas Pfister; Yejin Choi 001; et al. (CoRR, 2025)

On the Duality between Gradient Transformations and Adapters. - Lucas Torroba Hennigen; Hunter Lang; Han Guo; Yoon Kim (CoRR, 2025)

PrefPalette: Personalized Preference Modeling with Latent Attributes. - Shuyue Stella Li; Melanie Sclar; Hunter Lang; Ansong Ni; Jacqueline He; Puxin Xu; Andrew Cohen; Chan Young Park; Yulia Tsvetkov; Asli Celikyilmaz (CoRR, 2025)