Learning Features of Music From Scratch. - John Thickstun; Zaïd Harchaoui; Sham M. Kakade (ICLR (Poster), 2017)

Invariances and Data Augmentation for Supervised Music Transcription. - John Thickstun; Zaïd Harchaoui; Dean P. Foster; Sham M. Kakade (ICASSP, 2018)

Coupled Recurrent Models for Polyphonic Music Composition. - John Thickstun; Zaïd Harchaoui; Dean P. Foster; Sham M. Kakade (ISMIR, 2019)

Convolutional Composer Classification. - Harsh Verma; John Thickstun (ISMIR, 2019)

An Information Bottleneck Approach for Controlling Conciseness in Rationale Extraction. - Bhargavi Paranjape; Mandar Joshi; John Thickstun; Hannaneh Hajishirzi; Luke Zettlemoyer (EMNLP (1), 2020)

Source Separation with Deep Generative Priors. - Vivek Jayaram; John Thickstun (ICML, 2020)

Rethinking Evaluation Methodology for Audio-to-Score Alignment. - John Thickstun; Jennifer Brennan; Harsh Verma (CoRR, 2020)

Parallel and Flexible Sampling from Autoregressive Models via Langevin Dynamics. - Vivek Jayaram; John Thickstun (ICML, 2021)

Faster Policy Learning with Continuous-Time Gradients. - Samuel K. Ainsworth; Kendall Lowrey; John Thickstun; Zaïd Harchaoui; Siddhartha S. Srinivasa (L4DC, 2021)

MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers. - Krishna Pillutla; Swabha Swayamdipta; Rowan Zellers; John Thickstun; Sean Welleck; Yejin Choi 001; Zaïd Harchaoui (NeurIPS, 2021)

MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. - Krishna Pillutla; Swabha Swayamdipta; Rowan Zellers; John Thickstun; Yejin Choi 001; Zaïd Harchaoui (CoRR, 2021)

Leveraging Generative Models for Music and Signal Processing. - John Thickstun (2021)

Melody transcription via generative pre-training. - Chris Donahue; John Thickstun; Percy Liang (ISMIR, 2022)

Diffusion-LM Improves Controllable Text Generation. - Xiang Lisa Li; John Thickstun; Ishaan Gulrajani; Percy Liang; Tatsunori B. Hashimoto (NeurIPS, 2022)

Backpack Language Models. - John Hewitt; John Thickstun; Christopher D. Manning; Percy Liang (ACL (1), 2023)

MAUVE Scores for Generative Models: Theory and Practice. - Krishna Pillutla; Lang Liu; John Thickstun; Sean Welleck; Swabha Swayamdipta; Rowan Zellers; Sewoong Oh; Yejin Choi 001; Zaïd Harchaoui (J. Mach. Learn. Res., 2023)

Evaluating Human-Language Model Interaction. - Mina Lee 002; Megha Srivastava; Amelia Hardy; John Thickstun; Esin Durmus; Ashwin Paranjape; Ines Gerard-Ursin; Xiang Lisa Li; Faisal Ladhak; Frieda Rong; Rose E. Wang; Minae Kwon; et al. (Trans. Mach. Learn. Res., 2023)

Constrained Diffusion Implicit Models. - Vivek Jayaram; Ira Kemelmacher-Shlizerman; Steven M. Seitz; John Thickstun (CoRR, 2024)

Robust Distortion-free Watermarks for Language Models. - Rohith Kuditipudi; John Thickstun; Tatsunori Hashimoto; Percy Liang (Trans. Mach. Learn. Res., 2024)

Anticipatory Music Transformer. - John Thickstun; David Leo Wright Hall; Chris Donahue; Percy Liang (Trans. Mach. Learn. Res., 2024)

Hookpad Aria: A Copilot for Songwriters. - Chris Donahue; Shih-Lun Wu; Yewon Kim; Dave B. Carlton; Ryan H. Miyakawa; John Thickstun (CoRR, 2025)

Aligning Text-to-Music Evaluation with Human Preferences. - Yichen Huang; Zachary Novack; Koichi Saito; Jiatong Shi; Shinji Watanabe 001; Yuki Mitsufuji; John Thickstun; Chris Donahue (CoRR, 2025)

Esoteric Language Models. - Subham Sekhar Sahoo; Zhihan Yang; Yash Akhauri; Johnna Liu; Deepansha Singh; Zhoujun Cheng; Zhengzhong Liu 001; Eric Xing; John Thickstun; Arash Vahdat (CoRR, 2025)