John Thickstun

I am a Postdoctoral Scholar at Stanford, advised by Percy Liang. Previously I completed a doctorate in the Allen School of Computer Science & Engineering at the University of Washington, where I was co-advised by Sham Kakade and Zaid Harchaoui. I completed my undergraduate degree in Applied Mathematics at Brown University, where I was advised by Eugene Charniak and Björn Sandstede. I work on machine learning, with a current focus on controllable generative modeling. I am also interested in applications of these methods to solve artificial intelligence problems in areas including audio, language, and music. My research has been supported by a 2017 NSF graduate fellowship, and a 2020 Qualcomm innovation fellowship.

I am on the job market this year (2022-2023)! I am broadly interested in academic faculty and industry research positions.

My CV is available here.

The MusicNet dataset has moved to permanent hosting at Zenodo.

selected publications

Neurips Oral Presentation

Diffusion-LM improves controllable text generation

Li, Xiang Lisa, Thickstun, John, Gulrajani, Ishaan, Liang, Percy, and Hashimoto, Tatsunori B.

In Advances in Neural Information Processing Systems 2022

Abs arXiv Code Slides

Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic structure). To address this challenge, we develop a new non-autoregressive language model based on continuous diffusions that we call Diffusion-LM. Building upon the recent successes of diffusion models in continuous domains, Diffusion-LM iteratively denoises a sequence of Gaussian vectors into word vectors, yielding a sequence of intermediate latent variables. The continuous, hierarchical nature of these intermediate variables enables a simple gradient-based algorithm to perform complex, controllable generation tasks. We demonstrate successful control of Diffusion-LM for six challenging fine-grained control tasks, significantly outperforming prior work.
Dissertation

Leveraging generative models for music and signal processing

Thickstun, John

University of Washington 2021

PDF
Neurips Outstanding Paper

MAUVE: measuring the gap between neural text and human text using divergence frontiers

Pillutla, Krishna, Swayamdipta, Swabha, Zellers, Rowan, Thickstun, John, Welleck, Sean, Choi, Yejin, and Harchaoui, Zaid

In Advances in Neural Information Processing Systems 2021

Abs arXiv Code

As major progress is made in open-ended text generation, measuring how close machine-generated text is to human language remains a critical open problem. We introduce MAUVE, a comparison measure for open-ended text generation, which directly compares the learnt distribution from a text generation model to the distribution of human-written text using divergence frontiers. MAUVE scales up to modern text generation models by computing information divergences in a quantized embedding space. Through an extensive empirical study on three open-ended generation tasks, we find that MAUVE identifies known properties of generated text, scales naturally with model size, and correlates with human judgments, with fewer restrictions than existing distributional evaluation metrics.
ICML

Parallel and flexible sampling from autoregressive models via langevin dynamics

Jayaram, Vivek, and Thickstun, John

In International Conference on Machine Learning 2021

Abs arXiv Code Poster Website

This paper introduces an alternative approach to sampling from autoregressive models. Autoregressive models are typically sampled sequentially, according to the transition dynamics defined by the model. Instead, we propose a sampling procedure that initializes a sequence with white noise and follows a Markov chain defined by Langevin dynamics on the global log-likelihood of the sequence. This approach parallelizes the sampling process and generalizes to conditional sampling. Using an autoregressive model as a Bayesian prior, we can steer the output of a generative model using a conditional likelihood or constraints. We apply these techniques to autoregressive models in the visual and audio domains, with competitive results for audio source separation, super-resolution, and inpainting.
ICML

Source separation with deep generative priors

Jayaram, Vivek, and Thickstun, John

In International Conference on Machine Learning 2020

Abs arXiv Code Poster

Despite substantial progress in signal source separation, results for richly structured data continue to contain perceptible artifacts. In contrast, recent deep generative models can produce authentic samples in a variety of domains that are indistinguishable from samples of the data distribution. This paper introduces a Bayesian approach to source separation that uses generative models as priors over the components of a mixture of sources, and noise-annealed Langevin dynamics to sample from the posterior distribution of sources given a mixture. This decouples the source separation problem from generative modeling, enabling us to directly use cutting-edge generative models as priors. The method achieves state-of-the-art performance for MNIST digit separation. We introduce new methodology for evaluating separation quality on richer datasets, providing quantitative evaluation of separation results on CIFAR-10. We also provide qualitative results on LSUN.
ICASSP Oral Presentation

Invariances and data augmentation for supervised music transcription

Thickstun, John, Harchaoui, Zaid, Foster, Dean P, and Kakade, Sham M

In International Conference on Acoustics, Speech and Signal Processing 2018

Abs arXiv Code

This paper explores a variety of models for frame-based music transcription, with an emphasis on the methods needed to reach state-of-the-art on human recordings. The translation-invariant network discussed in this paper, which combines a traditional filterbank with a convolutional neural network, was the top-performing model in the 2017 MIREX Multiple Fundamental Frequency Estimation evaluation. This class of models shares parameters in the log-frequency domain, which exploits the frequency invariance of music to reduce the number of model parameters and avoid overfitting to the training data. All models in this paper were trained with supervision by labeled data from the MusicNet dataset, augmented by random label-preserving pitch-shift transformations.
ICLR

Learning features of music from scratch

Thickstun, John, Harchaoui, Zaid, and Kakade, Sham M

In International Conference on Learning Representations 2017

Abs arXiv Code Poster Website

This paper introduces a new large-scale music dataset, MusicNet, to serve as a source of supervision and evaluation of machine learning methods for music research. MusicNet consists of hundreds of freely-licensed classical music recordings by 10 composers, written for 11 instruments, together with instrument/note annotations resulting in over 1 million temporal labels on 34 hours of chamber music performances under various studio and microphone conditions. The paper defines a multi-label classification task to predict notes in musical recordings, along with an evaluation protocol, and benchmarks several machine learning architectures for this task: i) learning from spectrogram features; ii) end to-end learning with a neural net; iii) end-to-end learning with a convolutional neural net. These experiments show that end-to-end models trained for note prediction learn frequency selective filters as a low-level representation of audio.