Publications

DITTO: Diffusion Inference-Time T-Optimization for Music Generation
Zachary Novack, Julian McAuley, Taylor Berg-Kirkpatrick, Nicholas J. Bryan
Paper / Website / Summary / BibTex

This work presents DITTO, a novel method for controlling pretrained text-to-music diffusion models without any model finetuning, enabling a wide range of artistic editing and control tasks.

@misc{Novack2024Ditto,
title={DITTO: Diffusion Inference-Time T-Optimization for Music Generation},
author={Novack, Zachary and McAuley, Julian and Berg-Kirkpatrick, Taylor and Bryan, Nicholas J.},
year={2024},
eprint={2401.12179},
archivePrefix={arXiv},
primaryClass={cs.SD}}

Unsupervised Lead Sheet Generation via Semantic Compression
Zachary Novack, Nikita Srivatsan, Taylor Berg-Kirkpatrick, Julian McAuley
Paper / Code / Summary / BibTex

This work presents a novel method Lead-AE for conditional lead sheet generation, leveraging a discrete auto-encoder setup that allows for fully unsupervised training.

@misc{novack2023unsupervised,
title={Unsupervised Lead Sheet Generation via Semantic Compression},
author={Novack, Zachary and Srivatsan, Nikita and Berg-Kirkpatrick, Taylor and McAuley, Julian},
year={2023},
eprint={2310.10772},
archivePrefix={arXiv},}

CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets
Zachary Novack, Julian McAuley, Zachary Lipton, Saurabh Garg
International Conference on Machine Learning (ICML), 2023
1st Workshop on Multimodal Representation Learning, ICLR, 2023
Paper / Code / Summary / BibTex

This work introduces CHiLS, a strategy for zero-shot classification to improve CLIP-like models that focuses on improving class names and utilizes implicit semantic hierarchies to enhance accuracy without requiring additional training.

@inproceedings{novack2023chils,
title={CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets},
author={Novack, Zachary and McAuley, Julian and Lipton, Zachary and Garg, Saurabh},
year={2023},
booktitle={International Conference on Machine Learning (ICML)}, }

Disentangling the Mechanisms Behind Implicit Regularization in SGD
Zachary Novack, Simran Kaur, Tanya Marwah, Saurabh Garg, Zachary Lipton
International Conference on Learning Representations (ICLR), 2023
Spotlight at NeurIPS Workshop on The Benefits of Higher-Order Optimization in Machine Learning, 2022
Paper / Code / Summary / BibTex

This work presents a comparative empirical study of different gradient norm-based regularizers for improving Large-Batch Stochastic Gradient Descent, and ties this behavior to the trajectory of the micro-batch gradient norm during training.

@inproceedings{novack2023disentangling,
title={Disentangling the Mechanisms Behind Implicit Regularization in SGD},
author={Novack, Zachary and Kaur, Simran and Marwah, Tanya and Garg, Saurabh and Lipton, Zachary},
booktitle={International Conference on Learning Representations (ICLR)},
year={2023} }

Personalized Sequential Recommendation for Adaptive Itemization in MOBA Games
Zachary Novack
Paper / Summary / BibTex

This paper adapts the state-of-the-art work HT4-Rec for personalized sequential item recommendation in the popular video game League of Legends, incorporating user skill information to directly capture personal player ability.

@misc{novack2022personalized,
title={Personalized Sequential Recommendation for Adaptive Itemization in MOBA Games},
author={Novack, Zachary},
year={2022}}

Towards Generalizable Deep Speech Anonymization
Aaron Broukhim, Zachary Novack
Paper / Summary / BibTex

This work presents a method for learning language-agnostic speech anonymization systems, utilizing a GAN backbone and modified loss to actively learn an anonymized speaker distribution.

@misc{broukhim2022towards,
title={Towards Generalizable Deep Speech Anonymization},
author={Broukhim, Aaron and Novack, Zachary},
year={2022}}

Down the Rabbit Hole: Modeling Twitter Dynamics through Bayesian Inference
Zachary Novack
Paper / Summary / BibTex

This work presents an analytical study into addiction effects on Twitter, and uncovers the presence of different posting types that obey fundamentally chaotic behavior when modeled using an autoregressive Bayesian framework.

@article{novack2022down,
title={Down the Rabbit Hole: Modeling Twitter Dynamics through Bayesian Inference},
author={Novack, Zachary},
doi={"10.1184/R1/20638989.v1"},
year={2022}}

Approximating Optimal Transport via GANs for Recourse Disparity Analysis
Zachary Novack, Qi Xuan Teo, Ryan Steed
Paper / Summary / BibTex

This work presents a method for analyzing the recourse disparity across protected groups in algorithmic decision making systems, using GANs to approximate the optimal transport mapping between recourse-aligned subgroups.

@misc{novack2022approximating,
title={Approximating Optimal Transport via GANs for Recourse Disparity Analysis},
author={Novack, Zachary and Teo, Qi Xuan and Steed, Ryan},
year={2022}}

Lunch at the EigenSalad Bar: Linear Approaches to Dimensionality Reduction for Image Processing
Zachary Novack
Paper / Summary / BibTex

This pseudo-satirical work investigated linear dimensionality reduction techniques for processing images of food, attempting to find explainable bases for discrimintative food categories.

@misc{novack2021salad,
title={Lunch at the EigenSalad Bar: Linear Approaches to Dimensionality Reduction for Image Processing},
author={Novack, Zachary},
year={2021}}