| 2025 | ICML | An analytic theory of creativity in convolutional diffusion models. | Mason Kamb, Surya Ganguli |
| 2025 | ICML | Features are fate: a theory of transfer learning in high-dimensional regression. | Javan Tahir, Surya Ganguli, Grant M. Rotskoff |
| 2023 | ICLR | The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks. | Daniel Kunin, Atsushi Yamamura, Chao Ma, Surya Ganguli |
| 2023 | ICLR | Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? | Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite |
| 2023 | ICLR | Disentanglement with Biological Constraints: A Theory of Functional Cell Types. | James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy Behrens |
| 2022 | ICLR | MetaMorph: Learning Universal Controllers with Transformers. | Agrim Gupta, Linxi Fan, Surya Ganguli, Li Fei-Fei |
| 2022 | ICLR | How many degrees of freedom do we need to train deep networks: a loss landscape perspective. | Brett W. Larsen, Stanislav Fort, Nic Becker, Surya Ganguli |
| 2021 | ICLR | Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics. | Daniel Kunin, Javier Sagastuy-Brea, Surya Ganguli, Daniel L. K. Yamins, Hidenori Tanaka |
| 2021 | ICML | A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions. | Gabriel Mel, Surya Ganguli |
| 2021 | ICML | Understanding self-supervised learning dynamics without contrastive pairs. | Yuandong Tian, Xinlei Chen, Surya Ganguli |
| 2020 | EMNLP | RNNs can generate bounded hierarchical languages with optimal memory. | John Hewitt, Michael Hahn, Surya Ganguli, Percy Liang, Christopher D. Manning |
| 2020 | ICML | Two Routes to Scalable Credit Assignment without Weight Symmetry. | Daniel Kunin, Aran Nayebi, Javier Sagastuy-Brea, Surya Ganguli, Jonathan M. Bloom, Daniel Yamins |
| 2019 | ICLR | An analytic theory of generalization dynamics and transfer learning in deep linear networks. | Andrew K. Lampinen, Surya Ganguli |
| 2019 | ICLR | A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. | Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stphane Deny |
| 2018 | AISTATS | The emergence of spectral universality in deep networks. | Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli |
| 2017 | ICLR | Intelligent synapses for multi-task and transfer learning. | Ben Poole, Friedemann Zenke, Surya Ganguli |
| 2017 | ICLR | Deep Information Propagation. | Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein |
| 2017 | ICML | On the Expressive Power of Deep Neural Networks. | Maithra Raghu, Ben Poole, Jon M. Kleinberg, Surya Ganguli, Jascha Sohl-Dickstein |
| 2017 | ICML | Continual Learning Through Synaptic Intelligence. | Friedemann Zenke, Ben Poole, Surya Ganguli |
| 2015 | ICML | Deep Unsupervised Learning using Nonequilibrium Thermodynamics. | Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli |
| 2014 | ICML | Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. | Jascha Sohl-Dickstein, Ben Poole, Surya Ganguli |
| 2013 | CogSci | Learning hierarchical categories in deep neural networks. | Andrew M. Saxe, James L. McClelland, Surya Ganguli |