CDS Accepted Papers at NeurIPS 2022
We’re excited to announce that many CDS faculty, researchers, and students will present at the upcoming thirty-sixth NeurIPS (Neural Information Processing Systems) Conference, taking place Monday, 11/28 through Friday, 12/9. The conference began in 1987 and focuses on the advancement of research in AI and machine learning. This year’s event will take place in a hybrid format — the first week will occur in-person at the New Orleans Ernest N. Morial Convention Center, and the second week will be virtual. Below is a comprehensive list of projects and their corresponding CDS participants (indicated in bold with asterisk in front of it):
“A Data-Augmentation Is Worth A Thousand Samples” – Randall Balestriero · Ishan Misra · *Yann LeCun
“Are All Losses Created Equal: A Neural Collapse Perspective” – Jinxin Zhou · Chong You · Xiao Li · *Kangning Liu · *Sheng Liu · Qing Qu · Zhihui Zhu
“Asymptotics of smoothed Wasserstein distances in the small noise regime” – Yunzi Ding · *Jonathan Niles-Weed
“Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers” – Wanqian Yang · *Polina Kirichenko · *Micah Goldblum · *Andrew Wilson
“Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone” – Zi-Yi Dou · *Aishwarya Kamath · Zhe Gan · Pengchuan Zhang · Jianfeng Wang · Linjie Li · Zicheng Liu · Ce Liu · *Yann LeCun · Nanyun Peng · Jianfeng Gao · Lijuan Wang
“Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods” – Randall Balestriero · *Yann LeCun
“Distributional Convergence of the Sliced Wasserstein Process” – Jiaqi Xi · *Jonathan Niles-Weed
“Dungeons and Data: A Large-Scale NetHack Dataset” – Eric Hambro · Roberta Raileanu · *Danielle Rothermel · Vegard Mella · Tim Rocktäschel · Heinrich Küttler · Naila Murray
“Exponential Separations in Symmetric Neural Networks” – Aaron Zweig · *Joan Bruna
“Generative multitask learning mitigates target-causing confounding” — *Taro Makino · *Krzysztof Geras · *Kyunghyun Cho
“Learning single-index models with shallow neural networks” – *Alberto Bietti · *Joan Bruna · Clayton Sanford · Min Jae Song
“Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors” — Eric Wu · Alexander Sher · Alan Litke · *Eero Simoncelli · E.J. Chichilnisky · Nora Brackbill
“On Feature Learning in the Presence of Spurious Correlations” — Pavel Izmailov · *Polina Kirichenko · Nate Gruver · *Andrew Wilson
“On Non-Linear operators for Geometric Deep Learning” – Grégoire Sergeant-Perthui · Jakob Maier · *Joan Bruna · Edouard Oyallon
“On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification” — *Sanyam Kapoor · *Wesley Maddox · Pavel Izmailov · *Andrew Wilson
“PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization” — *Sanae Lotfi · *Sanyam Kapoor · Marc Finzi · *Andres Potapczynski · *Micah Goldblum · *Andrew Wilson
“Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors” — *Ravid Shwartz-Ziv · *Micah Goldblum · Hossein Souri · Sanyam Kapoor · Chen Zhu · *Yann LeCun · *Andrew Wilson
“projUNN: efficient method for training deep networks with unitary matrices” – Bobak Kiani · Randall Balestriero · *Yann LeCun · Seth Lloyd
“SeqPATE: Differentially Private Text Generation via Knowledge Distillation” — Zhiliang Tian · Yingxiu Zhao · Ziyue Huang · Yu-Xiang Wang · Nevin L. Zhang · *He He
“StrokeRehab: A Benchmark Dataset for Sub-second Action Identification” — *Aakash Kaku · *Kangning Liu · Avinash Parnandi · *Haresh Rengaraj Rajamohan · *Kannan Venkataramanan · Anita Venkatesan · Audre Wirtanen · Natasha Pandit · Heidi Schambra · *Carlos Fernandez-Granda
“The Effects of Regularization and Data Augmentation are Class Dependent” – Randall Balestriero · Leon Bottou · *Yann LeCun
“Using natural language and program abstractions to instill human inductive biases in machines” — Sreejan Kumar · Carlos G. Correa · Ishita Dasgupta · Raja Marjieh · *Michael Y Hu · Robert Hawkins · Jonathan D Cohen · Nathaniel Daw · Karthik Narasimhan · Tom Griffiths
“VICRegL: Self-Supervised Learning of Local Visual Features” – Adrien Bardes · *Jean Ponce · *Yann LeCun
“What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?” – *Nikolaos Tsilivis · *Julia Kempe
“When does return-conditioned supervised learning work for offline reinforcement learning?”– David Brandfonbrener · *Alberto Bietti · Jacob Buckman · Romain Laroche · *Joan Bruna
The conference’s preliminary presentation schedule can be found on the NeurIPS Schedule Overview page.
