NeurIPS 2022 Accepted Papers

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September 28, 2022

This year marks the 36th annual Conference on Neural Information Processing Systems (NeurIPS): a workshop and conference hosted by the Neural Information Processing Systems Foundation that celebrates the work being done in artificial intelligence and machine learning and promotes the exchange of research advances. The conference will take place from November 28th through December 9th and will be a Hybrid Conference with a physical component at the New Orleans Convention Center during the first week, and a virtual component the second week. This year fourteen of CAIDA's members have been featured with a total of 20 papers accepted.  You can see a list of our members’ papers below, and you can find out more about this year’s conference here.


Bowen Baker · Ilge Akkaya · Peter Zhokov · Joost Huizinga · Jie Tang · Adrien Ecoffet · Brandon Houghton · Raul Sampedro · Jeff Clune

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos


Naitong Chen · Zuheng Xu · Trevor Campbell

Bayesian inference via sparse Hamiltonian flows


Setareh Cohan · Nam Hee Kim · David Rolnick · Michiel van de Panne

Understanding the Evolution of Linear Regions in Deep Reinforcement Learning


William Harvey · Saeid Naderiparizi · Vaden Masrani · Christian Weilbach · Frank Wood

Flexible Diffusion Modeling of Long Videos


Xingzhe He · Bastian Wandt · Helge Rhodin

AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints


Yuhe Jin · Weiwei Sun · Jan Hosang · Eduard Trulls · Kwang Moo Yi

TUSK: Task-Agnostic Unsupervised Keypoints


Siddhesh Khandelwal · Leonid Sigal

Iterative Scene Graph Generation


Jiachang Liu · Chudi Zhong · Boxuan Li · Margo Seltzer · Cynthia Rudin

FasterRisk: Fast and Accurate Interpretable Risk Scores


Mohamad Amin Mohamadi · Wonho Bae · Danica J. Sutherland

Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels


Cian Naik · Judith Rousseau · Trevor Campbell

Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement


DOU QI · Konstantinos Kamnitsas · Yuankai Huo · Xiaoxiao Li · Daniel Moyer · Danielle Pace · Jonas Teuwen · Islem Rekik

Medical Imaging meets NeurIPS


Rindra Ramamonjison · Amin Banitalebi-Dehkordi · Giuseppe Carenini · Bissan Ghaddar · Timothy Yu · Zirui Zhou · Yong Zhang

NL4Opt: Formulating Optimization Problems Based on Their Natural Language Descriptions


Ali Seyfi · Jean-Francois Rajotte · Raymond Ng

Group GAN


Hamed Shirzad · Kaveh Hassani · Danica J. Sutherland

Evaluating Graph Generative Models with Contrastively Learned Features


Haoyuan Sun · Kwangjun Ahn · Christos Thrampoulidis · Navid Azizan

Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently


Nikola Surjanovic · Saifuddin Syed · Alexandre Bouchard-Côté · Trevor Campbell

Parallel Tempering With a Variational Reference


Christos Thrampoulidis · Ganesh Ramachandra Kini · Vala Vakilian · Tina Behnia

Imbalance Trouble: Revisiting Neural-Collapse Geometry


Rui Xin · Chudi Zhong · Zhi Chen · Takuya Takagi · Margo Seltzer · Cynthia Rudin

Exploring the Whole Rashomon Set of Sparse Decision Trees


Jinsoo Yoo · Frank Wood

BayesPCN: A Continually Learnable Predictive Coding Associative Memory


Lijia Zhou · Frederic Koehler · Pragya Sur · Danica J. Sutherland · Nati Srebro

A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models




Note: All CAIDA Members have been bolded and a link has been provided to their personal webpages

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