ICML 2023 Accepted Papers

ICML 2023
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July 31, 2023

This year marks the fortieth International Conference on Machine Learning (ICML).  Supported by the International Machine Learning Society, ICML is one of the biggest, and most prestigious, academic conferences for artificial intelligence.  This year’s conference took place from July 23rd through 29th at the Hawaii Convention Center, as well as virtually.  This year CAIDA had ten members with papers accepted for ICML.  A big congratulations to our members and their teams for their success! You can see the CAIDA papers below, and the remainder of ICML’s selections here.

Bahare Fatemi, Perouz Taslakian, David Vazquez, David Poole

Knowledge Hypergraph Embedding Meets Relational Algebra


Devon Graham, Kevin Leyton-Brown, Tim Roughgarden

Formalizing Preferences Over Runtime Distributions


Brian Irwin, Eldad Haber, Raviv Gal, Avi Ziv

Neural Network Accelerated Implicit Filtering: Integrating Neural Network Surrogates With Provably Convergent Derivative Free Optimization Methods


Jonathan Lavington, Sharan Vaswani, Reza Babanezhad, Mark Schmidt, Nicolas Le Roux

Target-based Surrogates for Stochastic Optimization


Xiaoxiao Li, Zhao Song, Jiaming Yang

Federated Adversarial Learning: A Framework with Convergence Analysis


Wu Lin, Valentin Duruisseaux, Melvin Leok, Frank Nielsen, Khan Emtiyaz, Mark Schmidt

Simplifying Momentum-based Positive-definite Submanifold Optimization with Applications to Deep Learning


Mohamad Amin Mohamadi, Won Bae, Danica J Sutherland

A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel


Andreas Munk, Alexander Mead, Frank Wood

Uncertain Evidence in Probabilistic Models and Stochastic Simulators


Julie Nutini, Issam Laradji, Mark Schmidt

Let's Make Block Coordinate Descent Converge Faster: Faster Greedy Rules, Message-Passing, Active-Set Complexity, and Superlinear Convergence


Samet Oymak, Ankit Singh Rawat, Mahdi Soltanolkotabi, Christos Thrampoulidis

On the Role of Attention in Prompt-tuning


Hamed Shirzad, Ameya Velingker, Balaji Venkatachalam, Danica J Sutherland, Ali K Sinop

Exphormer: Sparse Transformers for Graphs


Christian Weilbach, William Harvey, Frank Wood

Graphically Structured Diffusion Models


Zuheng Xu, Naitong Chen, Trevor Campbell

MixFlows: principled variational inference via mixed flows



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

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