ICML

Publications

2023

Tim Dettmers, Luke Zettlemoyer
The case for 4-bit precision: k-bit Inference Scaling Laws. ICML (2023)
Tomoya Murata, Taiji Suzuki
DIFF2: Differential Private Optimization via Gradient Differences for Nonconvex Distributed Learning. ICML (2023)
Andrew Szot, Unnat Jain, Dhruv Batra, Zsolt Kira, Ruta Desai, Akshara Rai
Adaptive Coordination in Social Embodied Rearrangement. ICML (2023)
Roberto Molinaro, Yunan Yang, Björn Engquist, Siddhartha Mishra
Neural Inverse Operators for Solving PDE Inverse Problems. ICML (2023)
Hadi Salman, Alaa Khaddaj, Guillaume Leclerc, Andrew Ilyas, Aleksander Madry
Raising the Cost of Malicious AI-Powered Image Editing. ICML (2023)
Zhihong Liu, Hoang Anh Just, Xiangyu Chang, Xi Chen, Ruoxi Jia
2D-Shapley: A Framework for Fragmented Data Valuation. ICML (2023)
Vin Sachidananda, Ziyi Yang, Chenguang Zhu
Global Selection of Contrastive Batches via Optimization on Sample Permutations. ICML (2023)
Ilia Markov, Adrian Vladu, Qi Guo, Dan Alistarh
Quantized Distributed Training of Large Models with Convergence Guarantees. ICML (2023)
Daoyuan Chen, Liuyi Yao, Dawei Gao, Bolin Ding, Yaliang Li
Efficient Personalized Federated Learning via Sparse Model-Adaptation. ICML (2023)
Seohong Park, Kimin Lee, Youngwoon Lee, Pieter Abbeel
Controllability-Aware Unsupervised Skill Discovery. ICML (2023)
Jamil Arbas, Hassan Ashtiani, Christopher Liaw
Polynomial Time and Private Learning of Unbounded Gaussian Mixture Models. ICML (2023)
Marin Ballu, Quentin Berthet
Mirror Sinkhorn: Fast Online Optimization on Transport Polytopes. ICML (2023)
Sanjay Kariyappa, Chuan Guo, Kiwan Maeng, Wenjie Xiong, G. Edward Suh, Moinuddin K. Qureshi, Hsien-Hsin S. Lee
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning Using Independent Component Analysis. ICML (2023)
Ira Globus-Harris, Declan Harrison, Michael Kearns, Aaron Roth, Jessica Sorrell
Multicalibration as Boosting for Regression. ICML (2023)
Bairu Hou, Joe O'Connor, Jacob Andreas, Shiyu Chang, Yang Zhang
PromptBoosting: Black-Box Text Classification with Ten Forward Passes. ICML (2023)
Jung Hyun Lee, Jeonghoon Kim, Se Jung Kwon, Dongsoo Lee
FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization. ICML (2023)
Quentin Garrido, Randall Balestriero, Laurent Najman, Yann LeCun
RankMe: Assessing the Downstream Performance of Pretrained Self-Supervised Representations by Their Rank. ICML (2023)
Abhijeet Vyas, Brian Bullins, Kamyar Azizzadenesheli
Competitive Gradient Optimization. ICML (2023)
Fan Yao, Chuanhao Li, Denis Nekipelov, Hongning Wang, Haifeng Xu
How Bad is Top-K Recommendation under Competing Content Creators? ICML (2023)
Pascal Welke, Maximilian Thiessen, Fabian Jogl, Thomas Gärtner
Expectation-Complete Graph Representations with Homomorphisms. ICML (2023)
Dongqi Cai, Yangyuxuan Kang, Anbang Yao, Yurong Chen
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition. ICML (2023)
Guangyu Nie, Changhoon Kim, Yezhou Yang, Yi Ren
Attributing Image Generative Models using Latent Fingerprints. ICML (2023)
Andy Shih, Dorsa Sadigh, Stefano Ermon
Long Horizon Temperature Scaling. ICML (2023)
Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen
Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks. ICML (2023)
Yuan-Ting Hu, Alexander G. Schwing, Raymond A. Yeh
Surface Snapping Optimization Layer for Single Image Object Shape Reconstruction. ICML (2023)
Emirhan Kurtulus, Zichao Li, Yann N. Dauphin, Ekin Dogus Cubuk
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation. ICML (2023)
Jonathan Wilder Lavington, Sharan Vaswani, Reza Babanezhad Harikandeh, Mark Schmidt, Nicolas Le Roux
Target-based Surrogates for Stochastic Optimization. ICML (2023)
Jianing Zhu, Xiawei Guo, Jiangchao Yao, Chao Du, Li He, Shuo Yuan, Tongliang Liu, Liang Wang, Bo Han
Exploring Model Dynamics for Accumulative Poisoning Discovery. ICML (2023)
Songming Liu, Zhongkai Hao, Chengyang Ying, Hang Su, Ze Cheng, Jun Zhu
NUNO: A General Framework for Learning Parametric PDEs with Non-Uniform Data. ICML (2023)
Advait Harshal Gadhikar, Sohom Mukherjee, Rebekka Burkholz
Why Random Pruning Is All We Need to Start Sparse. ICML (2023)
Minshuo Chen, Kaixuan Huang, Tuo Zhao, Mengdi Wang
Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data. ICML (2023)
Lena Podina, Brydon Eastman, Mohammad Kohandel
Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data. ICML (2023)
David Venuto, Sherry Yang, Pieter Abbeel, Doina Precup, Igor Mordatch, Ofir Nachum
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets. ICML (2023)
Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard
Fast Rates for Maximum Entropy Exploration. ICML (2023)
Linwei Tao, Minjing Dong, Chang Xu
Dual Focal Loss for Calibration. ICML (2023)
Xiaohui Chen, Jiaxing He, Xu Han, Liping Liu
Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling. ICML (2023)
Xujie Song, Jingliang Duan, Wenxuan Wang, Shengbo Eben Li, Chen Chen, Bo Cheng, Bo Zhang, Junqing Wei, Xiaoming Simon Wang
LipsNet: A Smooth and Robust Neural Network with Adaptive Lipschitz Constant for High Accuracy Optimal Control. ICML (2023)
Zhongzhi Yu, Yang Zhang, Kaizhi Qian, Cheng Wan, Yonggan Fu, Yongan Zhang, Yingyan Celine Lin
Master-ASR: Achieving Multilingual Scalability and Low-Resource Adaptation in ASR with Modular Learning. ICML (2023)
Tennison Liu, Jeroen Berrevoets, Zhaozhi Qian, Mihaela van der Schaar
Learning Representations without Compositional Assumptions. ICML (2023)
Ziyu Chen, Markos A. Katsoulakis, Luc Rey-Bellet, Wei Zhu
Sample Complexity of Probability Divergences under Group Symmetry. ICML (2023)
Ge Yan, Huaijin Wu, Junchi Yan
Quantum 3D Graph Learning with Applications to Molecule Embedding. ICML (2023)
Kartik Ahuja, Divyat Mahajan, Yixin Wang, Yoshua Bengio
Interventional Causal Representation Learning. ICML (2023)
Runzhong Wang, Yunhao Zhang, Ziao Guo, Tianyi Chen, Xiaokang Yang, Junchi Yan
LinSATNet: The Positive Linear Satisfiability Neural Networks. ICML (2023)
Jonathan Feldstein, Modestas Jurcius, Efthymia Tsamoura
Parallel Neurosymbolic Integration with Concordia. ICML (2023)
Guangxuan Xiao, Ji Lin, Mickaël Seznec, Hao Wu, Julien Demouth, Song Han
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models. ICML (2023)
Yuheng Zhang, Yu Bai, Nan Jiang
Offline Learning in Markov Games with General Function Approximation. ICML (2023)
Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. ICML (2023)
Paul Duetting, Federico Fusco, Silvio Lattanzi, Ashkan Norouzi-Fard, Morteza Zadimoghaddam
Fully Dynamic Submodular Maximization over Matroids. ICML (2023)
Chawin Sitawarin, Florian Tramèr, Nicholas Carlini
Preprocessors Matter! Realistic Decision-Based Attacks on Machine Learning Systems. ICML (2023)

volume 202, 2023

International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA. ICML 202 (2023)