July 11, 2023

IFML @ ICML 2023

IFML members had 35 papers accepted to ICML 2023 -- The Fortieth International Conference on Machine Learning


ICML | 2023

Fortieth International Conference on Machine Learning
July 23-July 9

Celebrating the IFML members with accepted papers at the International Conference on Machine Learning! ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. Visit the ICML conference schedule to bookmark these sessions.


  • Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers
    Sitan Chen, Giannis Daras, Alexandros Dimakis
  • MAHALO: Unifying Offline Reinforcement Learning and Imitation Learning from Observations
    Anqi Li, Byron Boots, Ching-An Cheng
  • Reward-Mixing MDPs with Few Latent Contexts are Learnable
    Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor
  • Horizon-Free and Variance-Dependent Reinforcement Learning for Latent Markov Decision Processes
    Runlong Zhou, Ruosong Wang, Simon Du
  • Sharp Variance-Dependent Bounds in Reinforcement Learning: Best of Both Worlds in Stochastic and Deterministic Environments
    Runlong Zhou, Zhang Zihan, Simon Du
  • Improved Active Multi-Task Representation Learning via Lasso
    Yiping Wang, Yifang Chen, Kevin Jamieson, Simon Du
  • On the Power of Pre-training for Generalization in RL: Provable Benefits and Hardness
    Haotian Ye, Xiaoyu Chen, Liwei Wang, Simon Du
  • Understanding Incremental Learning of Gradient Descent: A Fine-grained Analysis of Matrix Sensing
    Jikai Jin, Zhiyuan Li, Kaifeng Lyu, Simon Du, Jason Lee
  • SpotEM: Efficient Video Search for Episodic Memory
    Santhosh Kumar Ramakrishnan, Ziad Al-Halah, Kristen Grauman
  • Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron
    Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, Sham Kakade
  • Hardness of Independent Learning and Sparse Equilibrium Computation in Markov Games
    Dylan Foster, Noah Golowich, Sham Kakade
  • On Provable Copyright Protection for Generative Models
    Nikhil Vyas, Sham Kakade, Boaz Barak
  • DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design
    Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
  • MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation
    Xingang Peng, Jiaqi Guan, qiang liu, Jianzhu Ma
  • CRISP: Curriculum based Sequential neural decoders for Polar code family
    S Ashwin Hebbar, Viraj Nadkarni, Ashok Vardhan Makkuva, Suma Bhat, Sewoong Oh, Pramod Viswanath
  • Why Is Public Pretraining Necessary for Private Model Training?
    Arun Ganesh, Mahdi Haghifam, Milad Nasresfahani, Sewoong Oh, Thomas Steinke, Om Thakkar, Abhradeep Guha Thakurta, Lun Wang
  • Private Federated Learning with Autotuned Compression
    Enayat Ullah, Christopher Choquette-Choo, Peter Kairouz, Sewoong Oh
  • High-dimensional Location Estimation via Norm Concentration for Subgamma Vectors
    Shivam Gupta, Jasper Lee, Eric Price
  • Understanding Self-Distillation in the Presence of Label Noise
    Rudrajit Das, Sujay Sanghavi
  • Beyond Uniform Lipschitz Condition in Differentially Private Optimization
    Rudrajit Das, Satyen Kale, Zheng Xu, Tong Zhang, Sujay Sanghavi
  • PAC Generalization via Invariant Representations
    Advait Parulekar, Karthikeyan Shanmugam, Sanjay Shakkottai
  • Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits
    Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R Srikant
  • Optimal Goal-Reaching Reinforcement Learning via Quasimetric Learning
    Tongzhou Wang, Antonio Torralba, Phillip Isola, Amy Zhang
  • LIV: Language-Image Representations and Rewards for Robotic Control
    Yecheng Jason Ma, Vikash Kumar, Amy Zhang, Osbert Bastani, Dinesh Jayaraman
  • Adaptively Weighted Data Augmentation Consistency Regularization for Robust Optimization under Concept Shift
    Yijun Dong, Yuege Xie, Rachel Ward
  • Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
    Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang “Atlas” Wang
  • Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
    Ajay Jaiswal, Shiwei Liu, Tianlong Chen, Ying Ding, Zhangyang “Atlas” Wang
  • Are Large Kernels Better Teachers than Transformers for ConvNets?
    Tianjin Huang , Lu Yin, Zhenyu Zhang, Li Shen, Meng Fang, Mykola Pechenizkiy, Zhangyang “Atlas” Wang, Shiwei Liu
  • Robust Weight Signatures: Gaining Robustness as Easy as Patching Weights?
    Ruisi Cai, Zhenyu Zhang, Zhangyang “Atlas” Wang
  • Data Efficient Neural Scaling Law via Model Reusing
    Peihao Wang, Rameswar Panda, Zhangyang “Atlas” Wang
  • Outline, Then Details: Syntactically Guided Coarse-To-Fine Code Generation
    Wenqing Zheng, S P Sharan, Ajay Jaiswal, Kevin Wang, Yihan Xi, Dejia Xu, Zhangyang “Atlas” Wang
  • Learning to Optimize Differentiable Games
    Xuxi Chen, Nelson Vadori, Tianlong Chen, Zhangyang “Atlas” Wang
  • Lowering the Pre-training Tax for Gradient-based Subset Training: A Lightweight Distributed Pre-Training Toolkit
    Yeonju Ro, Zhangyang “Atlas” Wang, Vijay Chidambaram, Aditya Akella
  • Towards Constituting Mathematical Structures for Learning to Optimize
    Jialin Li, Xiaohan Chen, Zhangyang “Atlas” Wang, Wotao Yin, HanQin Cai
  • Self-Attention Amortized Distributional Projection Optimization for Sliced Wasserstein Point-Cloud Reconstruction
    Khai Nguyen, Dang Nguyen, Nhat Ho