IFML @ ICML 2023

ICML | 2023

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

Congratulations!