Papers
Cooperative Online Learning in Stochastic and Adversarial MDPs
Tal Lancewicki, Aviv Rosenberg, Yishay Mansour
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis et al.
Coordinated Double Machine Learning
Nitai Fingerhut, Matteo Sesia, Yaniv Romano
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
Michael Zhang, Nimit S Sohoni, Hongyang R Zhang et al.
Correlated Quantization for Distributed Mean Estimation and Optimization
Ananda Theertha Suresh, Ziteng Sun, Jae Ro et al.
Co-training Improves Prompt-based Learning for Large Language Models
Hunter Lang, Monica N Agrawal, Yoon Kim et al.
Counterfactual Prediction for Outcome-Oriented Treatments
Hao Zou, Bo Li, Jiangang Han et al.
Counterfactual Transportability: A Formal Approach
Juan D Correa, Sanghack Lee, Elias Bareinboim
Cross-Space Active Learning on Graph Convolutional Networks
Yufei Tao, Hao Wu, Shiyuan Deng
CtrlFormer: Learning Transferable State Representation for Visual Control via Transformer
Yao Mark Mu, Shoufa Chen, Mingyu Ding et al.
Curriculum Reinforcement Learning via Constrained Optimal Transport
Pascal Klink, Haoyi Yang, Carlo D’Eramo et al.
Cycle Representation Learning for Inductive Relation Prediction
Zuoyu Yan, Tengfei Ma, Liangcai Gao et al.
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
Robert Hönig, Yiren Zhao, Robert Mullins
data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu et al.
Data Augmentation as Feature Manipulation
Ruoqi Shen, Sebastien Bubeck, Suriya Gunasekar
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Alex Fang, Gabriel Ilharco, Mitchell Wortsman et al.
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen, Zhenyu Zhang, Sijia Liu et al.
Datamodels: Understanding Predictions with Data and Data with Predictions
Andrew Ilyas, Sung Min Park, Logan Engstrom et al.
Data Scaling Laws in NMT: The Effect of Noise and Architecture
Yamini Bansal, Behrooz Ghorbani, Ankush Garg et al.
Dataset Condensation via Efficient Synthetic-Data Parameterization
Jang-Hyun Kim, Jinuk Kim, Seong Joon Oh et al.
Dataset Condensation with Contrastive Signals
Saehyung Lee, Sanghyuk Chun, Sangwon Jung et al.
Data-SUITE: Data-centric identification of in-distribution incongruous examples
Nabeel Seedat, Jonathan Crabbé, Mihaela van der Schaar
DAVINZ: Data Valuation using Deep Neural Networks at Initialization
Zhaoxuan Wu, Yao Shu, Bryan Kian Hsiang Low
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed