Liu, Ran

9 publications

ICML 2024 Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y Jin, Wenrui Ma, Vidya Muthukumar, Eva L Dyer

ICLR 2024 GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings Jingyun Xiao, Ran Liu, Eva L Dyer

WACV 2024 LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer

NeurIPS 2024 Your Contrastive Learning Problem Is Secretly a Distribution Alignment Problem Zihao Chen, Chi-Heng Lin, Ran Liu, Jingyun Xiao, Eva L. Dyer

ICML 2023 Half-Hop: A Graph Upsampling Approach for Slowing Down Message Passing Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Veličković, Eva L Dyer

NeurIPS 2022 MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William Gray-Roncal, Erik Johnson, Eva Dyer

NeurIPS 2022 Seeing the Forest and the Tree: Building Representations of Both Individual and Collective Dynamics with Transformers Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva Dyer

CoRL 2021 "Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer Andrew Hundt, Aditya Murali, Priyanka Hubli, Ran Liu, Nakul Gopalan, Matthew Gombolay, Gregory D. Hager

NeurIPS 2021 Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith Hengen, Michal Valko, Eva Dyer