Liu, Bingbin

19 publications

ICLR 2025 Progressive Distillation Induces an Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

ICLRW 2024 Augmentation Alone Leads to Generalization Runtian Zhai, Bingbin Liu, Andrej Risteski, J Zico Kolter, Pradeep Kumar Ravikumar

ICMLW 2024 Progressive Distillation Improves Feature Learning via Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

ICMLW 2024 Progressive Distillation Improves Feature Learning via Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

NeurIPSW 2024 Progressive Distillation Induces an Implicit Curriculum Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Andrej Risteski, Surbhi Goel

ICLR 2024 Understanding Augmentation-Based Self-Supervised Representation Learning via RKHS Approximation and Regression Runtian Zhai, Bingbin Liu, Andrej Risteski, J Zico Kolter, Pradeep Kumar Ravikumar

ICMLW 2023 (Un)interpretability of Transformers: A Case Study with Dyck Grammars Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski

NeurIPS 2023 Exposing Attention Glitches with Flip-Flop Language Modeling Bingbin Liu, Jordan Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

ICMLW 2023 Exposing Attention Glitches with Flip-Flop Language Modeling Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

NeurIPSW 2023 TinyGSM: Achieving 80% on GSM8k with One Billion Parameters Bingbin Liu, Sebastien Bubeck, Ronen Eldan, Janardhan Kulkarni, Yuanzhi Li, Anh Nguyen, Rachel Ward, Yi Zhang

NeurIPS 2023 Transformers Are Uninterpretable with Myopic Methods: A Case Study with Bounded Dyck Grammars Kaiyue Wen, Yuchen Li, Bingbin Liu, Andrej Risteski

ICLR 2023 Transformers Learn Shortcuts to Automata Bingbin Liu, Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Cyril Zhang

ICLR 2022 Analyzing and Improving the Optimization Landscape of Noise-Contrastive Estimation Bingbin Liu, Elan Rosenfeld, Pradeep Kumar Ravikumar, Andrej Risteski

NeurIPS 2022 Masked Prediction: A Parameter Identifiability View Bingbin Liu, Daniel J. Hsu, Pradeep K. Ravikumar, Andrej Risteski

AISTATS 2021 Contrastive Learning of Strong-Mixing Continuous-Time Stochastic Processes Bingbin Liu, Pradeep Ravikumar, Andrej Risteski

NeurIPS 2020 Generalized Boosting Arun Suggala, Bingbin Liu, Pradeep K. Ravikumar

MLHC 2018 3D Point Cloud-Based Visual Prediction of ICU Mobility Care Activities Bingbin Liu, Michelle Guo, Edward Chou, Rishab Mehra, Serena Yeung, N. Lance Downing, Francesca Salipur, Jeffrey Jopling, Brandi Campbell, Kayla Deru, William Beninati, Arnold Milstein, Li Fei-Fei

NeurIPS 2018 Learning to Decompose and Disentangle Representations for Video Prediction Jun-Ting Hsieh, Bingbin Liu, De-An Huang, Li F Fei-Fei, Juan Carlos Niebles

ECCV 2018 Temporal Modular Networks for Retrieving Complex Compositional Activities in Videos Bingbin Liu, Serena Yeung, Edward Chou, De-An Huang, Li Fei-Fei, Juan Carlos Niebles