| May, 2025 | 3 papers were accepted at ICML 2025. We proposed CSR that builds state-of-the-art shortening embedding models (image/text/multimodal) with sparse coding. We characterized the reasons behind Transformers’ position bias and how LLMs’ length generalization requires output alignment. |
| April, 2025 | Our recent work When More is Less: Understanding Chain-of-Thought Length in LLMs received the Best Paper Runner-Up Award 🏆 at ICLR 2025 Workshop on Reasoning and Planning for LLMs. |
| April, 2025 | I will be giving a tutorial on the Principles of Self-supervised Learning in the Foundation Model Era at IJCAI 2025 (Aug 16 - Aug 22). See you in Montreal. |
| April, 2025 | I was invited to give a lightning talk titled Contextual Self-supervised Learning: A Lesson from LLMs (video) at the Self-Supervised Learning Workshop hosted by Flatiron Institute (Simons Foundation). |
| April, 2025 | I gave an invited talk at the CSE 600 Seminar at Stony Brook University on April 18. |
| March, 2025 | Our recent work Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation has sparked significant interest as a promising alternative paradigm for efficient embedding retrieval. |
| January, 2025 | I gave an an invited talk at the University of Michigan on Feb 12. |
| January, 2025 | 5 papers were accepted at ICLR 2025 (3 as a co-first author)! We proposed long-context perplexity and invariant in-context learning for better training and usage of LLMs. We also looked into some fundamental questions, such as OOD generalization of in-context learning, interplay between monosemanticity and robustness, and the nature of projection heads. |
| January, 2025 | I gave an invited talk at the CILVR Seminars at New York University on Feb 5. |
| December, 2024 | Our NeurIPS’24 work ContextSSL was featured by MIT 📰: Machines that Self-adapt to New Tasks without Re-training. It was also selected as an oral presentation (top 4) at NeurIPS’24 SSL workshop. |
| October, 2024 | 6 papers were accepted to NeurIPS 2024. We inverstigated how LLMs perform self-correction at test time (paper), how to build dynamic world models through joint embedding methods (paper), how Transformers avoid feature collapse with LayerNorm and attention masks (paper), and why equivariant prediction of data corruptions helps learn good representations (paper). |
| September, 2024 | I gave an invited talk at NYU Tandon on Building Safe Foundation Models from Principled Understanding. |
| August, 2024 | I gave an invited talk at Princeton University on Reimagining Self-Supervised Learning with Context. |