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ORCIDKai Xu 0016
Person information
- affiliation: University of Edinburgh, School of Informatics, UK
- affiliation: MIT-IBM Watson AI Lab, IBM Research, Cambridge, MA, USA
Other persons with the same name
- Kai Xu — disambiguation page
- Kai Xu 0001
![[0000-0003-1690-3370]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0003-1690-3370")
— Shanghai Jiao Tong University, School of Mechanical Engineering, China (and 1 more) - Kai Xu 0002 — University of Minnesota, Department of Computer Science and Engineering, Minneapolis, MN, USA
- Kai Xu 0003 — University of Nottingham, UK (and 2 more)
- Kai Xu 0004 — National University of Defense Technology, School of Computer Science, Changsha, China (and 3 more)
- Kai Xu 0005 — Columbia University, Department of Mechanical Engineering
- Kai Xu 0006 — Tsinghua University, Institute of Hydrology and Water Resources, Beijing, China
- Kai Xu 0007 — Arizona State University, School of Computing, Informatics, and Decision Systems Engineering, Tempe, AZ, USA
- Kai Xu 0008 — Wuhan University, State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, China
- Kai Xu 0009 — Wuhan University of Science and Technology, College of Urban Construction, China (and 1 more)
- Kai Xu 0010 — Guangdong University of Technology, School of Computer Science and Technology, Guangzhou, China
- Kai Xu 0011 — Nantong University, School of Electronics and Information, China
- Kai Xu 0012 — Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Kai Xu 0013 — University of Chinese Academy of Sciences (UCAS), School of Computer Science and Technology, Beijing, China
- Kai Xu 0014 — National University of Defense Technology, College of Systems Engineering, Changsha, Hunan, China
- Kai Xu 0015 — Nanchang University, Department of Electronic Information Engineering, China
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2020 – today
- 2025
[c9]Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, Akash Srivastava:
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs. ICLR 2025- [i17]Isha Puri, Shivchander Sudalairaj, Guangxuan Xu, Kai Xu, Akash Srivastava:
A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods. CoRR abs/2502.01618 (2025) - [i16]Hao Wang, Ligong Han, Kai Xu, Akash Srivastava:
SQuat: Subspace-orthogonal KV Cache Quantization. CoRR abs/2503.24358 (2025) - [i15]Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han, Abhishek Bhandwaldar, Prateek Chanda, Kai Xu, Hao Wang, Aldo Pareja, Oleg Silkin, Mustafa Eyceoz, Akash Srivastava:
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning. CoRR abs/2504.07097 (2025) - 2024
- [i14]Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava:
LAB: Large-Scale Alignment for ChatBots. CoRR abs/2403.01081 (2024) - [i13]Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed:
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis. CoRR abs/2405.20592 (2024) - [i12]Maxwell Schrader, Navish Kumar, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei, Nicolas Collignon:
Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments. CoRR abs/2409.06730 (2024) - [i11]Guangxuan Xu, Kai Xu, Shivchander Sudalairaj, Hao Wang, Akash Srivastava:
CDR: Customizable Density Ratios of Strong-over-weak LLMs for Preference Annotation. CoRR abs/2411.02481 (2024) - [i10]Aldo Pareja, Nikhil Shivakumar Nayak, Hao Wang, Krishnateja Killamsetty, Shivchander Sudalairaj, Wenlong Zhao, Seungwook Han, Abhishek Bhandwaldar, Guangxuan Xu, Kai Xu, Ligong Han, Luke Inglis, Akash Srivastava:
Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs. CoRR abs/2412.13337 (2024) - 2023
- [j1]Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann:
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. Trans. Mach. Learn. Res. 2023 (2023) - [c8]Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. ICML 2023: 22614-22630 - [i9]Max Schrader, Navish Kumar, Nicolas Collignon, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei:
Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics. CoRR abs/2301.12887 (2023) - [i8]Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava:
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. CoRR abs/2303.02484 (2023) - [i7]Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann:
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. CoRR abs/2305.00869 (2023) - 2022
- [i6]Simão Eduardo, Kai Xu, Alfredo Nazábal, Charles Sutton:
Repairing Systematic Outliers by Learning Clean Subspaces in VAEs. CoRR abs/2207.08050 (2022) - 2021
- [c7]Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge:
Couplings for Multinomial Hamiltonian Monte Carlo. AISTATS 2021: 3646-3654 - [c6]Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton:
A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. NeurIPS 2021: 2478-2490 - [c5]Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig:
Targeted Neural Dynamical Modeling. NeurIPS 2021: 29379-29392 - 2020
- [c4]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Generative Ratio Matching Networks. ICLR 2020 - [c3]Benjamin Rhodes, Kai Xu, Michael U. Gutmann:
Telescoping Density-Ratio Estimation. NeurIPS 2020 - [i5]Benjamin Rhodes, Kai Xu, Michael U. Gutmann:
Telescoping Density-Ratio Estimation. CoRR abs/2006.12204 (2020) - [i4]Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund:
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. CoRR abs/2010.13187 (2020)
2010 – 2019
- 2019
- [c2]Kai Xu, Akash Srivastava, Charles Sutton:
Variational Russian Roulette for Deep Bayesian Nonparametrics. ICML 2019: 6963-6972 - [c1]Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig:
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. NeurIPS 2019: 4726-4738 - [i3]Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig:
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. CoRR abs/1905.12375 (2019) - 2018
- [i2]Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton:
Interpreting Deep Classifier by Visual Distillation of Dark Knowledge. CoRR abs/1803.04042 (2018) - [i1]Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton:
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. CoRR abs/1806.00101 (2018)
Coauthor Index
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