Kai Xu

According to our database, Kai Xu authored at least 22 papers between 2018 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning.

Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han, Abhishek Bhandwaldar, Prateek Chanda, Kai Xu, Hao Wang, Aldo Pareja, Oleg Silkin, Mustafa Eyceoz, Akash Srivastava

CoRR, April, 2025

SQuat: Subspace-orthogonal KV Cache Quantization.

Hao Wang, Ligong Han, Kai Xu, Akash Srivastava

CoRR, March, 2025

A Probabilistic Inference Approach to Inference-Time Scaling of LLMs using Particle-Based Monte Carlo Methods.

Isha Puri, Shivchander Sudalairaj, Guangxuan Xu, Kai Xu, Akash Srivastava

CoRR, February, 2025

Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small LLMs.

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

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis.

Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund, Kai Xu, Faez Ahmed

Trans. Mach. Learn. Res., 2024

CDR: Customizable Density Ratios of Strong-over-weak LLMs for Preference Annotation.

Guangxuan Xu, Kai Xu, Shivchander Sudalairaj, Hao Wang, Akash Srivastava

CoRR, 2024

Urban context and delivery performance: Modelling service time for cargo bikes and vans across diverse urban environments.

Maxwell Schrader, Navish Kumar, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei, Nicolas Collignon

CoRR, 2024

LAB: Large-Scale Alignment for ChatBots.

Shivchander Sudalairaj, Abhishek Bhandwaldar, Aldo Pareja, Kai Xu, David D. Cox, Akash Srivastava

CoRR, 2024

2023

Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression.

Akash Srivastava, Seungwook Han, Kai Xu, Benjamin Rhodes, Michael U. Gutmann

Trans. Mach. Learn. Res., 2023

Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics.

Max Schrader, Navish Kumar, Nicolas Collignon, Esben Sørig, Soonmyeong Yoon, Akash Srivastava, Kai Xu, Maria Sinziana Astefanoaei

CoRR, 2023

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries.

Charlotte Loh, Seungwook Han, Shivchander Sudalairaj, Rumen Dangovski, Kai Xu, Florian Wenzel, Marin Soljacic, Akash Srivastava

Proceedings of the International Conference on Machine Learning, 2023

2022

Repairing Systematic Outliers by Learning Clean Subspaces in VAEs.

Simão Eduardo, Kai Xu, Alfredo Nazábal, Charles Sutton

CoRR, 2022

2021

A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics.

Kai Xu, Akash Srivastava, Dan Gutfreund, Felix Sosa, Tomer D. Ullman, Josh Tenenbaum, Charles Sutton

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Targeted Neural Dynamical Modeling.

Cole L. Hurwitz, Akash Srivastava, Kai Xu, Justin Jude, Matthew G. Perich, Lee E. Miller, Matthias H. Hennig

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Couplings for Multinomial Hamiltonian Monte Carlo.

Kai Xu, Tor Erlend Fjelde, Charles Sutton, Hong Ge

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling.

Akash Srivastava, Yamini Bansal, Yukun Ding, Cole L. Hurwitz, Kai Xu, Bernhard Egger, Prasanna Sattigeri, Josh Tenenbaum, David D. Cox, Dan Gutfreund

CoRR, 2020

Telescoping Density-Ratio Estimation.

Benjamin Rhodes, Kai Xu, Michael U. Gutmann

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Generative Ratio Matching Networks.

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference.

Cole L. Hurwitz, Kai Xu, Akash Srivastava, Alessio Paolo Buccino, Matthias H. Hennig

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Variational Russian Roulette for Deep Bayesian Nonparametrics.

Kai Xu, Akash Srivastava, Charles Sutton

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models.

Akash Srivastava, Kai Xu, Michael U. Gutmann, Charles Sutton

CoRR, 2018

Interpreting Deep Classifier by Visual Distillation of Dark Knowledge.

Kai Xu, Dae Hoon Park, Chang Yi, Charles Sutton

CoRR, 2018