Interpreting Deep Classifier by Visual Distillation of Dark Knowledge. - Kai Xu 016; Dae Hoon Park; Chang Yi; Charles Sutton (CoRR, 2018)

Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models. - Akash Srivastava; Kai Xu 016; Michael U. Gutmann; Charles Sutton (CoRR, 2018)

Variational Russian Roulette for Deep Bayesian Nonparametrics. - Kai Xu 016; Akash Srivastava; Charles Sutton (ICML, 2019)

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference. - Cole L. Hurwitz; Kai Xu 016; Akash Srivastava; Alessio Paolo Buccino; Matthias H. Hennig (NeurIPS, 2019)

Generative Ratio Matching Networks. - Akash Srivastava; Kai Xu 016; Michael U. Gutmann; Charles Sutton (ICLR, 2020)

Telescoping Density-Ratio Estimation. - Benjamin Rhodes; Kai Xu 016; Michael U. Gutmann (NeurIPS, 2020)

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling. - Akash Srivastava; Yamini Bansal; Yukun Ding; Cole L. Hurwitz; Kai Xu 016; Bernhard Egger 001; Prasanna Sattigeri; Josh Tenenbaum 001; David D. Cox; Dan Gutfreund (CoRR, 2020)

Couplings for Multinomial Hamiltonian Monte Carlo. - Kai Xu 016; Tor Erlend Fjelde; Charles Sutton; Hong Ge (AISTATS, 2021)

Targeted Neural Dynamical Modeling. - Cole L. Hurwitz; Akash Srivastava; Kai Xu 016; Justin Jude; Matthew G. Perich; Lee E. Miller; Matthias H. Hennig (NeurIPS, 2021)

A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics. - Kai Xu 016; Akash Srivastava; Dan Gutfreund; Felix Sosa; Tomer D. Ullman; Josh Tenenbaum 001; Charles Sutton (NeurIPS, 2021)

Repairing Systematic Outliers by Learning Clean Subspaces in VAEs. - Simão Eduardo; Kai Xu 016; Alfredo Nazábal; Charles Sutton (CoRR, 2022)

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries. - Charlotte Loh; Seungwook Han; Shivchander Sudalairaj; Rumen Dangovski; Kai Xu 016; Florian Wenzel; Marin Soljacic; Akash Srivastava (ICML, 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 016; Maria Sinziana Astefanoaei (CoRR, 2023)

Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression. - Akash Srivastava; Seungwook Han; Kai Xu 016; Benjamin Rhodes; Michael U. Gutmann (Trans. Mach. Learn. Res., 2023)

LAB: Large-Scale Alignment for ChatBots. - Shivchander Sudalairaj; Abhishek Bhandwaldar; Aldo Pareja; Kai Xu 016; David D. Cox; 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 016; Maria Sinziana Astefanoaei; Nicolas Collignon (CoRR, 2024)

CDR: Customizable Density Ratios of Strong-over-weak LLMs for Preference Annotation. - Guangxuan Xu; Kai Xu 016; Shivchander Sudalairaj; Hao Wang; Akash Srivastava (CoRR, 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 016; Faez Ahmed (Trans. Mach. Learn. Res., 2024)

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 001; Seungwook Han; Abhishek Bhandwaldar; Guangxuan Xu; Kai Xu 016; Ligong Han; Luke Inglis; et al. (ICLR, 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 016; Akash Srivastava (CoRR, 2025)

SQuat: Subspace-orthogonal KV Cache Quantization. - Hao Wang; Ligong Han; Kai Xu 016; Akash Srivastava (CoRR, 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 016; Hao Wang; Aldo Pareja; Oleg Silkin; Mustafa Eyceoz; Akash Srivastava (CoRR, 2025)