Vector Spaces. - Holden Lee (Arch. Formal Proofs, 2014)

Quadratic polynomials of small modulus cannot represent OR. - Holden Lee (CoRR, 2015)

On the Ability of Neural Nets to Express Distributions. - Holden Lee; Rong Ge 001; Tengyu Ma 001; Andrej Risteski; Sanjeev Arora (COLT, 2017)

Towards Provable Control for Unknown Linear Dynamical Systems. - Sanjeev Arora; Elad Hazan; Holden Lee; Karan Singh; Cyril Zhang; Yi Zhang 074 (ICLR (Workshop), 2018)

Spectral Filtering for General Linear Dynamical Systems. - Elad Hazan; Holden Lee; Karan Singh; Cyril Zhang; Yi Zhang 074 (NeurIPS, 2018)

Beyond Log-concavity: Provable Guarantees for Sampling Multi-modal Distributions using Simulated Tempering Langevin Monte Carlo. - Holden Lee; Andrej Risteski; Rong Ge 001 (NeurIPS, 2018)

Simulated Tempering Langevin Monte Carlo II: An Improved Proof using Soft Markov Chain Decomposition. - Rong Ge 001; Holden Lee; Andrej Risteski (CoRR, 2018)

Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. - Rohith Kuditipudi; Xiang Wang 011; Holden Lee; Yi Zhang 074; Zhiyuan Li 005; Wei Hu 014; Rong Ge 001; Sanjeev Arora (NeurIPS, 2019)

Online sampling from log-concave distributions. - Holden Lee; Oren Mangoubi; Nisheeth K. Vishnoi (NeurIPS, 2019)

MCMC algorithms for sampling from multimodal and changing distributions - Holden Lee (2019)

Robust guarantees for learning an autoregressive filter. - Holden Lee; Cyril Zhang (ALT, 2020)

No-Regret Prediction in Marginally Stable Systems. - Udaya Ghai; Holden Lee; Karan Singh; Cyril Zhang; Yi Zhang (COLT, 2020)

Estimating normalizing constants for log-concave distributions: algorithms and lower bounds. - Rong Ge 001; Holden Lee; Jianfeng Lu 001 (STOC, 2020)

Improved rates for identification of partially observed linear dynamical systems. - Holden Lee (CoRR, 2020)

Efficient sampling from the Bingham distribution. - Rong Ge 001; Holden Lee; Jianfeng Lu 001; Andrej Risteski (ALT, 2021)

Universal Approximation Using Well-Conditioned Normalizing Flows. - Holden Lee; Chirag Pabbaraju; Anish Prasad Sevekari; Andrej Risteski (NeurIPS, 2021)

Universal Approximation for Log-concave Distributions using Well-conditioned Normalizing Flows. - Holden Lee; Chirag Pabbaraju; Anish Prasad Sevekari; Andrej Risteski (CoRR, 2021)

Improved rates for prediction and identification of partially observed linear dynamical systems. - Holden Lee (ALT, 2022)

Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. - Frederic Koehler; Holden Lee; Andrej Risteski (COLT, 2022)

Extracting Latent State Representations with Linear Dynamics from Rich Observations. - Abraham Frandsen; Rong Ge 001; Holden Lee (ICML, 2022)

Convergence for score-based generative modeling with polynomial complexity. - Holden Lee; Jianfeng Lu 001; Yixin Tan (NeurIPS, 2022)

Fisher information lower bounds for sampling. - Sinho Chewi; Patrik Gerber; Holden Lee; Chen Lu 002 (ALT, 2023)

Convergence of score-based generative modeling for general data distributions. - Holden Lee; Jianfeng Lu 001; Yixin Tan (ALT, 2023)

Pitfalls of Gaussians as a noise distribution in NCE. - Holden Lee; Chirag Pabbaraju; Anish Prasad Sevekari; Andrej Risteski (ICLR, 2023)

Improved Analysis of Score-based Generative Modeling: User-Friendly Bounds under Minimal Smoothness Assumptions. - Hongrui Chen; Holden Lee; Jianfeng Lu 001 (ICML, 2023)

Connecting Pre-trained Language Model and Downstream Task via Properties of Representation. - Chenwei Wu 002; Holden Lee; Rong Ge 001 (NeurIPS, 2023)

The probability flow ODE is provably fast. - Sitan Chen; Sinho Chewi; Holden Lee; Yuanzhi Li; Jianfeng Lu 001; Adil Salim (NeurIPS, 2023)

Provable benefits of score matching. - Chirag Pabbaraju; Dhruv Rohatgi; Anish Prasad Sevekari; Holden Lee; Ankur Moitra; Andrej Risteski (NeurIPS, 2023)

Improved Bound for Mixing Time of Parallel Tempering. - Holden Lee; Zeyu Shen 001 (CoRR, 2023)

Principled Gradient-based Markov Chain Monte Carlo for Text Generation. - Li Du; Afra Amini; Lucas Torroba Hennigen; Xinyan Velocity Yu; Jason Eisner; Holden Lee; Ryan Cotterell (CoRR, 2023)

Approximation Algorithms for the Random Field Ising Model. - Tyler Helmuth; Holden Lee; Will Perkins 001; Mohan Ravichandran; Qiang Wu (SIAM J. Discret. Math., 2023)

When is a Language Process a Language Model? - Li Du; Holden Lee; Jason Eisner; Ryan Cotterell (ACL (Findings), 2024)

Parallelising Glauber Dynamics. - Holden Lee (APPROX/RANDOM, 2024)

How Flawed Is ECE? An Analysis via Logit Smoothing. - Muthu Chidambaram; Holden Lee; Colin McSwiggen; Semon Rezchikov (ICML, 2024)

Principled Gradient-Based MCMC for Conditional Sampling of Text. - Li Du; Afra Amini; Lucas Torroba Hennigen; Xinyan Velocity Yu; Holden Lee; Jason Eisner; Ryan Cotterell (ICML, 2024)

What does guidance do? A fine-grained analysis in a simple setting. - Muthu Chidambaram; Khashayar Gatmiry; Sitan Chen; Holden Lee; Jianfeng Lu 001 (NeurIPS, 2024)

Convergence Bounds for Sequential Monte Carlo on Multimodal Distributions using Soft Decomposition. - Holden Lee; Matheau Santana-Gijzen (CoRR, 2024)

Sampling from the Continuous Random Energy Model in Total Variation Distance. - Holden Lee; Qiang Wu (CoRR, 2024)

Learning Mixtures of Gaussians Using Diffusion Models. - Khashayar Gatmiry; Jonathan A. Kelner; Holden Lee (COLT, 2025)

Efficiently learning and sampling multimodal distributions with data-based initialization. - Frederic Koehler; Holden Lee; Thuy-Duong Vuong (COLT, 2025)

Sampling List Packings. - Evan Camrud; Ewan Davies; Alex Karduna; Holden Lee (ITCS, 2025)