I am a senior research scientist at Google DeepMind, working on applying reinforcement learning and statistical inference techniques to LLM post-training and test-time compute.
I completed my PhD in computer science with a focus in machine learning at Stanford University, advised by Scott Linderman.
Email: dieterich.lawson@gmail.com
Last updated Mar 8 2025
NAS-X: Neural Adaptive Smoothing via Twisting
Dieterich Lawson*, Michael Y. Li*, Scott Linderman
NeurIPS 2023
SIXO: Smoothing Inference with Twisted Objectives
Dieterich Lawson*, Allan Raventós*, Andrew Warrington*, Scott Linderman
Oral, NeurIPS 2022
Energy-Inspired Models: Learning with Sampler-Induced Distributions
Dieterich Lawson*, George Tucker*, Bo Dai, and Rajesh Ranganath
NeurIPS 2019
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J Maddison
ICLR 2019
Filtering Variational Objectives
Chris J. Maddison*, Dieterich Lawson*, George Tucker*, Nicolas Heess, Mohammad
Norouzi, Andriy Mnih, Arnaud Doucet, and Yee Whye Teh
NeurIPS 2017, Best Paper at ICML 2017 Deep Structured Prediction Workshop
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
George Tucker, Andriy Mnih, Chris J Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
Oral, NeurIPS 2017
Learning Hard Alignments with Variational Inference
Dieterich Lawson*, Chung-Cheng Chiu*, George Tucker*, Colin Raffel, Kevin Swersky, and Navdeep Jaitly
ICASSP, 2018
Particle Value Functions
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
ICLR Workshop Track, 2017