Dieterich Lawson

Publication Activity (10 Years)

Years Active: 2011-2023
Publications (10 Years): 23

Top Topics

Gradient Estimators

Reinforcement Learning

Latent Variables

Pitman Yor Process

Top Venues

ICLR (Workshop)

Publications

Dieterich Lawson, Michael Li, Scott W. Linderman
NAS-X: Neural Adaptive Smoothing via Twisting. NeurIPS (2023)
Dieterich Lawson, Michael Li, Scott W. Linderman
NAS-X: Neural Adaptive Smoothing via Twisting. CoRR (2023)
Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
SIXO: Smoothing Inference with Twisted Objectives. NeurIPS (2022)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Xiuyuan Lu, Morteza Ibrahimi, Dieterich Lawson, Botao Hao, Brendan O'Donoghue, Benjamin Van Roy
The Neural Testbed: Evaluating Joint Predictions. NeurIPS (2022)
Dieterich Lawson, Allan Raventós, Andrew Warrington, Scott W. Linderman
SIXO: Smoothing Inference with Twisted Objectives. CoRR (2022)
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Botao Hao, Morteza Ibrahimi, Dieterich Lawson, Xiuyuan Lu, Brendan O'Donoghue, Benjamin Van Roy
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work? CoRR (2021)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Energy-Inspired Models: Learning with Sampler-Induced Distributions. NeurIPS (2019)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Energy-Inspired Models: Learning with Sampler-Induced Distributions. CoRR (2019)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. ICLR (Poster) (2019)
Dieterich Lawson, George Tucker, Bo Dai, Rajesh Ranganath
Revisiting Auxiliary Latent Variables in Generative Models. DGS@ICLR (2019)
George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives. CoRR (2018)
Dieterich Lawson, Chung-Cheng Chiu, George Tucker, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Learning Hard Alignments with Variational Inference. ICASSP (2018)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
Filtering Variational Objectives. CoRR (2017)
Augustus Odena, Dieterich Lawson, Christopher Olah
Changing Model Behavior at Test-Time Using Reinforcement Learning. CoRR (2017)
Augustus Odena, Dieterich Lawson, Christopher Olah
Changing Model Behavior at Test-time Using Reinforcement Learning. ICLR (Workshop) (2017)
George Tucker, Andriy Mnih, Chris J. Maddison, Dieterich Lawson, Jascha Sohl-Dickstein
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models. NIPS (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Particle Value Functions. CoRR (2017)
Colin Raffel, Dieterich Lawson
Training a Subsampling Mechanism in Expectation. ICLR (Workshop) (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh
Particle Value Functions. ICLR (Workshop) (2017)
Colin Raffel, Dieterich Lawson
Training a Subsampling Mechanism in Expectation. CoRR (2017)
Chung-Cheng Chiu, Dieterich Lawson, Yuping Luo, George Tucker, Kevin Swersky, Ilya Sutskever, Navdeep Jaitly
An online sequence-to-sequence model for noisy speech recognition. CoRR (2017)
Dieterich Lawson, George Tucker, Chung-Cheng Chiu, Colin Raffel, Kevin Swersky, Navdeep Jaitly
Learning Hard Alignments with Variational Inference. CoRR (2017)
Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh
Filtering Variational Objectives. NIPS (2017)
Ka-Ping Yee, Dieterich Lawson, Dominic König, Dale Zak
The tablecast data publishing protocol. ISCRAM (2011)