## Slides From Talks by Radford Neal

``MCMC Training of Bayesian Neural Networks'', Machine Learning Advances
and Applications, Fields Institute, 16 May 2022:
slides,
video.
``MCMC for Hierachical Bayesian Models Using Non-reversible Langevin
Methods'', virtual seminar at University of Oxford, 14 May 2020:
slides,
video.
``Non-reversible Langevin Methods for
Sampling Complex Distributions'', virtual seminar at McMaster
University, 18 March 2020: PDF.
``Non-reversibly updating a uniform [0,1] value for accept/reject
decisions'', 2nd Symposium on Advances in Approximate Bayesian Inference,
Vancouver, 8 December 2019: poster,
paper.
``Using Deterministic
Maps when Sampling from Complex Distributions'',
Evolution of Deep Learning Symposium (in honour of Geoffrey Hinton),
16 October 2019: PDF.
``An Automatic Differentiation Extension for R, and its Implementation
in pqR'', RIOT2019, Toulouse, 11 July 2019:
PDF.
``Automatic Differentiation for R'',
Vector Research Symposium, 22 February 2019:
PDF.
``Recent and Planned Language Extensions in pqR'',
RIOT2017, Brussels, 5 July 2017: PDF.
``Advances in Memory Management and Symbol Lookup in pqR'',
RIOT2017, Brussels, 5 July 2017: PDF.
``Performance improvements and future language extensions in the pqR
implementation of R'', Greater Toronto Area R User's Group,
April 2016: PDF.
``Reinforcement Learning with Randomization, Memory, and Prediction'',
CRM - University of Ottawa Distinguished Lecture, April 2016:
PDF.
``Can Interpeting be as Fast as Byte Compiling? + Other Developments
in pqR'', R Summit Conference, June 2015:
PDF.
``Learning to Randomize and Remmember in Partially-Observed
Environments'', talk at the Fields Institute, Workshop
on Big Data and Statistical Machine Learning, January 2015:
PDF (video
here).
``Proposals for Extending the R language'',
talk given at Directions in Statistical Computing (DSC 2014),
Brixen / Bressanone, Italy, June 2014:
PDF.
``Speed Improvements in pqR: Current Status and Future Plans'',
talk given at Directions in Statistical Computing (DSC 2014),
Brixen / Bressanone, Italy, June 2014:
PDF.
``Speeding up R with Multithreading, Task Merging, and Other Techniques'',
talk given at the University of Guelph, Dept. of Mathematics &
Statistics, 29 November 2013: PDF.
``Probability and Anthropic Reasoning in
Small, Large, and Infinite Universes'', Conference on
Challenges for Early Universe Cosmology, Perimeter Institute,
July 2011:
PDF of slides (but note that much
of the talk was on the blackboard).
``New Monte Carlo Methods Based on Hamiltonian Dynamics'',
MaxEnt 2011, July 2011:
PDF.
``MCMC Using Ensembles of States with Application to
Gaussian Process Regression'', talk given at the University of Toronto
Dept. of Economics (2010-10-22) and Dept. of Computer Science (2010-11-15):
Postscript,
PDF.
``Nuisance Parameters and Other Issues in Searching for
Signals in High-Energy Physics Experiments'', Talk (remotely) at the
PHYSTAT-LHC Workshop
, June 2007: Postscript,
PDF.
``Short-Cut MCMC: An Alternative to Adaptation'', Talk at the Third
Workshop on Monte Carlo Methods, May 2007:
Postscript,
PDF.
``Constructing Efficient MCMC Methods Using Temporary Mapping and Caching'',
Talk at Columbia University, December 2006:
Postscript,
PDF.
``Estimation of Failure Probabilities Using Linked Importance Sampling'',
Workshop on Nonlinearlity and Randomness in Complex Systems,
SUNY at Buffalo, April 2006:
Postscript (0.4 MBytes),
PDF (4.7 MBytes).
``Hamiltonian Importance Sampling'', BIRS workshop on
Mathematical Issues in Molecular Dynamics, June 2005:
Postscript,
PDF.
``Creating Non-Gaussian Processes from Gaussian Processes
by the Log-Sum-Exp Approach'', talk to the U of T machine learning
group, February 2005:
Postscript,
PDF.
NIPS*2004 tutorial on ``Bayesian Methods for Machine Learning'':
Postscript,
PDF.
``A New Proof of Peksun's Theorem Regarding the
Asymptotic Variance of MCMC Estimators'', poster at ISBA 2004,
Vina del Mar, Chile, May 2004:
postscript,
pdf.
``Classification for High Dimensional Problems Using Bayesian Neural
Networks and Dirichlet Diffusion Trees'',
NIPS*2003 Feature Selection Workshop,
Whistler, British Columbia, December 2003 (describing the winning entry):
pdf.
``Markov chain Monte Carlo computations
for Dirichlet diffusion trees'', NTOC 2001, Kyoto, December 2001:
postscript,
pdf.
``Survival analysis using a Bayesian
neural network'', Joint Statistical Meetings,
Atlanta, 2001:
postscript,
pdf.
``Monte Carlo decoding of LDPC codes'',
ICTP Workshop on Statistical Physics and
Capacity-Approaching Codes, May 2001:
postscript,
pdf.
``Circularly-coupled Markov chain sampling'',
April 2000:
postscript,
pdf. See also the
earlier technical report.
``Markov chain sampling using Hamiltonian
dynamics'', Joint Statistical Meetings, Baltimore, 1999:
postscript,
pdf.
``Faster encoding for low-density
parity check codes using sparse matrix methods'',
IMA workshop on Codes, Systems and
Graphical Models, Minneapolis, 1999:
postscript,
pdf.
``Tutorial on exact sampling methods'',
given 26 October 1998 during the
workshop
on Monte Carlo methods at the
Fields Institute:
postscript,
pdf.
``Improving Markov chain sampling by suppressing
random walks'', AMS-IMS-SIAM Joint Workshop on
Stochastic Inference, Monte Carlo, and Empirical Methods, July 1996:
postscript,
pdf.

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