Tomas Geffner

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All published works

Action	Title	Year	Authors
+ PDF Chat	Energy-Based Diffusion Language Models for Text Generation	2024	Minkai Xu Tomas Geffner Karsten Kreis Weili Nie Yilun Xu Jure Leskovec Stefano Ermon Arash Vahdat
+ PDF Chat	Truncated Consistency Models	2024	Sangyun Lee Yan Xu Tomas Geffner Giulia Fanti Karsten Kreis Arash Vahdat Weili Nie
+ PDF Chat	Stochastic Flow Matching for Resolving Small-Scale Physics	2024	Stathi Fotiadis Noah Brenowitz Tomas Geffner Yair Cohen Michael S. Pritchard Arash Vahdat Morteza Mardani
+ PDF Chat	Aligning Target-Aware Molecule Diffusion Models with Exact Energy Optimization	2024	Siyi Gu Minkai Xu Alexander K. Powers Weili Nie Tomas Geffner Karsten Kreis Jure Leskovec Arash Vahdat Stefano Ermon
+	Variational Inference with Locally Enhanced Bounds for Hierarchical Models	2022	Tomas Geffner Justin Domke
+	Deep End-to-end Causal Inference	2022	Tomas Geffner Javier Antorán Adam S. Foster Wenbo Gong Chao Ma Emre Kıcıman Amit Sharma Angus Lamb Martin Kukla Nick Pawlowski
+	Langevin Diffusion Variational Inference	2022	Tomas Geffner Justin Domke
+	Compositional Score Modeling for Simulation-based Inference	2022	Tomas Geffner George Papamakarios Andriy Mnih
+	Joint control variate for faster black-box variational inference	2022	Xi Wang Tomas Geffner Justin Domke
+	MCMC Variational Inference via Uncorrected Hamiltonian Annealing	2021	Tomas Geffner Justin Domke
+	Empirical Evaluation of Biased Methods for Alpha Divergence Minimization	2021	Tomas Geffner Justin Domke
+	MCMC Variational Inference via Uncorrected Hamiltonian Annealing	2021	Tomas Geffner Justin Domke
+	Empirical Evaluation of Biased Methods for Alpha Divergence Minimization	2020	Tomas Geffner Justin Domke
+	On the Difficulty of Unbiased Alpha Divergence Minimization	2020	Tomas Geffner Justin Domke
+	Approximation Based Variance Reduction for Reparameterization Gradients	2020	Tomas Geffner Justin Domke
+	On the Difficulty of Unbiased Alpha Divergence Minimization	2020	Tomas Geffner Justin Domke
+	A Rule for Gradient Estimator Selection, with an Application to Variational Inference.	2019	Tomas Geffner Justin Domke
+	A Rule for Gradient Estimator Selection, with an Application to Variational Inference.	2019	Tomas Geffner Justin Domke
+	A Rule for Gradient Estimator Selection, with an Application to Variational Inference	2019	Tomas Geffner Justin Domke
+ PDF Chat	Compact Policies for Fully Observable Non-Deterministic Planning as SAT	2018	Tomas Geffner Héctor Geffner
+	Compact Policies for Fully Observable Non-Deterministic Planning as SAT.	2018	Tomas Geffner Héctor Geffner
+	Using Large Ensembles of Control Variates for Variational Inference	2018	Tomas Geffner Justin Domke
+	Using Large Ensembles of Control Variates for Variational Inference	2018	Tomas Geffner Justin Domke
+	Compact Policies for Fully-Observable Non-Deterministic Planning as SAT	2018	Tomas Geffner Héctor Geffner

Common Coauthors

Coauthor	Papers Together
Justin Domke	15
Arash Vahdat	4
Karsten Kreis	3
Weili Nie	3
Héctor Geffner	3
Jure Leskovec	2
Minkai Xu	2
Stefano Ermon	2
Yilun Xu	1
Stathi Fotiadis	1
Siyi Gu	1
Morteza Mardani	1
Wenbo Gong	1
Alexander K. Powers	1
Chao Ma	1
Andriy Mnih	1
Miltiadis Allamanis	1
Angus Lamb	1
Noah Brenowitz	1
Cheng Zhang	1
Xi Wang	1
Amit Sharma	1
Sangyun Lee	1
Emre Kıcıman	1
Michael S. Pritchard	1
Giulia Fanti	1
Yair Cohen	1
Adam S. Foster	1
Yan Xu	1
Nick Pawlowski	1
George Papamakarios	1
Martin Kukla	1
Javier Antorán	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	4
+	Variational Inference: A Review for Statisticians	2017	David M. Blei Alp Kucukelbir Jon McAuliffe	4
+	Black Box Variational Inference	2014	Rajesh Ranganath Sean Gerrish David M. Blei	3
+	Importance Weighted Autoencoders	2015	Yuri Burda Roger Grosse Ruslan Salakhutdinov	3
+	Variational Bayesian Inference with Stochastic Search	2012	John Paisley David M. Blei Michael I. Jordan	3
+	The Generalized Reparameterization Gradient	2016	Francisco J. R. Ruiz Michalis K. Titsias David M. Blei	3
+	Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives	2019	George Tucker Dieterich Lawson Shixiang Gu Christopher Maddison	3
+	Advances in Variational Inference	2017	Cheng Zhang Judith Bütepage Hedvig Kjellström Stephan Mandt	3
+	Markovian Score Climbing: Variational Inference with KL(p\|\|q)	2020	Christian A. Naesseth Fredrik Lindsten David M. Blei	2
+	Variational Inference via $\chi$ Upper Bound Minimization	2017	Adji B. Dieng Dustin Tran Rajesh Ranganath John Paisley David M. Blei	2
+	Optimization Methods for Large-Scale Machine Learning	2018	Léon Bottou Frank E. Curtis Jorge Nocedal	2
+	The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables	2016	Chris J. Maddison Andriy Mnih Yee Whye Teh	2
+ PDF Chat	<i>Stan</i>: A Probabilistic Programming Language	2017	Bob Carpenter Andrew Gelman Matthew D. Hoffman Daniel C. Lee Ben Goodrich Michael Betancourt Marcus A. Brubaker Jiqiang Guo Peter Li Allen Riddell	2
+	Overdispersed Black-Box Variational Inference	2016	Francisco J. R. Ruiz Michalis K. Titsias David M. Blei	2
+	Variational Bayesian Inference with Stochastic Search	2012	David M. Blei Michael I. Jordan John Paisley	2
+	Using Large Ensembles of Control Variates for Variational Inference	2018	Tomas Geffner Justin Domke	2
+	Categorical Reparameterization with Gumbel-Softmax	2016	Eric Jang Shixiang Gu Ben Poole	2
+	Reweighted Wake-Sleep	2014	Jörg Bornschein Yoshua Bengio	2
+	Edward: A library for probabilistic modeling, inference, and criticism	2016	Dustin Tran Alp Kucukelbir Adji B. Dieng Maja Rudolph Dawen Liang David M. Blei	1
+	Improving Variational Auto-Encoders using Householder Flow	2016	Jakub M. Tomczak Max Welling	1
+ PDF Chat	Gaussian Variational Approximation With a Factor Covariance Structure	2017	Victor M.-H. Ong David J. Nott Michael S. Smith	1
+	Approximate Inference with Amortised MCMC	2017	Yingzhen Li Richard E. Turner Qiang Liu	1
+	Sticking the Landing: An Asymptotically Zero-Variance Gradient Estimator for Variational Inference.	2017	Geoffrey Roeder Yuhuai Wu David Duvenaud	1
+	Reinterpreting Importance-Weighted Autoencoders	2017	Chris Cremer Quaid Morris David Duvenaud	1
+	Generalized Planning: Non-Deterministic Abstractions and Trajectory Constraints	2017	Blai Bonet Giuseppe De Giacomo Héctor Geffner Sasha Rubin	1
+	Sticking the Landing: Simple, Lower-Variance Gradient Estimators for Variational Inference	2017	Geoffrey Roeder Yuhuai Wu David Duvenaud	1
+	Backpropagation through the Void: Optimizing control variates for black-box gradient estimation	2017	Will Grathwohl Dami Choi Yuhuai Wu Geoffrey Roeder David Duvenaud	1
+	Alpha-Beta Divergence For Variational Inference	2018	Jean-Baptiste Regli Ricardo Silva	1
+	Variational Inference with Tail-adaptive f-Divergence	2018	Dilin Wang Hao Liu Qiang Liu	1
+	Improving Explorability in Variational Inference with Annealed Variational Objectives	2018	Chin-Wei Huang Shawn Tan Alexandre Lacoste Aaron Courville	1
+	Deep Learning for Classical Japanese Literature	2018	Alex Lamb Asanobu Kitamoto David Ha Kazuaki Yamamoto Mikel Bober-Irizar Tarin Clanuwat	1
+	Reducing Reparameterization Gradient Variance	2017	Andrew C. Miller Nicholas J. Foti Alexander D’Amour Ryan P. Adams	1
+	Neural Variational Inference and Learning in Belief Networks	2014	Andriy Mnih Karol Gregor	1
+	Monte Carlo Gradient Estimation in Machine Learning	2019	Shakir Mohamed Mihaela Rosca Michael Figurnov Andriy Mnih	1
+	The geometric foundations of Hamiltonian Monte Carlo	2017	Michael Betancourt Simon Byrne Samuel Livingstone Mark Girolami	1
+ PDF Chat	Advances in Variational Inference	2018	Cheng Zhang Judith Bütepage Hedvig Kjellström Stephan Mandt	1
+	A symbolic SAT-based algorithm for almost-sure reachability with small strategies in POMDPs	2016	Krishnendu Chatterjee Martin Chmelík Jessica Davies	1
+	Measuring the reliability of MCMC inference with bidirectional Monte Carlo	2016	Roger Grosse Siddharth Ancha Daniel M. Roy	1
+	Hamiltonian Variational Auto-Encoder	2018	Anthony L. Caterini Arnaud Doucet Dino Sejdinović	1
+	The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo	2014	Matthew D. Homan Andrew Gelman	1
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	1
+	Underdamped Langevin MCMC: A non-asymptotic analysis	2017	Xiang Cheng Niladri S. Chatterji Peter L. Bartlett Michael I. Jordan	1
+	On importance-weighted autoencoders	2019	Axel Finke Alexandre H. Thiéry	1
+	Learning Deep Generative Models with Annealed Importance Sampling	2019	Xinqiang Ding David J. Freedman	1
+	Amortized variance reduction for doubly stochastic objectives	2020	Ayman Boustati Sattar Vakili James Hensman St. John	1
+	f-Divergence Variational Inference	2020	Neng Wan Dapeng Li Naira Hovakimyan	1
+	On the Difficulty of Unbiased Alpha Divergence Minimization	2020	Tomas Geffner Justin Domke	1
+	Empirical Evaluation of Biased Methods for Alpha Divergence Minimization	2021	Tomas Geffner Justin Domke	1
+	None	2000	Tommi Jaakkola Michael I. Jordan	1
+	Monte Carlo Variational Auto-Encoders	2021	Achille Thin Nikita Kotelevskii Arnaud Doucet Alain Durmus Éric Moulines Maxim Panov	1