Katelyn Gao

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All published works

Action	Title	Year	Authors
+	Generalizing Gaussian Smoothing for Random Search	2022	Katelyn Gao Ozan Şener
+	Modeling and Optimization Trade-off in Meta-learning	2020	Katelyn Gao Ozan Şener
+	Modeling and Optimization Trade-off in Meta-learning	2020	Katelyn Gao Ozan Şener
+ PDF Chat	ESTIMATION AND INFERENCE FOR VERY LARGE LINEAR MIXED EFFECTS MODELS	2018	Katelyn Gao Art B. Owen
+	Assessing Generalization in Deep Reinforcement Learning	2018	Charles Packer Katelyn Gao Jernej Kos Philipp Krähenbühl Vladlen Koltun Dawn Song
+	Efficient moment calculations for variance components in large unbalanced crossed random effects models	2017	Katelyn Gao Art B. Owen
+	Statistical Inference for Algorithmic Leveraging	2016	Katelyn Gao
+	Estimation and Inference for Very Large Linear Mixed Effects Models	2016	Katelyn Gao A. B. Owen
+	Confidence Intervals for Algorithmic Leveraging in Linear Regression	2016	Katelyn Gao

Common Coauthors

Coauthor	Papers Together
Ozan Şener	3
Art B. Owen	2
Charles Packer	1
Philipp Krähenbühl	1
Jernej Kos	1
Vladlen Koltun	1
A. B. Owen	1
Dawn Song	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler	1997	Gareth O. Roberts Sujit K. Sahu	3
+	Estimation of Variance and Covariance Components	1953	Charles Henderson	3
+ PDF Chat	The ASA Statement on <i>p</i>-Values: Context, Process, and Purpose	2016	Ronald L. Wasserstein Nicole A. Lazar	2
+ PDF Chat	Bayes and big data: the consensus Monte Carlo algorithm	2016	Steven L. Scott Alexander W. Blocker Fernando V. Bonassi Hugh Chipman Edward I. George Robert E. McCulloch	2
+	A Crossed Random Effects Model for Unbalanced Data With Applications in Cross-Sectional and Longitudinal Research	1993	Stephen W. Raudenbush	2
+	Polynomial Accelerated MCMC and Other Sampling Algorithms Inspired by Computational Optimization	2013	Colin Fox	2
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	2
+	Matrix versions of the Cauchy and Kantorovich inequalities	1990	Albert W. Marshall Ingram Olkin	2
+	Updating the Inverse of a Matrix	1989	William W. Hager	2
+	RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning	2016	Yan Duan John Schulman Xi Chen Peter L. Bartlett Ilya Sutskever Pieter Abbeel	2
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel	2
+ PDF Chat	The pigeonhole bootstrap	2007	Art B. Owen	2
+	The BOBYQA algorithm for bound constrained optimization without derivatives	2009	M. J. D. Powell	2
+ PDF Chat	Julia: A Fresh Approach to Numerical Computing	2017	Jeff Bezanson Alan Edelman Stefan Karpinski Viral B. Shah	2
+	Bilevel Programming: A Combinatorial Perspective	2005	Patrice Marcotte Gilles Savard	1
+	On best approximate solutions of linear matrix equations	1956	Roger Penrose	1
+	Consistent Estimators in Generalized Linear Mixed Models	1998	Jiming Jiang	1
+	Regression Shrinkage and Selection Via the Lasso	1996	Robert Tibshirani	1
+ PDF Chat	CUR matrix decompositions for improved data analysis	2009	Michael W. Mahoney Petros Drineas	1
+	Optimizing the CVaR via sampling	2015	Aviv Tamar Yonatan Glassner Shie Mannor	1
+	Benchmarking Deep Reinforcement Learning for Continuous Control	2016	Yan Duan Xi Chen Rein Houthooft John Schulman Pieter Abbeel	1
+	ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning	2016	Michał Kempka Marek Wydmuch Grzegorz Runc Jakub Toczek Wojciech Jaśkowski	1
+	EPOpt: Learning Robust Neural Network Policies Using Model Ensembles	2016	Aravind Rajeswaran Sarvjeet Ghotra Balaraman Ravindran Sergey Levine	1
+	Learning to reinforcement learn	2016	Jane X. Wang Zeb Kurth‐Nelson Dhruva Tirumala Hubert Soyer Joel Z. Leibo Rémi Munos Charles Blundell Dharshan Kumaran Matt Botvinick	1
+ PDF Chat	50 Years of Data Science	2017	David L. Donoho	1
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	1
+ PDF Chat	Domain randomization for transferring deep neural networks from simulation to the real world	2017	Josh Tobin Rachel Fong Alex Ray Jonas Schneider Wojciech Zaremba Pieter Abbeel	1
+	A Statistical Perspective on Algorithmic Leveraging	2013	Ping Ma Michael W. Mahoney Bin Yu	1
+	Fast approximation of matrix coherence and statistical leverage	2012	Petros Drineas Malik Magdon‐Ismail Michael W. Mahoney David P. Woodruff	1
+	Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics	2017	Ken Kansky Tom Silver David A. Mély Mohamed Eldawy Miguel Lázaro-Gredilla Xinghua Lou Nimrod Dorfman Szymon Sidor Scott Phoenix Dileep George	1
+	An Overview of Multi-Task Learning in Deep Neural Networks	2017	Sebastian Ruder	1
+	Reinforcement Learning under Model Mismatch	2017	Aurko Roy Huan Xu Sebastian Pokutta	1
+	Learning to Learn: Meta-Critic Networks for Sample Efficient Learning	2017	Flood Sung Zhang Li Tao Xiang Timothy M. Hospedales Yongxin Yang	1
+ PDF Chat	Deep Reinforcement Learning That Matters	2018	Peter Henderson Riashat Islam Philip Bachman Joëlle Pineau Doina Precup David Meger	1
+	Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments	2017	Maruan Al-Shedivat Trapit Bansal Yuri Burda Ilya Sutskever Igor Mordatch Pieter Abbeel	1
+	Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm	2017	Chelsea Finn Sergey Levine	1
+	AI Safety Gridworlds	2017	Jan Leike Miljan Martic Victoria Krakovna Pedro A. Ortega Tom Everitt Andrew Lefrancq Laurent Orseau Shane Legg	1
+	Deep Learning: A Critical Appraisal	2018	Gary Marcus	1
+	High-Dimensional Probability: An Introduction with Applications in Data Science	2018	Roman Vershynin	1
+	Meta Reinforcement Learning with Latent Variable Gaussian Processes	2018	Steindór Sæmundsson Katja Hofmann Marc Peter Deisenroth	1
+	An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling	2018	Shaojie Bai J. Zico Kolter Vladlen Koltun	1
+	Reptile: a Scalable Metalearning Algorithm	2018	Alex Nichol John Schulman	1
+	On First-Order Meta-Learning Algorithms.	2018	Alex Nichol Joshua Achiam John Schulman	1
+	Gotta Learn Fast: A New Benchmark for Generalization in RL	2018	Alex Nichol Vicki Pfau Christopher Hesse Oleg Klimov John Schulman	1
+	Bilevel Programming for Hyperparameter Optimization and Meta-Learning	2018	Luca Franceschi Paolo Frasconi Saverio Salzo Riccardo Grazzi Massimilano Pontil	1
+	A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning	2018	Amy Zhang Nicolas Ballas Joëlle Pineau	1
+	Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation	2018	Niels Justesen Rubén Rodríguez Torrado Philip Bontrager Ahmed Khalifa Julian Togelius Sebastian Risi	1
+	Assessing Generalization in Deep Reinforcement Learning	2018	Charles Packer Katelyn Gao Jernej Kos Philipp Krähenbühl Vladlen Koltun Dawn Song	1
+	Quantifying Generalization in Reinforcement Learning	2018	Karl Cobbe Oleg Klimov Chris Hesse Taehoon Kim John Schulman	1
+	Online Meta-Learning	2019	Chelsea Finn Aravind Rajeswaran Sham M. Kakade Sergey Levine	1