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3Analyses of Deep Learning
Analyses of Deep Learning (STATS 385)
4 years ago by @kirk86
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1Graduate Algorithms
Graduate Algorithms, Spring 2001
4 years ago by @kirk86
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1Metric Embeddings and Algorithmic Applications
Metric Embeddings and Algorithmic Applications 2006
4 years ago by @kirk86
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3The Design of Approximation Algorithms
The Design of Approximation Algorithms
4 years ago by @kirk86
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1Advanced Approximation Algorithms
Advanced Approximation Algorithms, Spring 2008
4 years ago by @kirk86
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1Approximation Algorithms Course
Approximation Algorithms Course
4 years ago by @kirk86
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1Stochastic Approximation
Stochastic Approximation Method, Robbins & Monro
4 years ago by @kirk86
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1MIA-BayesBag-talk-2019
Bagged Posterior for Robust Inference
4 years ago by @kirk86
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1Variational Inference
Variational inference
4 years ago by @kirk86
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1EE 227C (Spring 2018) Convex Optimization and Approximation
EE227C (Spring 2018) Course page on Convex Optimization and Approximation
5 years ago by @kirk86
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1The frontier of simulation-based inference
K. Cranmer, J. Brehmer, and G. Louppe. (2020)
4 years ago by @kirk86
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2Approximate Bayesian computation via the energy statistic
H. Nguyen, J. Arbel, H. Lü, and F. Forbes. IEEE Access, (2020)
4 years ago by @kirk86
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2Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems
Tianping Chen, and Hong Chen. IEEE Transactions on Neural Networks, 6 (4): 911-917 (July 1995)
4 years ago by @kirk86
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1Approximation Capabilities of Neural ODEs and Invertible Residual Networks
H. Zhang, X. Gao, J. Unterman, and T. Arodz. (2019)cite arxiv:1907.12998.
4 years ago by @kirk86
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1Echo State Networks trained by Tikhonov least squares are L2(\mu) approximators of ergodic dynamical systems
A. Hart, J. Hook, and J. Dawes. (2020)cite arxiv:2005.06967Comment: 10 pages, 5 figures.
4 years ago by @kirk86
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1Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
M. Järvenpää, A. Vehtari, and P. Marttinen. (2019)cite arxiv:1910.06121.
4 years ago by @kirk86
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1Hamiltonian Monte Carlo using an embedded Laplace approximation
C. Margossian, A. Vehtari, D. Simpson, and R. Agrawal. (2020)cite arxiv:2004.12550Comment: 16 pages, 12 figures.
4 years ago by @kirk86
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2An approximate KLD based experimental design for models with intractable likelihoods
Z. Ao, and J. Li. (2020)cite arxiv:2004.00715Comment: To appear in AISTATS 2020.
4 years ago by @kirk86
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3Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning
A. Bhattacharyya, S. Gayen, K. Meel, and N. Vinodchandran. (2020)cite arxiv:2002.05378Comment: 24 pages, 1 figure.
4 years ago by @kirk86
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3On Contrastive Learning for Likelihood-free Inference
C. Durkan, I. Murray, and G. Papamakarios. (2020)cite arxiv:2002.03712.
4 years ago by @kirk86
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2Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
V. Chatziafratis, S. Nagarajan, I. Panageas, and X. Wang. (2019)cite arxiv:1912.04378.
4 years ago by @kirk86
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5Variational Algorithms for Approximate Bayesian Inference
M. Beal. (2003)
4 years ago by @kirk86
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1Generating Functions
H. Wilf. (1994)
4 years ago by @kirk86
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1Lectures On Approximation By Polynomials
J. Burkill. (1959)
4 years ago by @kirk86
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1Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units
B. Li, S. Tang, and H. Yu. (2019)cite arxiv:1903.05858Comment: 28 pages, 4 figures.
4 years ago by @kirk86
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1Neural Network with Unbounded Activation Functions is Universal Approximator
S. Sonoda, and N. Murata. (2015)cite arxiv:1505.03654Comment: under review; first revised version.
4 years ago by @kirk86
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2Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information
J. Li, Y. Zhou, and J. Zhu. International Conference on Learning Representations, (2020)
4 years ago by @kirk86
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2Neural tangent kernels, transportation mappings, and universal approximation
Z. Ji, M. Telgarsky, and R. Xian. International Conference on Learning Representations, (2020)
4 years ago by @kirk86
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3A closer look at the approximation capabilities of neural networks
K. Chong. International Conference on Learning Representations, (2020)
4 years ago by @kirk86
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1Model Selection in Bayesian Neural Networks via Horseshoe Priors
S. Ghosh, and F. Doshi-Velez. (2017)cite arxiv:1705.10388.
4 years ago by @kirk86
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3Analyses of Deep Learning

1Graduate Algorithms

1Metric Embeddings and Algorithmic Applications

3The Design of Approximation Algorithms

1Advanced Approximation Algorithms

1Approximation Algorithms Course

1Stochastic Approximation

1MIA-BayesBag-talk-2019

1Variational Inference

1EE 227C (Spring 2018) Convex Optimization and Approximation

publications (hide)85
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1The frontier of simulation-based inference

2Approximate Bayesian computation via the energy statistic

2Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

1Approximation Capabilities of Neural ODEs and Invertible Residual Networks

1Echo State Networks trained by Tikhonov least squares are L2(\mu) approximators of ergodic dynamical systems

1Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation

1Hamiltonian Monte Carlo using an embedded Laplace approximation

2An approximate KLD based experimental design for models with intractable likelihoods

3Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning

3On Contrastive Learning for Likelihood-free Inference

2Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem

5Variational Algorithms for Approximate Bayesian Inference

1Generating Functions

1Lectures On Approximation By Polynomials

1Better Approximations of High Dimensional Smooth Functions by Deep Neural Networks with Rectified Power Units

1Neural Network with Unbounded Activation Functions is Universal Approximator

2Posterior sampling for multi-agent reinforcement learning: solving extensive games with imperfect information

2Neural tangent kernels, transportation mappings, and universal approximation

3A closer look at the approximation capabilities of neural networks

1Model Selection in Bayesian Neural Networks via Horseshoe Priors

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