Machine Learning (stat)

Papers filed under stat.ML on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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2,401 to 2,460 of 6,785

Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu, Yuanzhi Li, Yingyu Liang
cs.LGcs.DScs.NEarXiv:1811.04918v62018
A General Theory of Equivariant CNNs on Homogeneous Spaces
Taco Cohen, Mario Geiger, Maurice Weiler
cs.LGcs.AIcs.CGarXiv:1811.02017v22018
SLAYER: Spike Layer Error Reassignment in Time
Sumit Bam Shrestha, Garrick Orchard
cs.NEcs.LGstat.MLarXiv:1810.08646v12018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris, Martin Ritzert, Matthias Fey +4
cs.LGcs.AIcs.CVarXiv:1810.02244v52018
Collaborative Deep Learning in Fixed Topology Networks
Zhanhong Jiang, Aditya Balu, Chinmay Hegde +1
stat.MLcs.LGarXiv:1706.07880v12017
CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)
Chenyu You, Guang Li, Yi Zhang +9
eess.IVcs.CVcs.LGarXiv:1808.04256v32018
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Alex Sherstinsky
cs.LGstat.MLarXiv:1808.03314v102018
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Yuanzhi Li, Yingyu Liang
cs.LGstat.MLarXiv:1808.01204v32018
Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, Oriol Vinyals
cs.LGstat.MLarXiv:1807.03748v22018
Federated Learning with Non-IID Data
Yue Zhao, Meng Li, Liangzhen Lai +3
cs.LGstat.MLarXiv:1806.00582v22018
Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning
Xianfu Chen, Honggang Zhang, Celimuge Wu +3
cs.LGcs.AIstat.MLarXiv:1805.06146v12018
Adaptive Federated Learning in Resource Constrained Edge Computing Systems
Shiqiang Wang, Tiffany Tuor, Theodoros Salonidis +4
cs.DCcs.LGmath.OCarXiv:1804.05271v32018
Attention, Learn to Solve Routing Problems!
Wouter Kool, Herke van Hoof, Max Welling
stat.MLcs.LGarXiv:1803.08475v32018
A DIRT-T Approach to Unsupervised Domain Adaptation
Rui Shu, Hung H. Bui, Hirokazu Narui +1
stat.MLcs.CVcs.LGarXiv:1802.08735v22018
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
Leland McInnes, John Healy, James Melville
stat.MLcs.CGcs.LGarXiv:1802.03426v32018
Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations
Maziar Raissi, Paris Perdikaris, George Em Karniadakis
cs.AIcs.LGmath.DSarXiv:1711.10561v12017
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
Eli David, Nathan S. Netanyahu
cs.CRcs.LGcs.NEarXiv:1711.08336v22017
Advances in Variational Inference
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom +1
cs.LGstat.MLarXiv:1711.05597v32017
Certifying Some Distributional Robustness with Principled Adversarial Training
Aman Sinha, Hongseok Namkoong, Riccardo Volpi +1
stat.MLcs.LGarXiv:1710.10571v52017
VAMPnets: Deep learning of molecular kinetics
Andreas Mardt, Luca Pasquali, Hao Wu +1
stat.MLphysics.bio-phphysics.chem-pharXiv:1710.06012v22017
Lipschitz Continuity in Model-based Reinforcement Learning
Kavosh Asadi, Dipendra Misra, Michael L. Littman
cs.LGcs.AIstat.MLarXiv:1804.07193v32018
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
Maximilian Igl, Kamil Ciosek, Yingzhen Li +4
cs.LGcs.AIstat.MLarXiv:1910.12911v12019
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
Maziar Raissi, George Em Karniadakis
cs.AIcs.LGmath.AParXiv:1708.00588v22017
Variational approach for learning Markov processes from time series data
Hao Wu, Frank Noé
stat.MLmath.DSarXiv:1707.04659v32017
Spectrally-normalized margin bounds for neural networks
Peter Bartlett, Dylan J. Foster, Matus Telgarsky
cs.LGcs.NEstat.MLarXiv:1706.08498v22017
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Jason Altschuler, Jonathan Weed, Philippe Rigollet
cs.DSstat.MLarXiv:1705.09634v22017
Learning Combinatorial Optimization Algorithms over Graphs
Hanjun Dai, Elias B. Khalil, Yuyu Zhang +2
cs.LGstat.MLarXiv:1704.01665v42017
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh +3
cs.LGstat.MLarXiv:1703.06114v32017
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz +4
cs.LGcs.CLcs.NEarXiv:1701.06538v12017
Random Forest Missing Data Algorithms
Fei Tang, Hemant Ishwaran
stat.MLarXiv:1701.05305v22017
Learning to Invert: Signal Recovery via Deep Convolutional Networks
Ali Mousavi, Richard G. Baraniuk
stat.MLcs.AIcs.ITarXiv:1701.03891v12017
Machine Learning of Linear Differential Equations using Gaussian Processes
Maziar Raissi, George Em. Karniadakis
cs.LGmath.NAstat.MLarXiv:1701.02440v12017
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne, Gowtham Atluri, James Faghmous +6
cs.LGcs.AIstat.MLarXiv:1612.08544v22016
Practical Secure Aggregation for Federated Learning on User-Held Data
Keith Bonawitz, Vladimir Ivanov, Ben Kreuter +6
cs.CRstat.MLarXiv:1611.04482v12016
Understanding Deep Neural Networks with Rectified Linear Units
Raman Arora, Amitabh Basu, Poorya Mianjy +1
cs.LGcond-mat.dis-nncs.AIarXiv:1611.01491v62016
Generalized Random Forests
Susan Athey, Julie Tibshirani, Stefan Wager
stat.MEecon.EMstat.MLarXiv:1610.01271v42016
Practical sketching algorithms for low-rank matrix approximation
Joel A. Tropp, Alp Yurtsever, Madeleine Udell +1
math.NAcs.DSstat.COarXiv:1609.00048v22016
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, Samy Bengio
cs.CVcs.CRcs.LGarXiv:1607.02533v42016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
cs.LGcs.AIstat.MLarXiv:1602.04938v32016
Benefits of depth in neural networks
Matus Telgarsky
cs.LGcs.NEstat.MLarXiv:1602.04485v22016
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
stat.COcs.LGstat.MLarXiv:1601.00670v92016
The Power of Depth for Feedforward Neural Networks
Ronen Eldan, Ohad Shamir
cs.LGcs.NEstat.MLarXiv:1512.03965v42015
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha +3
cs.CRcs.LGcs.NEarXiv:1511.07528v12015
Best Subset Selection via a Modern Optimization Lens
Dimitris Bertsimas, Angela King, Rahul Mazumder
stat.MEmath.OCstat.COarXiv:1507.03133v12015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens, Roger Grosse
cs.LGcs.NEstat.MLarXiv:1503.05671v72015
An Introduction to Matrix Concentration Inequalities
Joel A. Tropp
math.PRcs.DScs.ITarXiv:1501.01571v12015
A convex formulation for hyperspectral image superresolution via subspace-based regularization
Miguel Simões, José Bioucas-Dias, Luis B. Almeida +1
cs.CVphysics.data-anstat.MLarXiv:1411.4005v12014
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
cs.CLcs.LGcs.NEarXiv:1409.0473v72014
From Denoising to Compressed Sensing
Christopher A. Metzler, Arian Maleki, Richard G. Baraniuk
cs.ITmath.STstat.MLarXiv:1406.4175v52014
Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
stat.MLcs.LGarXiv:1312.6114v112013
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
Jun Fang, Yanning Shen, Hongbin Li +1
cs.ITcs.LGstat.MLarXiv:1311.2150v12013
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
Edoardo M Airoldi, Thiago B Costa, Stanley H Chan
stat.MEcs.LGcs.SIarXiv:1311.1731v22013
Deep Learning Through the Lens of Example Difficulty
Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
cs.LGstat.MLarXiv:2106.09647v22021
Domain Generalization via Invariant Feature Representation
Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
stat.MLcs.LGarXiv:1301.2115v12013
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton +1
stat.MEcs.LGmath.STarXiv:1207.6076v32012
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle, Ryan P. Adams
stat.MLcs.LGarXiv:1206.2944v22012
Optimal detection of sparse principal components in high dimension
Quentin Berthet, Philippe Rigollet
math.STstat.MLarXiv:1202.5070v32012
Convergence of latent mixing measures in finite and infinite mixture models
XuanLong Nguyen
math.STstat.MLarXiv:1109.3250v52011
Spectral Methods for Learning Multivariate Latent Tree Structure
Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu +3
cs.LGstat.MLarXiv:1107.1283v22011
Distributed Delayed Stochastic Optimization
Alekh Agarwal, John C. Duchi
math.OCstat.MLarXiv:1104.5525v12011

Machine Learning (stat)

Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers

A General Theory of Equivariant CNNs on Homogeneous Spaces

SLAYER: Spike Layer Error Reassignment in Time

Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks

Collaborative Deep Learning in Fixed Topology Networks

CT Super-resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble(GAN-CIRCLE)

Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network

Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

Representation Learning with Contrastive Predictive Coding

Federated Learning with Non-IID Data

Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

Adaptive Federated Learning in Resource Constrained Edge Computing Systems

Attention, Learn to Solve Routing Problems!

A DIRT-T Approach to Unsupervised Domain Adaptation

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification

Advances in Variational Inference

Certifying Some Distributional Robustness with Principled Adversarial Training

VAMPnets: Deep learning of molecular kinetics

Lipschitz Continuity in Model-based Reinforcement Learning

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations

Variational approach for learning Markov processes from time series data

Spectrally-normalized margin bounds for neural networks

Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration

Learning Combinatorial Optimization Algorithms over Graphs

Deep Sets

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Random Forest Missing Data Algorithms

Learning to Invert: Signal Recovery via Deep Convolutional Networks

Machine Learning of Linear Differential Equations using Gaussian Processes

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

Practical Secure Aggregation for Federated Learning on User-Held Data

Understanding Deep Neural Networks with Rectified Linear Units

Generalized Random Forests

Practical sketching algorithms for low-rank matrix approximation

Adversarial examples in the physical world

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Benefits of depth in neural networks

Variational Inference: A Review for Statisticians

The Power of Depth for Feedforward Neural Networks

The Limitations of Deep Learning in Adversarial Settings

Best Subset Selection via a Modern Optimization Lens

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

An Introduction to Matrix Concentration Inequalities

A convex formulation for hyperspectral image superresolution via subspace-based regularization

Neural Machine Translation by Jointly Learning to Align and Translate

From Denoising to Compressed Sensing

Auto-Encoding Variational Bayes

Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals

Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

Deep Learning Through the Lens of Example Difficulty

Domain Generalization via Invariant Feature Representation

Equivalence of distance-based and RKHS-based statistics in hypothesis testing

Practical Bayesian Optimization of Machine Learning Algorithms

Optimal detection of sparse principal components in high dimension

Convergence of latent mixing measures in finite and infinite mixture models

Spectral Methods for Learning Multivariate Latent Tree Structure

Distributed Delayed Stochastic Optimization