There goes your saturday morning, here are some videos from the ICML meeting that might be of interest to the readers of Nuit Blanche:
- Robust Principal Component Analysis with Complex Noise Authors: Qian Zhao; Deyu Meng; Zongben Xu; Wangmeng Zuo; Lei Zhang
- Randomized Nonlinear Component Analysis Authors: David Lopez-Paz; Suvrit Sra; Alex Smola; Zoubin Ghahramani; Bernhard Schoelkopf
- Discriminative Features via Generalized Eigenvectors Authors: Nikos Karampatziakis; Paul Mineiro
- Square Deal: Lower Bounds and Improved Relaxations for Tensor Recovery Authors: Cun Mu; Bo Huang; John Wright; Donald Goldfarb
- Coherent Matrix Completion Authors: Yudong Chen; Srinadh Bhojanapalli; Sujay Sanghavi; Rachel Ward
- Universal Matrix Completion Authors: Srinadh Bhojanapalli; Prateek Jain
- Exponential Family Matrix Completion under Structural Constraints Authors: Suriya Gunasekar; Pradeep Ravikumar; Joydeep Ghosh
- An Adaptive Accelerated Proximal Gradient Method and its Homotopy Continuation for Sparse Optimization Authors: Qihang Lin; Lin Xiao
- A Single-Pass Algorithm for Efficiently Recovering Sparse Cluster Centers of High-dimensional Data Authors: Jinfeng Yi; Lijun Zhang; Jun Wang; Rong Jin; Anil Jain
- Robust Distance Metric Learning via Simultaneous L1-Norm Minimization and Maximization Authors: Hua Wang; Feiping Nie; Heng Huang
- Fast Stochastic Alternating Direction Method of Multipliers Authors: Wenliang Zhong; James Kwok
- Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits Authors: Alekh Agarwal; Daniel Hsu; Satyen Kale; John Langford; Lihong Li; Robert Schapire
- Narrowing the Gap: Random Forests In Theory and In Practice Authors: Misha Denil; David Matheson; Nando De Freitas
- Marginalized Denoising Auto-encoders for Nonlinear Representations Authors: Minmin Chen; Kilian Weinberger; Fei Sha; Yoshua Bengio
- Deep Generative Stochastic Networks Trainable by Backprop Authors: Yoshua Bengio; Eric Laufer; Guillaume Alain; Jason Yosinski
- Stochastic Inference for Scalable Probabilistic Modeling of Binary Matrices Authors: Jose Miguel Hernandez-Lobato; Neil Houlsby; Zoubin Ghahramani
- Cold-start Active Learning with Robust Ordinal Matrix Factorization Authors: Neil Houlsby; Jose Miguel Hernandez-Lobato; Zoubin Ghahramani
- Probabilistic Matrix Factorization with Non-random Missing Data Authors: Jose Miguel Hernandez-Lobato; Neil Houlsby; Zoubin Ghahramani
- A Deep Semi-NMF Model for Learning Hidden Representations Authors: George Trigeorgis; Konstantinos Bousmalis; Stefanos Zafeiriou; Bjoern Schuller
- Structured Low-Rank Matrix Factorization: Optimality, Algorithm, and Applications to Image ProcessingAuthors: Benjamin Haeffele; Eric Young; Rene Vidal
- An Information Geometry of Statistical Manifold Learning Authors: Ke Sun; St
- Geodesic Distance Function Learning via Heat Flow on Vector Fields Authors: Binbin Lin; Ji Yang; Xiaofei He; Jieping Ye
- On learning to localize objects with minimal supervisionAuthors: Hyun Oh Song; Ross Girshick; Stefanie Jegelka; Julien Mairal; Zaid Harchaoui; Trevor Darrell
- Active Detection via Adaptive Submodularity Authors: Yuxin Chen; Hiroaki Shioi; Cesar Fuentes Montesinos; Lian Pin Koh; Serge Wich; Andreas Krause
- Rank-One Matrix Pursuit for Matrix Completion Authors: Zheng Wang; Ming-Jun Lai; Zhaosong Lu; Wei Fan; Hasan Davulcu; Jieping Ye
- Nuclear Norm Minimization via Active Subspace Selection Authors: Cho-Jui Hsieh; Peder Olsen
- Riemannian Pursuit for Big Matrix Recovery Authors: Mingkui Tan; Ivor W. Tsang; Li Wang; Bart Vandereycken; Sinno Jialin Pan
- Multiresolution Matrix Factorization Authors: Risi Kondor; Nedelina Teneva; Vikas Garg
- Coding for Random Projections Authors: Ping Li; Michael Mitzenmacher; Anshumali Shrivastava
- Nearest Neighbors Using Compact Sparse Codes Authors: Anoop Cherian
- Composite Quantization for Approximate Nearest Neighbor Search Authors: Ting Zhang; Chao Du; Jingdong Wang
- Gradient Hard Thresholding Pursuit for Sparsity-Constrained Optimization Authors: Xiaotong Yuan; Ping Li; Tong Zhang
- Forward-Backward Greedy Algorithms for General Convex Smooth Functions over A Cardinality Constraint Authors: Ji Liu; Jieping Ye; Ryohei Fujimaki
- Efficient Algorithms for Robust One-bit Compressive Sensing Authors: Lijun Zhang; Jinfeng Yi; Rong Jin
- Nonlinear Information-Theoretic Compressive Measurement Design Authors: Liming Wang; Abolfazl Razi; Miguel Rodrigues; Robert Calderbank; Lawrence Carin
- Elementary Estimators for High-Dimensional Linear Regression Authors: Eunho Yang; Aurelie Lozano; Pradeep Ravikumar
- Statistical-Computational Phase Transitions in Planted Models: The High-Dimensional SettingAuthors: Yudong Chen; Jiaming Xu
- Kernel Mean Estimation and Stein Effect Authors: Krikamol Muandet; Kenji Fukumizu; Bharath Sriperumbudur; Arthur Gretton; Bernhard Schoelkopf
- Quasi-Monte Carlo Feature Maps for Shift-Invariant Kernels Authors: Jiyan Yang; Vikas Sindhwani; Haim Avron; Michael Mahoney
- A Unifying View of Representer Theorems Authors: Andreas Argyriou; Francesco Dinuzzo
- Provable Bounds for Learning Some Deep Representations Authors: Sanjeev Arora; Aditya Bhaskara; Rong Ge; Tengyu Ma
- K-means recovers ICA filters when independent components are sparse Authors: Alon Vinnikov; Shai Shalev-Shwartz
- Anti-differentiating approximation algorithms:A case study with min-cuts, spectral, and flow Authors: David Gleich; Michael Mahoney
- Nonnegative Sparse PCA with Provable Guarantees Authors: Megasthenis Asteris; Dimitris Papailiopoulos; Alexandros Dimakis
- Finding Dense Subgraphs via Low-Rank Bilinear Optimization Authors: Dimitris Papailiopoulos; Ioannis Mitliagkas; Alexandros Dimakis; Constantine Caramanis
- Estimating Diffusion Network Structures: Recovery Conditions, Sample Complexity & Soft-thresholding Algorithm Authors: Hadi Daneshmand; Manuel Gomez-Rodriguez; Le Song; Bernhard Schoelkopf
- Sparse Reinforcement Learning via Convex OptimizationAuthors: Zhiwei Qin; Weichang Li; Firdaus Janoos
- Elementary Estimators for Sparse Covariance Matrices and other Structured MomentsAuthors: Eunho Yang; Aurelie Lozano; Pradeep Ravikumar
- Robust Inverse Covariance Estimation under Noisy Measurements Authors: Jun-Kun Wang; Shou-de Lin
- An Analysis of State-Relevance Weights and Sampling Distributions on L1-Regularized Approximate Linear Programming Approximation Accuracy Authors: Gavin Taylor; Connor Geer; David Piekut
- Compact Random Feature Maps Authors: Raffay Hamid; Ying Xiao; Alex Gittens; Dennis Decoste
- Margins, Kernels and Non-linear Smoothed Perceptrons Authors: Aaditya Ramdas; Javier Pe
- Understanding Protein Dynamics with L1-Regularized Reversible Hidden Markov Models Authors: Robert McGibbon; Bharath Ramsundar; Mohammad Sultan; Gert Kiss; Vijay Pande
Image Credit: NASA/JPL/Space Science Institute
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