Nuit Blanche in Review ( November 2014 )

Since the last Nuit Blanche in Review ( October 2014 ), we landed on a comet (ESA's 28 minutes of Terror : Philae's Landing on 67P, Yes, We Can Locate Philae and Here Is How ...). I also co-hosted the Paris Machine Learning #3 Season 2: Building a Data Science Team, Opinion Mining, Word2Vec, Kaggle and noticed some old way of performing robust PCA (The first ever Robust PCA applied in Imaging). The Sparse- and Low-Rank Approximation Wiki was featured and I came up with one Sunday Morning Insight:

• Sunday Morning Insight: It's Complicated...On Games, NP-hardness, Grothendieck, Learning and Friends.

 We also had quite a few -to say the least) implementations:

• Matrix Completion on Graphs - implementation -
• Spectral Clustering of Graphs with the Bethe Hessian - implementation -
• CKN: Convolutional Kernel Networks - implementation - 
• Compressive Sensing Reconstruction Solvers in Julia 
• LRSLibrary : 64 Algorithms for Low-Rank and Sparse for Background Modeling and Subtraction in Videos
• MatchLift : Near-Optimal Joint Object Matching via Convex Relaxation - implementation - 
• Zero SR1 quasi-Newton method - implementation - 
• CVXPY: A Python-Embedded Modeling Language for Convex Optimization Problems - implementation
• Convex Optimization in Julia 

 

Focused entries

• Accelerated NMR spectroscopy with low-rank reconstruction
• An ODE for solvers, a Group approach to regularizers, Sketching for Kernels and Linear Algebra, Johnson-Lindenstrauss is optimal, Coresets of Streaming Data  
• Convex Optimization for Big Data 
• Provable Bounds for Learning Some Deep Representations
• Nonlinear Information-Theoretic Compressive Measurement Design
• Riemannian Pursuit for Big Matrix Recovery
• Sparse Reinforcement Learning via Convex Optimization
• How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
• A Randomized Algorithm for CCA
• 3D Shape Reconstruction from 2D Landmarks: A Convex Formulation
• Neural Word Embeddings as Implicit Matrix Factorization
• Stable Autoencoding: A Flexible Framework for Regularized Low-Rank Matrix Estimation
• Nuclear Norm Minimization via Active Subspace Selection
• Randomized Interpolative Decomposition of Separated Representations 
• Dimensionality Reduction for k-Means Clustering and Low Rank Approximation

 

Job

• Two Postdoc positions ( KU Leuven ) ERC Advanced Grant A-DATADRIVE-B for 1-year, extendable at ESAT-STADIUS
• CS Internship in France: Compatibilité de l'apprentissage par représentations parcimonieuses et de la compression avec pertes / Compatibility between sparse machine learning and lossy compression
• Upcoming CSJobs and more ....

 Meetings / Slides / Videos / Cfps

• NIPS 2014 proceedings are out, what paper did you fancy ?
• Saturday Morning Videos: Big data and Data science: LIX colloquium 2014  
• Saturday Morning Videos: Machine Learning Summer School, Iceland 2014
• Slides : FOCS 2014 Workshop on The Sparse Fourier Transform: Theory and Applications
• Call for Papers : SPARS 2015 – Cambridge UK – 6-9 July 2015   

Books

• Book: Sparse Modeling for Image and Vision Processing
• Book: Regularization, Optimization, Kernels, and Support Vector Machines 

 

Other:

• Saturday Morning Video: Yielding of a Yogurt under constant stress 
• The researcher's article : a three-part adventure / Le chercheur et son article : une aventure en trois actes

Four image NAVCAM mosaic comprising images taken on 20 November. Credits: ESA/Rosetta/NAVCAM – CC BY-SA IGO 3.0

Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Saturday Morning Videos: Big data and Data science: LIX colloquium 2014

Laurent Duval did an outstanding job at summarizing yesterday's LIX colloquium at Polytechnique yesterday. He also featured the videos of the talks. 

You need to go there to watch the videos.

 Here is the list of talks.

• Keynote speech: Community detection in networks, Santo Fortunato, Aalto University
• Text and Big Data,Gregory Grefenstette, Inria Saclay - Île de France
• Accessing Information in Large Document Collections: classification in web-scale taxonomies, Eric Gaussier, Université Joseph Fourier (Grenoble I)
• Shaping Social Activity by Incentivizing Users, Manuel Gomez Rodriguez, Max Planck Institute for Software Systems
• Machine Learning on Graphs and Beyond, Marc Tommasi, Inria Lille 
• Learning to discover: data science in high-energy physics and the HiggsML challenge, Balázs Kégl, CNRS
• Big Data on Big Systems: Busting a Few Myths, Peter Triantafillou, University of Glasgow
• Big Sequence Management, Themis Palpanas, Paris Descartes University
• Learning to discover: data science in high-energy physics and the HiggsML challenge, Balázs Kégl, CNRS
• Big Data on Big Systems: Busting a Few Myths, Peter Triantafillou, University of Glasgow
• Big Sequence Management, Themis Palpanas, Paris Descartes University 
• Understanding Videos at YouTube Scale, Richard Washington, Google
• AWS's big data/HPC innovations, Stephan Hadinger, Amazon Web Services
• Big Data in Insurance - Evolution or disruption? Stéphane Guinet, AXA 
• Closing words on a word cloud (with time, series, graph and classification are the big four)

For the record, I am super glad that the organizers thought it was a good idea to use Google Hangout to keep some record of what was discussed (like we do at the Paris Machine Learning meetup - 1520 members and counting -) . We need more of these initiatives !

          Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Two Postdoc positions ( KU Leuven ) ERC Advanced Grant A-DATADRIVE-B for 1-year, extendable at ESAT-STADIUS

Marco Signoretto just sent me the following:

Dear Igor,

I hope everything is fine with you. Can you please post the following announcement [on] Nuit Blanche?

regards

Marco

Sure thing, Marco. Here is the announcement:

2 Postdoc positions ERC Advanced Grant A-DATADRIVE-B for 1-year, extendable at ESAT-STADIUS.

The research group KU Leuven ESAT-STADIUS is currently offering 2 Postdoc positions (1-year, extendable) within the framework of the ERC Advanced Grant A-DATADRIVE-B (PI: Johan Suykens) on Advanced Data-Driven Black-box modelling.
The research positions relate to the following possible topics:

1. Prior knowledge incorporation
2. Kernels and tensors
3. Modelling structured dynamical systems
4. Sparsity
5. Optimization algorithms
6. Core models and mathematical foundations
7. Next generation software tool

Interested?
The research group ESAT-STADIUS at the university KU Leuven Belgium provides an excellent research environment being active in the broad area of mathematical engineering, including systems and control theory, neural networks and machine learning, nonlinear systems and complex networks, optimization, signal processing, bioinformatics and biomedicine.
The research will be conducted under the supervision of Prof. Johan Suykens. Interested candidates having a solid mathematical background and PhD degree can on-line apply at the website by including CV and motivation letter. For further information on these positions you may contact johan.suykens@esat.kuleuven.be.

--
--
dr. Marco Signoretto

FWO research fellow,
ESAT - STADIUS,
Katholieke Universiteit Leuven,
Kasteelpark Arenberg 10, B-3001 LEUVEN - HEVERLEE (BELGIUM)   Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Matrix Completion on Graphs - implementation -

Vassilis Kalofolias just sent me the following:

Dear Igor, 

I am a student of Pierre Vandergheynst at EPFL and we recently had our paper on "Matrix Completion on Graphs" accepted at NIPS workshop on robustness of high dimensional data.

We have published Matlab code for our algorithm here, I think it could fit nicely somewhere in your blog space :)

https://lts2research.epfl.ch/software/matrix-completion-on-graphs/

I am also attaching the tar file with the code.

Thanks!

best regards,
Vassilis Kalofolias
PhD candidate,
LTS2, EPFL

Matrix Completion on Graphs by Vassilis Kalofolias, Xavier Bresson, Michael Bronstein, Pierre Vandergheynst

The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Although the problem under the standard low rank assumption is NP-hard, Cand\`es and Recht showed that it can be exactly relaxed if the number of observed entries is sufficiently large. In this work, we introduce a novel matrix completion model that makes use of proximity information about rows and columns by assuming they form communities. This assumption makes sense in several real-world problems like in recommender systems, where there are communities of people sharing preferences, while products form clusters that receive similar ratings. Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs. We borrow ideas from manifold learning to constrain our solution to be smooth on these graphs, in order to implicitly force row and column proximities. Our matrix recovery model is formulated as a convex non-smooth optimization problem, for which a well-posed iterative scheme is provided. We study and evaluate the proposed matrix completion on synthetic and real data, showing that the proposed structured low-rank recovery model outperforms the standard matrix completion model in many situations. 

  Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

CS Internship in France: Compatibilité de l'apprentissage par représentations parcimonieuses et de la compression avec pertes / Compatibility between sparse machine learning and lossy compression

Laurent Duval just put out this announcement (in French and to be translated in English soon) :

Internship (2015): learning / compression compatibility

Intitulé de stage

Compatibilité de l'apprentissage par représentations parcimonieuses et de la compression avec pertes [français] (5-6 mois, premier ou second semestre 2015)
 

Internship subject title

Compatibility between sparse machine learning and lossy compression [English, Coming soon] (5-6 months, 2015, first or second semester)

Information

• Description

  De nombreuses expérimentations acquièrent, en flux continu ou par séquence, des signaux ou images caractéristiques d'un phénomène particulier. Des exemples se trouvent à IFPEN dans les données sismiques, l'analyse par contrôle passif par émissions acoustiques (phénomènes de corrosion, diagnostic de batteries), les bancs d'essai moteur (signaux de pression cylindre, caméra rapide), l'expérimentation haut-débit en chimie. Très souvent, ces données sont analysées de manière standardisée par des indicateurs fixés a priori. La comparaison entre différentes expérimentations (par différences, classifications) se réalise le plus souvent sur les indicateurs calculés, sans revenir aux mesures initiales. Le volume croissant de ce type de données, la variabilité des capteurs et des échantillonnages, le fait qu'elles puissent avoir reçu des traitements différents posent deux problèmes distincts : la gestion et l'accès à ces volumes (aspect "big data") et leur exploitation optimale par des méthodes de réduction de dimension d'apprentissage, supervisé ou non (aspect "data science"). Ce projet vise à analyser conjointement les possibilités de représentations comprimées des données et l'extraction d'indicateurs pertinents à différentes échelles caractéristiques, et l'impact du premier aspect (dégradation due à la compression) sur le second (précision/robustesse des indicateurs extraits).
  
  L'objectif du stage est dual. Le premier aspect s'intéressera notamment aux travaux de représentation des signaux/images par des réseaux de convolution à base de techniques multi-échelles (ondelettes) appelés "réseaux de diffusion" (scattering networks), dont les descripteurs (ou empreintes) ont de bonnes propriétés d'invariance en termes de translation, rotation et échelle. Ces descripteurs seront employés à des fins de classification et de détection. Le second aspect portera sur l'évaluation de l'impact de techniques de compression avec pertes sur les résultats précédents, et potentiellement sur le développement de représentations parcimonieuses conjointes aux deux aspects (compression et apprentissage).

  [English, upcoming]

• Encadrement/supervision : Camille Couprie, Laurent Duval (Rueil-Malmaison, 92)
  • Candidature : par email de CV/lettre de motivation à laurent(dod)duval(ad)ifpen(dod)fr et camille(dod)couprie(ad)ifpen(dod)fr
  • Application: email resume/cover letter at laurent(dod)duval(ad)ifpen(dod)fr and camille(dod)couprie(ad)ifpen(dod)fr

Bibliographie/References

• J. Bruna, S. Mallat, Invariant scattering convolution networks, IEEE Trans. on Patt. Anal. and Mach. Int., 2010
• L. Jacques, L. Duval, C. Chaux, G. Peyré, A Panorama on Multiscale Geometric Representations, Intertwining Spatial, Directional and Frequency Selectivity, Signal Processing, 2011
• C. Couprie, C. Farabet, L. Najman, Y. LeCun, Convolutional Nets and Watershed Cuts for Real-Time Semantic Labeling of RGBD Videos, Journal of Machine Learning Research, 2014

 

    Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Upcoming CSJobs and more ....

Yves Wiaux, who soon will have many more friends, just sent me the following

  Hi Igor

Jason McEwen and myself have just been awarded £2M from UK research councils for research on compressive imaging in astronomy and medicine, along with Mike Davies and other colleagues.

Press releases about the funded initiative may be found on Edinburgh Heriot-Watt University website at http://www.hw.ac.uk/news-events/news/pioneering-work-helps-join-dots-across-known-19548.htm ,

and on University College London website at http://www.ucl.ac.uk/mathematical-physical-sciences/maps-news-publication/maps1431 ,

together with a first follow up article in ‘The Conversation’ at http://theconversation.com/monster-telescope-needs-mind-bending-mathematics-to-uncover-secrets-of-the-universe-34563.

Do not hesitate to post [this] on Nuit Blanche if you find it suitable.

Multiple positions will open very soon.

Cheers

Yves

___________________________________

Dr Yves Wiaux, Assoc. Prof., BASP Director

Institute of Sensors, Signals & Systems

School of Engineering & Physical Sciences

Heriot-Watt University, Edinburgh

basp.eps.hw.ac.uk

 

Fantastic news, Yves

One small note: While the conversation piece seems to have actively removed several instances of it, there was still one instance of the word "partial" in the writeup, which, in my view, was not necessary.

  Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

Stable Autoencoding: A Flexible Framework for Regularized Low-Rank Matrix Estimation

In the Matrix Factorization Jungle Page, there is a subsection for subspace clustering that reads like:

Subspace Clustering: A = AX  with unknown X, solve for sparse/other conditions on X 

In recent times, when people were looking for subspace clustering algorithms, the conditions imposed on X were focused on X having a zero main diagonal and sparse entries otherwise. In today's paper, the authors seek a low rank version of X instead and call that matrix/operator a linear autoencoder.

Let us note that product AX now provides an evaluation of the SVD of X. Following this line of thought, subspace clustering could also be construed as another instance of a Linear Autoencoder. Can this approach help in better designing noisy autoencoders as we recently saw [1] ? The question bears asking. Without further afo, here is: Stable Autoencoding: A Flexible Framework for Regularized Low-Rank Matrix Estimation by Julie Josse, Stefan Wager

We develop a framework for low-rank matrix estimation that allows us to transform noise models into regularization schemes via a simple parametric bootstrap. Effectively, our procedure seeks an autoencoding basis for the observed matrix that is robust with respect to the specified noise model. In the simplest case, with an isotropic noise model, our procedure is equivalent to a classical singular value shrinkage estimator. For non-isotropic noise models, however, our method does not reduce to singular value shrinkage, and instead yields new estimators that perform well in experiments. Moreover, by iterating our stable autoencoding scheme, we can automatically generate low-rank estimates without specifying the target rank as a tuning parameter.  

 

From [2]

Let us note that  in the subspace clustering approach of the paper if diag(Z) = 0, then Tr(Z) = 0 which really means that this decomposition yields a volume preserving Z (see Lie Algebra). One then wonders if, like in fluid mechanics, we should be aiming to find a mixed decomposition with a volume preserving transformation (really quantifying the deformation) on the one had and one that quantifies (low) volume change (low rank matrix, the paper featured today) for the autoencoders on the other.

Let us also note that while the aim for these subspace clustering algorithms is to have a zero diagonal, the regularizer may provide a solution that is close enough but is also low rank (see LRR for instance). Let us hope that this approach can provide some light on how to devise nonlinear autoencoders !

Relevant:

• [1] Provable Bounds for Learning Some Deep Representations 
• Autoencoders, Unsupervised Learning, and Deep Architectures by Pierre Baldi 

Subspace clustering algorithms implementation :

• LSR : Robust and Efficient Subspace Segmentation via Least Squares Regression by Canyi Lu, Hai Min, Zhong-Qiu Zhao, Lin Zhu, De-Shuang Huang, and Shuicheng Yan
• LRRSC : Subspace Clustering by Exploiting a Low-Rank Representation with a Symmetric Constraint by Jie Chen, Zhang Yi
• SSC : Sparse Subspace Clustering: Algorithm, Theory, and Applications by Ehsan Elhamifar, Rene Vidal.
• SMCE : Sparse Manifold Clustering and Embedding by Ehsan Elhamifar, Rene Vidal,
• Local Linear Embedding (LLE)

  Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.

NIPS 2014 proceedings are out, what paper did you fancy ?

The NIPS 2014 proceedings are out, what paper did you fancy ?

Advances in Neural Information Processing Systems 27 (NIPS 2014)

The papers below appear in Advances in Neural Information Processing Systems 27 edited by Z. Ghahramani and M. Welling and C. Cortes and N.D. Lawrence and K.Q. Weinberger.
They are proceedings from the conference Neural Information Processing Systems 2014.

• Kernel Mean Estimation via Spectral Filtering Krikamol Muandet, Bharath Sriperumbudur, Bernhard Schölkopf
• Semi-Separable Hamiltonian Monte Carlo for Inference in Bayesian Hierarchical Models Yichuan Zhang, Charles Sutton
• Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G. Andersen, Alex J. Smola, Kai Yu
• The Infinite Mixture of Infinite Gaussian Mixtures Halid Z. Yerebakan, Bartek Rajwa, Murat Dundar
• Robust Classification Under Sample Selection Bias Anqi Liu, Brian Ziebart
• Zeta Hull Pursuits: Learning Nonconvex Data Hulls Yuanjun Xiong, Wei Liu, Deli Zhao, Xiaoou Tang
• Grouping-Based Low-Rank Trajectory Completion and 3D Reconstruction Katerina Fragkiadaki, Marta Salas, Pablo Arbelaez, Jitendra Malik
• Sparse Space-Time Deconvolution for Calcium Image Analysis Ferran Diego Andilla, Fred A. Hamprecht
• Restricted Boltzmann machines modeling human choice Takayuki Osogami, Makoto Otsuka
• Multiscale Fields of Patterns Pedro Felzenszwalb, John G. Oberlin
• large scale canonical correlation analysis with iterative least squares Yichao Lu, Dean P. Foster
• Altitude Training: Strong Bounds for Single-Layer Dropout Stefan Wager, William Fithian, Sida Wang, Percy S. Liang
• Rounding-based Moves for Metric Labeling M. Pawan Kumar
• Parallel Double Greedy Submodular Maximization Xinghao Pan, Stefanie Jegelka, Joseph E. Gonzalez, Joseph K. Bradley, Michael I. Jordan
• Multivariate Regression with Calibration Han Liu, Lie Wang, Tuo Zhao
• Exact Post Model Selection Inference for Marginal Screening Jason D. Lee, Jonathan E. Taylor
• On a Theory of Nonparametric Pairwise Similarity for Clustering: Connecting Clustering to Classification Yingzhen Yang, Feng Liang, Shuicheng Yan, Zhangyang Wang, Thomas S. Huang
• Just-In-Time Learning for Fast and Flexible Inference S. M. Ali Eslami, Daniel Tarlow, Pushmeet Kohli, John Winn
• Fundamental Limits of Online and Distributed Algorithms for Statistical Learning and Estimation Ohad Shamir
• Quantized Kernel Learning for Feature Matching Danfeng Qin, Xuanli Chen, Matthieu Guillaumin, Luc V. Gool
• Parallel Direction Method of Multipliers Huahua Wang, Arindam Banerjee, Zhi-Quan Luo
• (Almost) No Label No Cry Giorgio Patrini, Richard Nock, Tiberio Caetano, Paul Rivera
• Stochastic Multi-Armed-Bandit Problem with Non-stationary Rewards Yonatan Gur, Assaf Zeevi, Omar Besbes
• Object Localization based on Structural SVM using Privileged Information Jan Feyereisl, Suha Kwak, Jeany Son, Bohyung Han
• Multi-View Perceptron: a Deep Model for Learning Face Identity and View Representations Zhenyao Zhu, Ping Luo, Xiaogang Wang, Xiaoou Tang
• Shape and Illumination from Shading using the Generic Viewpoint Assumption Daniel Zoran, Dilip Krishnan, Jose Bento, Bill Freeman
• Parallel Sampling of HDPs using Sub-Cluster Splits Jason Chang, John W. Fisher III
• From MAP to Marginals: Variational Inference in Bayesian Submodular Models Josip Djolonga, Andreas Krause
• Robust Logistic Regression and Classification Jiashi Feng, Huan Xu, Shie Mannor, Shuicheng Yan
• Extracting Certainty from Uncertainty: Transductive Pairwise Classification from Pairwise Similarities Tianbao Yang, Rong Jin
• A Unified Semantic Embedding: Relating Taxonomies and Attributes Sung Ju Hwang, Leonid Sigal
• Transportability from Multiple Environments with Limited Experiments: Completeness Results Elias Bareinboim, Judea Pearl
• Augmentative Message Passing for Traveling Salesman Problem and Graph Partitioning Siamak Ravanbakhsh, Reihaneh Rabbany, Russell Greiner
• Causal Inference through a Witness Protection Program Ricardo Silva, Robin Evans
• Incremental Clustering: The Case for Extra Clusters Margareta Ackerman, Sanjoy Dasgupta
• Multi-scale Graphical Models for Spatio-Temporal Processes firdaus janoos, Huseyin Denli, Niranjan Subrahmanya
• Iterative Neural Autoregressive Distribution Estimator NADE-k Tapani Raiko, Yao Li, Kyunghyun Cho, Yoshua Bengio
• Sparse PCA via Covariance Thresholding Yash Deshpande, Andrea Montanari
• Low-dimensional models of neural population activity in sensory cortical circuits Evan W. Archer, Urs Koster, Jonathan W. Pillow, Jakob H. Macke
• A Synaptical Story of Persistent Activity with Graded Lifetime in a Neural System Yuanyuan Mi, Luozheng Li, Dahui Wang, Si Wu
• A Representation Theory for Ranking Functions Harsh H. Pareek, Pradeep K. Ravikumar
• Near-optimal sample compression for nearest neighbors Lee-Ad Gottlieb, Aryeh Kontorovitch, Pinhas Nisnevitch
• Combinatorial Pure Exploration of Multi-Armed Bandits Shouyuan Chen, Tian Lin, Irwin King, Michael R. Lyu, Wei Chen
• Log-Hilbert-Schmidt metric between positive definite operators on Hilbert spaces Minh Ha Quang, Marco San Biagio, Vittorio Murino
• Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model Debarghya Ghoshdastidar, Ambedkar Dukkipati
• Spectral Clustering of graphs with the Bethe Hessian Alaa Saade, Florent Krzakala, Lenka Zdeborova
• Fast and Robust Least Squares Estimation in Corrupted Linear Models Brian McWilliams, Gabriel Krummenacher, Mario Lucic, Joachim M. Buhmann
• Local Decorrelation For Improved Pedestrian Detection Woonhyun Nam, Piotr Dollar, Joon Hee Han
• Robust Kernel Density Estimation by Scaling and Projection in Hilbert Space Robert A. Vandermeulen, Clayton Scott
• Beyond Disagreement-Based Agnostic Active Learning Chicheng Zhang, Kamalika Chaudhuri
• Bayes-Adaptive Simulation-based Search with Value Function Approximation Arthur Guez, Nicolas Heess, David Silver, Peter Dayan
• A State-Space Model for Decoding Auditory Attentional Modulation from MEG in a Competing-Speaker Environment Sahar Akram, Jonathan Z. Simon, Shihab A. Shamma, Behtash Babadi
• Active Regression by Stratification Sivan Sabato, Remi Munos
• Sensory Integration and Density Estimation Joseph G. Makin, Philip N. Sabes
• Learning Deep Features for Scene Recognition using Places Database Bolei Zhou, Agata Lapedriza, Jianxiong Xiao, Antonio Torralba, Aude Oliva
• A Complete Variational Tracker Ryan D. Turner, Steven Bottone, Bhargav Avasarala
• Spike Frequency Adaptation Implements Anticipative Tracking in Continuous Attractor Neural Networks Yuanyuan Mi, C. C. Alan Fung, K. Y. Michael Wong, Si Wu
• Efficient Sampling for Learning Sparse Additive Models in High Dimensions Hemant Tyagi, Bernd Gärtner, Andreas Krause
• Deep Joint Task Learning for Generic Object Extraction Xiaolong Wang, Liliang Zhang, Liang Lin, Zhujin Liang, Wangmeng Zuo
• Robust Bayesian Max-Margin Clustering Changyou Chen, Jun Zhu, Xinhua Zhang
• Permutation Diffusion Maps (PDM) with Application to the Image Association Problem in Computer Vision Deepti Pachauri, Risi Kondor, Gautam Sargur, Vikas Singh
• Bounded Regret for Finite-Armed Structured Bandits Tor Lattimore, Remi Munos
• Coresets for k-Segmentation of Streaming Data Guy Rosman, Mikhail Volkov, Dan Feldman, John W. Fisher III, Daniela Rus
• Two-Stream Convolutional Networks for Action Recognition in Videos Karen Simonyan, Andrew Zisserman
• Discovering Structure in High-Dimensional Data Through Correlation Explanation Greg Ver Steeg, Aram Galstyan
• Positive Curvature and Hamiltonian Monte Carlo Christof Seiler, Simon Rubinstein-Salzedo, Susan Holmes
• Learning Mixed Multinomial Logit Model from Ordinal Data Sewoong Oh, Devavrat Shah
• Near-optimal Reinforcement Learning in Factored MDPs Ian Osband, Benjamin Van Roy
• Efficient learning by implicit exploration in bandit problems with side observations Tomáš Kocák, Gergely Neu, Michal Valko, Remi Munos
• Repeated Contextual Auctions with Strategic Buyers Kareem Amin, Afshin Rostamizadeh, Umar Syed
• Recursive Inversion Models for Permutations Christopher Meek, Marina Meila
• On the Convergence Rate of Decomposable Submodular Function Minimization Robert Nishihara, Stefanie Jegelka, Michael I. Jordan
• New Rules for Domain Independent Lifted MAP Inference Happy Mittal, Prasoon Goyal, Vibhav G. Gogate, Parag Singla
• PAC-Bayesian AUC classification and scoring James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
• Optimization Methods for Sparse Pseudo-Likelihood Graphical Model Selection Sang Oh, Onkar Dalal, Kshitij Khare, Bala Rajaratnam
• On Prior Distributions and Approximate Inference for Structured Variables Oluwasanmi O. Koyejo, Rajiv Khanna, Joydeep Ghosh, Russell Poldrack
• On Iterative Hard Thresholding Methods for High-dimensional M-Estimation Prateek Jain, Ambuj Tewari, Purushottam Kar
• Online and Stochastic Gradient Methods for Non-decomposable Loss Functions Purushottam Kar, Harikrishna Narasimhan, Prateek Jain
• Analysis of Learning from Positive and Unlabeled Data Marthinus C. du Plessis, Gang Niu, Masashi Sugiyama
• Dimensionality Reduction with Subspace Structure Preservation Devansh Arpit, Ifeoma Nwogu, Venu Govindaraju
• Constrained convex minimization via model-based excessive gap Quoc Tran-Dinh, Volkan Cevher
• Poisson Process Jumping between an Unknown Number of Rates: Application to Neural Spike Data Florian Stimberg, Andreas Ruttor, Manfred Opper
• Probabilistic ODE Solvers with Runge-Kutta Means Michael Schober, David K. Duvenaud, Philipp Hennig
• Optimal decision-making with time-varying evidence reliability Jan Drugowitsch, Ruben Moreno-Bote, Alexandre Pouget
• Learning Shuffle Ideals Under Restricted Distributions Dongqu Chen
• Discriminative Unsupervised Feature Learning with Convolutional Neural Networks Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, Thomas Brox
• Distance-Based Network Recovery under Feature Correlation David Adametz, Volker Roth
• Bandit Convex Optimization: Towards Tight Bounds Elad Hazan, Kfir Levy
• Projective dictionary pair learning for pattern classification Shuhang Gu, Lei Zhang, Wangmeng Zuo, Xiangchu Feng
• Provable Submodular Minimization using Wolfe's Algorithm Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
• Exploiting easy data in online optimization Amir Sani, Gergely Neu, Alessandro Lazaric
• Sparse Multi-Task Reinforcement Learning Daniele Calandriello, Alessandro Lazaric, Marcello Restelli
• Best-Arm Identification in Linear Bandits Marta Soare, Alessandro Lazaric, Remi Munos
• Mind the Nuisance: Gaussian Process Classification using Privileged Noise Daniel Hernández-lobato, Viktoriia Sharmanska, Kristian Kersting, Christoph H. Lampert, Novi Quadrianto
• Tight Bounds for Influence in Diffusion Networks and Application to Bond Percolation and Epidemiology Remi Lemonnier, Kevin Scaman, Nicolas Vayatis
• On the Computational Efficiency of Training Neural Networks Roi Livni, Shai Shalev-Shwartz, Ohad Shamir
• Self-Adaptable Templates for Feature Coding Xavier Boix, Gemma Roig, Salomon Diether, Luc V. Gool
• Multilabel Structured Output Learning with Random Spanning Trees of Max-Margin Markov Networks Mario Marchand, Hongyu Su, Emilie Morvant, Juho Rousu, John S. Shawe-Taylor
• Stochastic Network Design in Bidirected Trees xiaojian wu, Daniel R. Sheldon, Shlomo Zilberstein
• Learning convolution filters for inverse covariance estimation of neural network connectivity George Mohler
• SerialRank: Spectral Ranking using Seriation Fajwel Fogel, Alexandre d'Aspremont, Milan Vojnovic
• Clamping Variables and Approximate Inference Adrian Weller, Tony Jebara
• Predictive Entropy Search for Efficient Global Optimization of Black-box Functions José Miguel Hernández-Lobato, Matthew W. Hoffman, Zoubin Ghahramani
• A Block-Coordinate Descent Approach for Large-scale Sparse Inverse Covariance Estimation Eran Treister, Javier S. Turek
• Efficient Inference of Continuous Markov Random Fields with Polynomial Potentials Shenlong Wang, Alex Schwing, Raquel Urtasun
• Scalable Methods for Nonnegative Matrix Factorizations of Near-separable Tall-and-skinny Matrices Austin R. Benson, Jason D. Lee, Bartek Rajwa, David F. Gleich
• Inferring synaptic conductances from spike trains with a biophysically inspired point process model Kenneth W. Latimer, E. J. Chichilnisky, Fred Rieke, Jonathan W. Pillow
• Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights Daniel Soudry, Itay Hubara, Ron Meir
• Incremental Local Gaussian Regression Franziska Meier, Philipp Hennig, Stefan Schaal
• General Table Completion using a Bayesian Nonparametric Model Isabel Valera, Zoubin Ghahramani
• Universal Option Models hengshuai yao, Csaba Szepesvari, Richard S. Sutton, Joseph Modayil, Shalabh Bhatnagar
• Approximating Hierarchical MV-sets for Hierarchical Clustering Assaf Glazer, Omer Weissbrod, Michael Lindenbaum, Shaul Markovitch
• Constant Nullspace Strong Convexity and Fast Convergence of Proximal Methods under High-Dimensional Settings En-Hsu Yen, Cho-Jui Hsieh, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm Deanna Needell, Rachel Ward, Nati Srebro
• A Framework for Testing Identifiability of Bayesian Models of Perception Luigi Acerbi, Wei Ji Ma, Sethu Vijayakumar
• Optimistic Planning in Markov Decision Processes Using a Generative Model Balázs Szörényi, Gunnar Kedenburg, Remi Munos
• Gaussian Process Volatility Model Yue Wu, José Miguel Hernández-Lobato, Zoubin Ghahramani
• A Safe Screening Rule for Sparse Logistic Regression Jie Wang, Jiayu Zhou, Jun Liu, Peter Wonka, Jieping Ye
• Hardness of parameter estimation in graphical models Guy Bresler, David Gamarnik, Devavrat Shah
• Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics Sergey Levine, Pieter Abbeel
• Magnitude-sensitive preference formation` Nisheeth Srivastava, Ed Vul, Paul R. Schrater
• Extreme bandits Alexandra Carpentier, Michal Valko
• Distributed Estimation, Information Loss and Exponential Families Qiang Liu, Alex T. Ihler
• Non-convex Robust PCA Praneeth Netrapalli, Niranjan U N, Sujay Sanghavi, Animashree Anandkumar, Prateek Jain
• Simultaneous Model Selection and Optimization through Parameter-free Stochastic Learning Francesco Orabona
• Learning From Weakly Supervised Data by The Expectation Loss SVM (e-SVM) algorithm Jun Zhu, Junhua Mao, Alan L. Yuille
• Message Passing Inference for Large Scale Graphical Models with High Order Potentials Jian Zhang, Alex Schwing, Raquel Urtasun
• Encoding High Dimensional Local Features by Sparse Coding Based Fisher Vectors Lingqiao Liu, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang
• Dependent nonparametric trees for dynamic hierarchical clustering Kumar Dubey, Qirong Ho, Sinead A. Williamson, Eric P. Xing
• Causal Strategic Inference in Networked Microfinance Economies Mohammad T. Irfan, Luis E. Ortiz
• Learning Multiple Tasks in Parallel with a Shared Annotator Haim Cohen, Koby Crammer
• Reducing the Rank in Relational Factorization Models by Including Observable Patterns Maximilian Nickel, Xueyan Jiang, Volker Tresp
• Clustering from Labels and Time-Varying Graphs Shiau Hong Lim, Yudong Chen, Huan Xu
• From Stochastic Mixability to Fast Rates Nishant A. Mehta, Robert C. Williamson
• Recovery of Coherent Data via Low-Rank Dictionary Pursuit Guangcan Liu, Ping Li
• Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit Karin C. Knudson, Jacob Yates, Alexander Huk, Jonathan W. Pillow
• Analysis of Variational Bayesian Latent Dirichlet Allocation: Weaker Sparsity Than MAP Shinichi Nakajima, Issei Sato, Masashi Sugiyama, Kazuho Watanabe, Hiroko Kobayashi
• Discovering, Learning and Exploiting Relevance Cem Tekin, Mihaela Van Der Schaar
• Divide-and-Conquer Learning by Anchoring a Conical Hull Tianyi Zhou, Jeff A. Bilmes, Carlos Guestrin
• Extended and Unscented Gaussian Processes Daniel M. Steinberg, Edwin V. Bonilla
• Spectral Methods meet EM: A Provably Optimal Algorithm for Crowdsourcing Yuchen Zhang, Xi Chen, Dengyong Zhou, Michael I. Jordan
• Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation Emily L. Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, Rob Fergus
• Learning to Discover Efficient Mathematical Identities Wojciech Zaremba, Karol Kurach, Rob Fergus
• The Large Margin Mechanism for Differentially Private Maximization Kamalika Chaudhuri, Daniel J. Hsu, Shuang Song
• DFacTo: Distributed Factorization of Tensors Joon Hee Choi, S. Vishwanathan
• Localized Data Fusion for Kernel k-Means Clustering with Application to Cancer Biology Mehmet Gönen, Adam A. Margolin
• Conditional Swap Regret and Conditional Correlated Equilibrium Mehryar Mohri, Scott Yang
• Mode Estimation for High Dimensional Discrete Tree Graphical Models Chao Chen, Han Liu, Dimitris Metaxas, Tianqi Zhao
• Large-scale L-BFGS using MapReduce Weizhu Chen, Zhenghao Wang, Jingren Zhou
• Submodular Attribute Selection for Action Recognition in Video Jingjing Zheng, Zhuolin Jiang, Rama Chellappa, Jonathon P. Phillips
• Efficient Structured Matrix Rank Minimization Adams Wei Yu, Wanli Ma, Yaoliang Yu, Jaime Carbonell, Suvrit Sra
• On Integrated Clustering and Outlier Detection Lionel Ott, Linsey Pang, Fabio T. Ramos, Sanjay Chawla
• A Drifting-Games Analysis for Online Learning and Applications to Boosting Haipeng Luo, Robert E. Schapire
• Projecting Markov Random Field Parameters for Fast Mixing Xianghang Liu, Justin Domke
• Automatic Discovery of Cognitive Skills to Improve the Prediction of Student Learning Robert V. Lindsey, Mohammad Khajah, Michael C. Mozer
• Near-Optimal-Sample Estimators for Spherical Gaussian Mixtures Ananda Theertha Suresh, Alon Orlitsky, Jayadev Acharya, Ashkan Jafarpour
• Automated Variational Inference for Gaussian Process Models Trung V. Nguyen, Edwin V. Bonilla
• Learning Mixtures of Submodular Functions for Image Collection Summarization Sebastian Tschiatschek, Rishabh K. Iyer, Haochen Wei, Jeff A. Bilmes
• Robust Tensor Decomposition with Gross Corruption Quanquan Gu, Huan Gui, Jiawei Han
• Provable Tensor Factorization with Missing Data Prateek Jain, Sewoong Oh
• Parallel Successive Convex Approximation for Nonsmooth Nonconvex Optimization Meisam Razaviyayn, Mingyi Hong, Zhi-Quan Luo, Jong-Shi Pang
• Using Convolutional Neural Networks to Recognize Rhythm Stimuli from Electroencephalography Recordings Sebastian Stober, Daniel J. Cameron, Jessica A. Grahn
• Blossom Tree Graphical Models Zhe Liu, John Lafferty
• Model-based Reinforcement Learning and the Eluder Dimension Ian Osband, Benjamin Van Roy
• Minimax-optimal Inference from Partial Rankings Bruce Hajek, Sewoong Oh, Jiaming Xu
• Spectral Methods for Indian Buffet Process Inference Hsiao-Yu Tung, Alex J. Smola
• On the Statistical Consistency of Plug-in Classifiers for Non-decomposable Performance Measures Harikrishna Narasimhan, Rohit Vaish, Shivani Agarwal
• Top Rank Optimization in Linear Time Nan Li, Rong Jin, Zhi-Hua Zhou
• Spectral Methods for Supervised Topic Models Yining Wang, Jun Zhu
• Graphical Models for Recovering Probabilistic and Causal Queries from Missing Data Karthika Mohan, Judea Pearl
• Sparse PCA with Oracle Property Quanquan Gu, Zhaoran Wang, Han Liu
• Unsupervised Transcription of Piano Music Taylor Berg-Kirkpatrick, Jacob Andreas, Dan Klein
• Decoupled Variational Gaussian Inference Mohammad E. Khan
• Estimation with Norm Regularization Arindam Banerjee, Sheng Chen, Farideh Fazayeli, Vidyashankar Sivakumar
• Decomposing Parameter Estimation Problems Khaled S. Refaat, Arthur Choi, Adnan Darwiche
• Stochastic Proximal Gradient Descent with Acceleration Techniques Atsushi Nitanda
• Learning to Optimize via Information-Directed Sampling Dan Russo, Benjamin Van Roy
• Covariance shrinkage for autocorrelated data Daniel Bartz, Klaus-Robert Müller
• Do Convnets Learn Correspondence? Jonathan L. Long, Ning Zhang, Trevor Darrell
• The Blinded Bandit: Learning with Adaptive Feedback Ofer Dekel, Elad Hazan, Tomer Koren
• Convex Optimization Procedure for Clustering: Theoretical Revisit Changbo Zhu, Huan Xu, Chenlei Leng, Shuicheng Yan
• Sparse Bayesian structure learning with “dependent relevance determination” priors Anqi Wu, Mijung Park, Oluwasanmi O. Koyejo, Jonathan W. Pillow
• Weakly-supervised Discovery of Visual Pattern Configurations Hyun Oh Song, Yong Jae Lee, Stefanie Jegelka, Trevor Darrell
• SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives Aaron Defazio, Francis Bach, Simon Lacoste-Julien
• Exclusive Feature Learning on Arbitrary Structures via \ell_{1,2}-norm Deguang Kong, Ryohei Fujimaki, Ji Liu, Feiping Nie, Chris Ding
• Time--Data Tradeoffs by Aggressive Smoothing John J. Bruer, Joel A. Tropp, Volkan Cevher, Stephen Becker
• Distributed Power-law Graph Computing: Theoretical and Empirical Analysis Cong Xie, Ling Yan, Wu-Jun Li, Zhihua Zhang
• A Multi-World Approach to Question Answering about Real-World Scenes based on Uncertain Input Mateusz Malinowski, Mario Fritz
• Efficient Partial Monitoring with Prior Information Hastagiri P. Vanchinathan, Gábor Bartók, Andreas Krause
• Distributed Parameter Estimation in Probabilistic Graphical Models Yariv D. Mizrahi, Misha Denil, Nando de Freitas
• Unsupervised Deep Haar Scattering on Graphs Xu Chen, Xiuyuan Cheng, Stephane Mallat
• Online Optimization for Max-Norm Regularization Jie Shen, Huan Xu, Ping Li
• Probabilistic low-rank matrix completion on finite alphabets Jean Lafond, Olga Klopp, Eric Moulines, Joseph Salmon
• Articulated Pose Estimation by a Graphical Model with Image Dependent Pairwise Relations Xianjie Chen, Alan L. Yuille
• Bayesian Inference for Structured Spike and Slab Priors Michael R. Andersen, Ole Winther, Lars K. Hansen
• Bayesian Nonlinear Support Vector Machines and Discriminative Factor Modeling Ricardo Henao, Xin Yuan, Lawrence Carin
• Generalized Higher-Order Orthogonal Iteration for Tensor Decomposition and Completion Yuanyuan Liu, Fanhua Shang, Wei Fan, James Cheng, Hong Cheng
• Making Pairwise Binary Graphical Models Attractive Nicholas Ruozzi, Tony Jebara
• Low Rank Approximation Lower Bounds in Row-Update Streams David Woodruff
• Deep Convolutional Neural Network for Image Deconvolution Li Xu, Jimmy S. Ren, Ce Liu, Jiaya Jia
• Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation Jonathan J. Tompson, Arjun Jain, Yann LeCun, Christoph Bregler
• Learning Generative Models with Visual Attention Yichuan Tang, Nitish Srivastava, Ruslan R. Salakhutdinov
• Metric Learning for Temporal Sequence Alignment Rémi Lajugie, Damien Garreau, Francis Bach, Sylvain Arlot
• Learning Optimal Commitment to Overcome Insecurity Avrim Blum, Nika Haghtalab, Ariel D. Procaccia
• How hard is my MDP?" The distribution-norm to the rescue" Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor
• Near-Optimal Density Estimation in Near-Linear Time Using Variable-Width Histograms Siu On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun
• An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P, Stanislas Lauly, Hugo Larochelle, Mitesh Khapra, Balaraman Ravindran, Vikas C. Raykar, Amrita Saha
• Sequential Monte Carlo for Graphical Models Christian Andersson Naesseth, Fredrik Lindsten, Thomas B. Schön
• Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers Mehryar Mohri, Andres Munoz
• Optimal prior-dependent neural population codes under shared input noise Agnieszka Grabska-Barwinska, Jonathan W. Pillow
• Deep Fragment Embeddings for Bidirectional Image Sentence Mapping Andrej Karpathy, Armand Joulin, Fei Fei F. Li
• Flexible Transfer Learning under Support and Model Shift Xuezhi Wang, Jeff Schneider
• Probabilistic Differential Dynamic Programming Yunpeng Pan, Evangelos Theodorou
• Predicting Useful Neighborhoods for Lazy Local Learning Aron Yu, Kristen Grauman
• Modeling Deep Temporal Dependencies with Recurrent Grammar Cells"" Vincent Michalski, Roland Memisevic, Kishore Konda
• Generalized Dantzig Selector: Application to the k-support norm Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
• Neurons as Monte Carlo Samplers: Bayesian Inference and Learning in Spiking Networks Yanping Huang, Rajesh P. Rao
• The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin, Julie A. Shah
• Latent Support Measure Machines for Bag-of-Words Data Classification Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada
• Local Linear Convergence of Forward--Backward under Partial Smoothness Jingwei Liang, Jalal Fadili, Gabriel Peyré
• RAAM: The Benefits of Robustness in Approximating Aggregated MDPs in Reinforcement Learning Marek Petrik, Dharmashankar Subramanian
• Deep Learning Face Representation by Joint Identification-Verification Yi Sun, Yuheng Chen, Xiaogang Wang, Xiaoou Tang
• A provable SVD-based algorithm for learning topics in dominant admixture corpus Trapit Bansal, Chiranjib Bhattacharyya, Ravindran Kannan
• QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models Cho-Jui Hsieh, Inderjit S. Dhillon, Pradeep K. Ravikumar, Stephen Becker, Peder A. Olsen
• General Stochastic Networks for Classification Matthias Zöhrer, Franz Pernkopf
• Spatio-temporal Representations of Uncertainty in Spiking Neural Networks Cristina Savin, Sophie Denève
• Attentional Neural Network: Feature Selection Using Cognitive Feedback Qian Wang, Jiaxing Zhang, Sen Song, Zheng Zhang
• Convolutional Neural Network Architectures for Matching Natural Language Sentences Baotian Hu, Zhengdong Lu, Hang Li, Qingcai Chen
• Scalable Non-linear Learning with Adaptive Polynomial Expansions Alekh Agarwal, Alina Beygelzimer, Daniel J. Hsu, John Langford, Matus J. Telgarsky
• On the relations of LFPs & Neural Spike Trains David E. Carlson, Jana Schaich Borg, Kafui Dzirasa, Lawrence Carin
• Diverse Sequential Subset Selection for Supervised Video Summarization Boqing Gong, Wei-Lun Chao, Kristen Grauman, Fei Sha
• Self-Paced Learning with Diversity Lu Jiang, Deyu Meng, Shoou-I Yu, Zhenzhong Lan, Shiguang Shan, Alexander Hauptmann
• Feature Cross-Substitution in Adversarial Classification Bo Li, Yevgeniy Vorobeychik
• Deep Recursive Neural Networks for Compositionality in Language Ozan Irsoy, Claire Cardie
• Inference by Learning: Speeding-up Graphical Model Optimization via a Coarse-to-Fine Cascade of Pruning Classifiers Bruno Conejo, Nikos Komodakis, Sebastien Leprince, Jean Philippe Avouac
• A Filtering Approach to Stochastic Variational Inference Neil Houlsby, David Blei
• Optimizing F-Measures by Cost-Sensitive Classification Shameem Puthiya Parambath, Nicolas Usunier, Yves Grandvalet
• Two-Layer Feature Reduction for Sparse-Group Lasso via Decomposition of Convex Sets Jie Wang, Jieping Ye
• Improved Multimodal Deep Learning with Variation of Information Kihyuk Sohn, Wenling Shang, Honglak Lee
• PEWA: Patch-based Exponentially Weighted Aggregation for image denoising Charles Kervrann
• Elementary Estimators for Graphical Models Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
• Beyond the Birkhoff Polytope: Convex Relaxations for Vector Permutation Problems Cong Han Lim, Stephen Wright
• Neural Word Embedding as Implicit Matrix Factorization Omer Levy, Yoav Goldberg
• Multi-Resolution Cascades for Multiclass Object Detection Mohammad Saberian, Nuno Vasconcelos
• Median Selection Subset Aggregation for Parallel Inference Xiangyu Wang, Peichao Peng, David B. Dunson
• Recurrent Models of Visual Attention Volodymyr Mnih, Nicolas Heess, Alex Graves, koray kavukcuoglu
• Tree-structured Gaussian Process Approximations Thang D. Bui, Richard E. Turner
• Active Learning and Best-Response Dynamics Maria-Florina F. Balcan, Christopher Berlind, Avrim Blum, Emma Cohen, Kaushik Patnaik, Le Song
• Analog Memories in a Balanced Rate-Based Network of E-I Neurons Dylan Festa, Guillaume Hennequin, Mate Lengyel
• Fast Sampling-Based Inference in Balanced Neuronal Networks Guillaume Hennequin, Laurence Aitchison, Mate Lengyel
• Spectral Learning of Mixture of Hidden Markov Models Cem Subakan, Johannes Traa, Paris Smaragdis
• Subspace Embeddings for the Polynomial Kernel Haim Avron, Huy Nguyen, David Woodruff
• A Boosting Framework on Grounds of Online Learning Tofigh Naghibi Mohamadpoor, Beat Pfister
• A Dual Algorithm for Olfactory Computation in the Locust Brain Sina Tootoonian, Mate Lengyel
• Advances in Learning Bayesian Networks of Bounded Treewidth Siqi Nie, Denis D. Maua, Cassio P. de Campos, Qiang Ji
• Learning the Learning Rate for Prediction with Expert Advice Wouter M. Koolen, Tim van Erven, Peter Grünwald
• On the Information Theoretic Limits of Learning Ising Models Rashish Tandon, Karthikeyan Shanmugam, Pradeep K. Ravikumar, Alexandros G. Dimakis
• Efficient Optimization for Average Precision SVM Pritish Mohapatra, C.V. Jawahar, M. Pawan Kumar
• Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) Anshumali Shrivastava, Ping Li
• A framework for studying synaptic plasticity with neural spike train data Scott Linderman, Christopher H. Stock, Ryan P. Adams
• Randomized Experimental Design for Causal Graph Discovery Huining Hu, Zhentao Li, Adrian R. Vetta
• A Multiplicative Model for Learning Distributed Text-Based Attribute Representations Ryan Kiros, Richard Zemel, Ruslan R. Salakhutdinov
• Learning Chordal Markov Networks by Dynamic Programming Kustaa Kangas, Mikko Koivisto, Teppo Niinimäki
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network David Eigen, Christian Puhrsch, Rob Fergus
• Proximal Quasi-Newton for Computationally Intensive L1-regularized M-estimators Kai Zhong, En-Hsu Yen, Inderjit S. Dhillon, Pradeep K. Ravikumar
• A Probabilistic Framework for Multimodal Retrieval using Integrative Indian Buffet Process Bahadir Ozdemir, Larry S. Davis
• Searching for Higgs Boson Decay Modes with Deep Learning Peter J. Sadowski, Daniel Whiteson, Pierre Baldi
• Structure Regularization for Structured Prediction Xu Sun
• On Multiplicative Multitask Feature Learning Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun
• Multivariate f-divergence Estimation With Confidence Kevin Moon, Alfred Hero
• Generalized Unsupervised Manifold Alignment Zhen Cui, Hong Chang, Shiguang Shan, Xilin Chen
• Smoothed Gradients for Stochastic Variational Inference Stephan Mandt, David Blei
• Recursive Context Propagation Network for Semantic Scene Labeling Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu
• Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space En-Hsu Yen, Ting-Wei Lin, Shou-De Lin, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Optimal Teaching for Limited-Capacity Human Learners Kaustubh R. Patil, Xiaojin Zhu, Łukasz Kopeć, Bradley C. Love
• Shaping Social Activity by Incentivizing Users Mehrdad Farajtabar, Nan Du, Manuel Gomez-Rodriguez, Isabel Valera, Hongyuan Zha, Le Song
• Analysis of Brain States from Multi-Region LFP Time-Series Kyle R. Ulrich, David E. Carlson, Wenzhao Lian, Jana S. Borg, Kafui Dzirasa, Lawrence Carin
• Reputation-based Worker Filtering in Crowdsourcing Srikanth Jagabathula, Lakshminarayanan Subramanian, Ashwin Venkataraman
• Multi-Class Deep Boosting Vitaly Kuznetsov, Mehryar Mohri, Umar Syed
• A Differential Equation for Modeling Nesterov’s Accelerated Gradient Method: Theory and Insights Weijie Su, Stephen Boyd, Emmanuel Candes
• Difference of Convex Functions Programming for Reinforcement Learning Bilal Piot, Matthieu Geist, Olivier Pietquin
• Design Principles of the Hippocampal Cognitive Map Kimberly L. Stachenfeld, Matthew Botvinick, Samuel J. Gershman
• Deep Symmetry Networks Robert Gens, Pedro M. Domingos
• Nonparametric Bayesian inference on multivariate exponential families William R. Vega-Brown, Marek Doniec, Nicholas G. Roy
• Optimal rates for k-NN density and mode estimation Sanjoy Dasgupta, Samory Kpotufe
• Feedforward Learning of Mixture Models Matthew Lawlor, Steven W. Zucker
• Diverse Randomized Agents Vote to Win Albert Jiang, Leandro Soriano Marcolino, Ariel D. Procaccia, Tuomas Sandholm, Nisarg Shah, Milind Tambe
• Ranking via Robust Binary Classification Hyokun Yun, Parameswaran Raman, S. Vishwanathan
• Distributed Balanced Clustering via Mapping Coresets Mohammadhossein Bateni, Aditya Bhaskara, Silvio Lattanzi, Vahab Mirrokni
• Augur: Data-Parallel Probabilistic Modeling Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti, Adam C. Pocock, Stephen Green, Guy L. Steele
• Learning Mixtures of Ranking Models Pranjal Awasthi, Avrim Blum, Or Sheffet, Aravindan Vijayaraghavan
• Controlling privacy in recommender systems Yu Xin, Tommi Jaakkola
• Convolutional Kernel Networks Julien Mairal, Piotr Koniusz, Zaid Harchaoui, Cordelia Schmid
• Fairness in Multi-Agent Sequential Decision-Making Chongjie Zhang, Julie A. Shah
• Submodular meets Structured: Finding Diverse Subsets in Exponentially-Large Structured Item Sets Adarsh Prasad, Stefanie Jegelka, Dhruv Batra
• Do Deep Nets Really Need to be Deep? Jimmy Ba, Rich Caruana
• Dynamic Rank Factor Model for Text Streams Shaobo Han, Lin Du, Esther Salazar, Lawrence Carin
• Generative Adversarial Nets Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
• Testing Unfaithful Gaussian Graphical Models De Wen Soh, Sekhar C. Tatikonda
• Global Sensitivity Analysis for MAP Inference in Graphical Models Jasper De Bock, Cassio P. de Campos, Alessandro Antonucci
• Deconvolution of High Dimensional Mixtures via Boosting, with Application to Diffusion-Weighted MRI of Human Brain Charles Y. Zheng, Franco Pestilli, Ariel Rokem
• Efficient Minimax Signal Detection on Graphs Jing Qian, Venkatesh Saligrama
• Cone-Constrained Principal Component Analysis Yash Deshpande, Andrea Montanari, Emile Richard
• On Communication Cost of Distributed Statistical Estimation and Dimensionality Ankit Garg, Tengyu Ma, Huy Nguyen
• Computing Nash Equilibria in Generalized Interdependent Security Games Hau Chan, Luis E. Ortiz
• Consistent Binary Classification with Generalized Performance Metrics Oluwasanmi O. Koyejo, Nagarajan Natarajan, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Greedy Subspace Clustering Dohyung Park, Constantine Caramanis, Sujay Sanghavi
• Deterministic Symmetric Positive Semidefinite Matrix Completion William E. Bishop, Byron M. Yu
• Multi-Step Stochastic ADMM in High Dimensions: Applications to Sparse Optimization and Matrix Decomposition Hanie Sedghi, Anima Anandkumar, Edmond Jonckheere
• Online combinatorial optimization with stochastic decision sets and adversarial losses Gergely Neu, Michal Valko
• Sampling for Inference in Probabilistic Models with Fast Bayesian Quadrature Tom Gunter, Michael A. Osborne, Roman Garnett, Philipp Hennig, Stephen J. Roberts
• Multi-Scale Spectral Decomposition of Massive Graphs Si Si, Donghyuk Shin, Inderjit S. Dhillon, Beresford N. Parlett
• The limits of squared Euclidean distance regularization Michal Derezinski, Manfred K. Warmuth
• Bregman Alternating Direction Method of Multipliers Huahua Wang, Arindam Banerjee
• Multitask learning meets tensor factorization: task imputation via convex optimization Kishan Wimalawarne, Masashi Sugiyama, Ryota Tomioka
• On Model Parallelization and Scheduling Strategies for Distributed Machine Learning Seunghak Lee, Jin Kyu Kim, Xun Zheng, Qirong Ho, Garth A. Gibson, Eric P. Xing
• Scalable Inference for Neuronal Connectivity from Calcium Imaging Alyson K. Fletcher, Sundeep Rangan
• Structure learning of antiferromagnetic Ising models Guy Bresler, David Gamarnik, Devavrat Shah
• The Noisy Power Method: A Meta Algorithm with Applications Moritz Hardt, Eric Price
• Algorithm selection by rational metareasoning as a model of human strategy selection Falk Lieder, Dillon Plunkett, Jessica B. Hamrick, Stuart J. Russell, Nicholas Hay, Thomas Griffiths
• Extremal Mechanisms for Local Differential Privacy Peter Kairouz, Sewoong Oh, Pramod Viswanath
• Global Belief Recursive Neural Networks Romain Paulus, Richard Socher, Christopher D. Manning
• A statistical model for tensor PCA Emile Richard, Andrea Montanari
• Real-Time Decoding of an Integrate and Fire Encoder Shreya Saxena, Munther Dahleh
• Delay-Tolerant Algorithms for Asynchronous Distributed Online Learning Brendan McMahan, Matthew Streeter
• On the Number of Linear Regions of Deep Neural Networks Guido F. Montufar, Razvan Pascanu, Kyunghyun Cho, Yoshua Bengio
• Identifying and attacking the saddle point problem in high-dimensional non-convex optimization Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio
• Extracting Latent Structure From Multiple Interacting Neural Populations Joao Semedo, Amin Zandvakili, Adam Kohn, Christian K. Machens, Byron M. Yu
• Learning with Fredholm Kernels Qichao Que, Mikhail Belkin, Yusu Wang
• Hamming Ball Auxiliary Sampling for Factorial Hidden Markov Models Michalis Titsias, Christopher Yau
• Optimizing Energy Production Using Policy Search and Predictive State Representations Yuri Grinberg, Doina Precup, Michel Gendreau
• Scaling-up Importance Sampling for Markov Logic Networks Deepak Venugopal, Vibhav G. Gogate
• Optimal Neural Codes for Control and Estimation Alex K. Susemihl, Ron Meir, Manfred Opper
• Graph Clustering With Missing Data: Convex Algorithms and Analysis Ramya Korlakai Vinayak, Samet Oymak, Babak Hassibi
• Scale Adaptive Blind Deblurring Haichao Zhang, Jianchao Yang
• Weighted importance sampling for off-policy learning with linear function approximation A. Rupam Mahmood, Hado P. van Hasselt, Richard S. Sutton
• Information-based learning by agents in unbounded state spaces Shariq A. Mobin, James A. Arnemann, Fritz Sommer
• Exponential Concentration of a Density Functional Estimator Shashank Singh, Barnabas Poczos
• Scalable Kernel Methods via Doubly Stochastic Gradients Bo Dai, Bo Xie, Niao He, Yingyu Liang, Anant Raj, Maria-Florina F. Balcan, Le Song
• Fast Training of Pose Detectors in the Fourier Domain João F. Henriques, Pedro Martins, Rui F. Caseiro, Jorge Batista
• An Accelerated Proximal Coordinate Gradient Method Qihang Lin, Zhaosong Lu, Lin Xiao
• Communication-Efficient Distributed Dual Coordinate Ascent Martin Jaggi, Virginia Smith, Martin Takac, Jonathan Terhorst, Sanjay Krishnan, Thomas Hofmann, Michael I. Jordan
• Simple MAP Inference via Low-Rank Relaxations Roy Frostig, Sida Wang, Percy S. Liang, Christopher D. Manning
• A* Sampling Chris J. Maddison, Daniel Tarlow, Tom Minka
• A Bayesian model for identifying hierarchically organised states in neural population activity Patrick Putzky, Florian Franzen, Giacomo Bassetto, Jakob H. Macke
• Sequence to Sequence Learning with Neural Networks Ilya Sutskever, Oriol Vinyals, Quoc V. V. Le
• Improved Distributed Principal Component Analysis Yingyu Liang, Maria-Florina F. Balcan, Vandana Kanchanapally, David Woodruff
• Sparse Polynomial Learning and Graph Sketching Murat Kocaoglu, Karthikeyan Shanmugam, Alexandros G. Dimakis, Adam Klivans
• Tight Continuous Relaxation of the Balanced k-Cut Problem Syama Sundar Rangapuram, Pramod Kaushik Mudrakarta, Matthias Hein
• Mondrian Forests: Efficient Online Random Forests Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
• Expectation-Maximization for Learning Determinantal Point Processes Jennifer A. Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar
• Capturing Semantically Meaningful Word Dependencies with an Admixture of Poisson MRFs David Inouye, Pradeep K. Ravikumar, Inderjit S. Dhillon
• Streaming, Memory Limited Algorithms for Community Detection Se-Young Yun, marc lelarge, Alexandre Proutiere
• Content-based recommendations with Poisson factorization Prem K. Gopalan, Laurent Charlin, David Blei
• A Statistical Decision-Theoretic Framework for Social Choice Hossein Azari Soufiani, David C. Parkes, Lirong Xia
• Compressive Sensing of Signals from a GMM with Sparse Precision Matrices Jianbo Yang, Xuejun Liao, Minhua Chen, Lawrence Carin
• Bayesian Sampling Using Stochastic Gradient Thermostats Nan Ding, Youhan Fang, Ryan Babbush, Changyou Chen, Robert D. Skeel, Hartmut Neven
• On Sparse Gaussian Chain Graph Models Calvin McCarter, Seyoung Kim
• Orbit Regularization Renato Negrinho, Andre Martins
• Efficient Minimax Strategies for Square Loss Games Wouter M. Koolen, Alan Malek, Peter L. Bartlett
• A Residual Bootstrap for High-Dimensional Regression with Near Low-Rank Designs Miles Lopes
• Large-Margin Convex Polytope Machine Alex Kantchelian, Michael C. Tschantz, Ling Huang, Peter L. Bartlett, Anthony D. Joseph, J. D. Tygar
• Distributed Variational Inference in Sparse Gaussian Process Regression and Latent Variable Models Yarin Gal, Mark van der Wilk, Carl Rasmussen
• Learning Distributed Representations for Structured Output Prediction Vivek Srikumar, Christopher D. Manning
• Convex Deep Learning via Normalized Kernels Özlem Aslan, Xinhua Zhang, Dale Schuurmans
• Tight convex relaxations for sparse matrix factorization Emile Richard, Guillaume R. Obozinski, Jean-Philippe Vert
• Learning to Search in Branch and Bound Algorithms He He, Hal Daume III, Jason M. Eisner
• An Integer Polynomial Programming Based Framework for Lifted MAP Inference Somdeb Sarkhel, Deepak Venugopal, Parag Singla, Vibhav G. Gogate
• Conditional Random Field Autoencoders for Unsupervised Structured Prediction Waleed Ammar, Chris Dyer, Noah A. Smith
• How transferable are features in deep neural networks? Jason Yosinski, Jeff Clune, Yoshua Bengio, Hod Lipson
• Accelerated Mini-batch Randomized Block Coordinate Descent Method Tuo Zhao, Mo Yu, Yiming Wang, Raman Arora, Han Liu
• Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning Xiaoxiao Guo, Satinder Singh, Honglak Lee, Richard L. Lewis, Xiaoshi Wang
• A Latent Source Model for Online Collaborative Filtering Guy Bresler, George H. Chen, Devavrat Shah
• Distributed Bayesian Posterior Sampling via Moment Sharing Minjie Xu, Balaji Lakshminarayanan, Yee Whye Teh, Jun Zhu, Bo Zhang
• Learning with Pseudo-Ensembles Phil Bachman, Ouais Alsharif, Doina Precup
• Learning Time-Varying Coverage Functions Nan Du, Yingyu Liang, Maria-Florina F. Balcan, Le Song
• Tighten after Relax: Minimax-Optimal Sparse PCA in Polynomial Time Zhaoran Wang, Huanran Lu, Han Liu
• Discriminative Metric Learning by Neighborhood Gerrymandering Shubhendu Trivedi, David Mcallester, Greg Shakhnarovich
• Finding a sparse vector in a subspace: Linear sparsity using alternating directions Qing Qu, Ju Sun, John Wright
• Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh
• Discrete Graph Hashing Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang
• Feedback Detection for Live Predictors Stefan Wager, Nick Chamandy, Omkar Muralidharan, Amir Najmi
• Rates of Convergence for Nearest Neighbor Classification Kamalika Chaudhuri, Sanjoy Dasgupta
• Consistency of weighted majority votes Daniel Berend, Aryeh Kontorovitch
• Beta-Negative Binomial Process and Exchangeable Random Partitions for Mixed-Membership Modeling Mingyuan Zhou
• Zero-shot recognition with unreliable attributes Dinesh Jayaraman, Kristen Grauman
• Concavity of reweighted Kikuchi approximation Po-Ling Loh, Andre Wibisono
• Online Decision-Making in General Combinatorial Spaces Arun Rajkumar, Shivani Agarwal
• Fast Multivariate Spatio-temporal Analysis via Low Rank Tensor Learning Mohammad Taha Bahadori, Qi (Rose) Yu, Yan Liu
• Clustered factor analysis of multineuronal spike data Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
• Algorithms for CVaR Optimization in MDPs Yinlam Chow, Mohammad Ghavamzadeh
• Factoring Variations in Natural Images with Deep Gaussian Mixture Models Aaron van den Oord, Benjamin Schrauwen
• Partition-wise Linear Models Hidekazu Oiwa, Ryohei Fujimaki
• LSDA: Large Scale Detection through Adaptation Judy Hoffman, Sergio Guadarrama, Eric S. Tzeng, Ronghang Hu, Jeff Donahue, Ross Girshick, Trevor Darrell, Kate Saenko
• Deep Networks with Internal Selective Attention through Feedback Connections Marijn F. Stollenga, Jonathan Masci, Faustino Gomez, Jürgen Schmidhuber
• Parallel Feature Selection Inspired by Group Testing Yingbo Zhou, Utkarsh Porwal, Ce Zhang, Hung Q. Ngo, Long Nguyen, Christopher Ré, Venu Govindaraju
• Low-Rank Time-Frequency Synthesis Cédric Févotte, Matthieu Kowalski
• Pre-training of Recurrent Neural Networks via Linear Autoencoders Luca Pasa, Alessandro Sperduti
• Semi-supervised Learning with Deep Generative Models Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling
• Signal Aggregate Constraints in Additive Factorial HMMs, with Application to Energy Disaggregation Mingjun Zhong, Nigel Goddard, Charles Sutton
• Stochastic variational inference for hidden Markov models Nicholas Foti, Jason Xu, Dillon Laird, Emily Fox
• A Wild Bootstrap for Degenerate Kernel Tests Kacper P. Chwialkowski, Dino Sejdinovic, Arthur Gretton
• Biclustering Usinig Message Passing Luke O'Connor, Soheil Feizi
• Fast Kernel Learning for Multidimensional Pattern Extrapolation Andrew Wilson, Elad Gilboa, John P. Cunningham, Arye Nehorai
• Learning on graphs using Orthonormal Representation is Statistically Consistent Rakesh Shivanna, Chiranjib Bhattacharyya
• Spectral k-Support Norm Regularization Andrew M. McDonald, Massimiliano Pontil, Dimitris Stamos
• Unsupervised learning of an efficient short-term memory network Pietro Vertechi, Wieland Brendel, Christian K. Machens
• Quantized Estimation of Gaussian Sequence Models in Euclidean Balls Yuancheng Zhu, John Lafferty
• Learning a Concept Hierarchy from Multi-labeled Documents Viet-An Nguyen, Jordan L. Boyd-Graber, Philip Resnik, Jonathan Chang
• Variational Gaussian Process State-Space Models Roger Frigola, Yutian Chen, Carl Rasmussen
• Fast Prediction for Large-Scale Kernel Machines Cho-Jui Hsieh, Si Si, Inderjit S. Dhillon

        Join the CompressiveSensing subreddit or the Google+ Community and post there !
Liked this entry ? subscribe to Nuit Blanche's feed, there's more where that came from. You can also subscribe to Nuit Blanche by Email, explore the Big Picture in Compressive Sensing or the Matrix Factorization Jungle and join the conversations on compressive sensing, advanced matrix factorization and calibration issues on Linkedin.