Saturday Morning Video: The iSPEX project

Inouk Bourgon just mentioned to me this interesting iSPEX project that turns a smartphone camera into a spectrometer (humm a single pixel hyperspectral imager):     Thanks Inouk    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.

Around the blogs in 80 Summer hours

Quite a few interesting blog entries in these past two weeks:

Dusty THREE TALKS FROM SAMPTA 2013
Bob

• Evaluation slides
• Classification accuracy is not enough
• Paper of the Day (Po'D): SOM Based Artistic Styles Visualization

Rich

• “Opening Education” in NAE’s The Bridge
• SPARFA – Sparse Factor Analysis for Learning and Content Analytics

Hein

• Linear-Nonlinear-Poisson Neurons
• Simpson’s paradox and Judea Pearl’s Causal Calculus
• “Deep Support Vector Machines for Regression Problems”

Mohammed 10 Months at Harvard, Quantified
John ICML 2012 videos lost

David AI and Free Will: When do choices exist?
Jeremy Announcing my New Book: Exploring Arduino
Larry

• LOST CAUSES IN STATISTICS II: Noninformative Priors
• LOST CAUSES IN STATISTICS I: Finite Additivity

Andrej CVPR 2013 reading list
Dan Playing detective with rolling shutter photos
John Some things can’t be done slowly
Anand

• De-anonymized reviewers in discussion
• a cute paper: finding a most biased coin with fewest flips
• ISIT highlights request

Christian Bayes 250th versus Bayes 2.5.0.

Pip Ghostbusting Old Factoring Ideas
Dick Surely You Are Joking?
Ken Rough Problems
Steve

• CMU Snake Robot Trial at Nuclear Power Plant
• Amoeba-inspired Self-Organizing Systems
• Random Robot Roundup

Sebastien Two summer readings on Big Data and Deep Learning
Frank

• Geometry of binary and multiple hypothesis testing
• Riemannian matrix centers

Djalil

• A cube, a starfish, a thin shell, and the central limit theorem
• Quatre années à Marne-la-Vallée
• Universal convergence
• Publications scientifiques : révolution du low cost

Vladimir

• First Imaging Forum Opens Registration
• Rambus Expands Imaging Product Offerings

in that entry one can find the publication of one of Nuit Blanche's reader

"Lensless Ultra-Miniature Imagers Using Odd-Symmetry Spiral Phase Gratings"

Patrick R. Gill and David G. Stork

Drs. Gill and Stork introduce a new type of diffractive element based on odd-symmetry phase gratings. Spiral arrangements of these gratings over photo sensors constitute a new class of unprecedentedly small computational camera.

"Transmissive Structured Illumination Reveals Target Depth and Cast Shadow Details Obscured by Scattering Media"

Avinash Kumar, Patrick R. Gill, Thomas Vogelsang, and David G. Stork

During this presentation, Rambus engineers will describe novel methods of computing sharp shadows and depth estimates of objects immersed in highly scattering media using multi-scale structured transmissive illumination, such as modified Hadamard tiles.

• e2v wins Sir Arthur Clarke Award for Outstanding Achievement in Space
• Aptina Explains Clarity+ Technology, Reveals 1.1um Pixel Product
• Apple Demos New Camera Features
• Pseudo 3D Imager from Cornell University
• Point Grey Explains Sony Technology
• Nokia Lumia 1020 Features 1.1um BSI Pixel 41MP Sensors
• BBC on Computational Imaging
• Movidius Raises $16M, Gets New CEO, Moves to the Silicon Valley
• Sony Applies for Triangular and Hexagonal Pixels Patent
• Image Sensors at imec, Personal View
• Yole on Image Sensor Market
• BSI vs FSI at 2.4um Pixel Size
• ToF Imaging Review
• Toshiba 1.12um Pixel Mass Production Starts Today
• Inside Softkinetic ToF Camera

Fabian Different ways to get memory consumption or lessons learned from ``memory_profiler``
Patrick

• GraphChi – Moving on up!
• Building a Real-time, Big Data Analytics Platform with Solr
• GraphLab 2013 – Supplemental
• I’m Feeling Lucky
• Microbial Life Database
• fastcluster: Fast Hierarchical, Agglomerative Clustering Routines for R and Python

Timothy SIGGRAPH 2013 Course: Physically Based Shading in Theory and Practice
Josh Topology Optimization in GE Jet Engine Bracket Challenge Entry

While on Nuit Blanche, we had

• SPARS13 plenary talks videos
• Recovery conditions for Greedy Solvers
• Videos: Workshop on Sparsity, Compressed Sensing and Applications
• 2nd GraphLab workshop videos release
• Call for Expression of Interest in hosting "SPARS15"
• Videos of the ICML2013 meeting - part II
• Videos of the ICML2013 meeting - Part I
• Cassini took a picture of you two days ago
• Examples of 1-bit hardware sensors
• Saturday Morning Videos: Trinity College Dublin's 86 years experiment
• PhD studentship: "Low-Dimensional Prior Models for Hyperspectral Images and Applications"
• ScatNet: An implementation of Scattering Networks transforms and classification algorithms
• Cluster expansion made easy with Bayesian compressive sensing
• Optimally weighted recovery of a low-rank signal matrix from a high-dimensional signal-plus-noise matrix - implementation -
• A small Q&A with Valerio Cambareri
• Embedding Multiclass Data Hiding in Compressed Sensing - implementation -
• Group Testing: GROTESQUE: Noisy Group Testing (Quick and Efficient), Efficient Probabilistic Group Testing Based on Traitor Tracing, Group testing algorithms: bounds and simulations
• Saturday Morning Videos: Man powered Helicopter, Squito, SLAM prototype, Grasshopper taking off and landing
• SPARC 2013 LIst of abstracts and Workshop on Theoretical Aspects of Big Data at CUHK abstracts

This image was taken by Navcam: Right B (NAV_RIGHT_B) onboard NASA's Mars rover Curiosity on Sol 344 (2013-07-25 15:42:34 UTC). 

Image Credit: NASA/JPL-Caltech 

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.

SPARS13 plenary talks videos

There are several videos that can be viewable from the SPARS13 meeting  

• Ronald DeVore: An abbreviated and very personal history of nonlinear approximation
• Stéphane Mallat, Classification with Sparse Deep Scattering Networks
• Peter Bühlmann, Assigning statistical significance in high-dimensional problems
• Rob Nowak, Adaptive Sensing of Sparse Signals
• Patrick Combettes, From Convex Feasibility to Optimization in Signal Recovery and Learning
• Inderjit Dhillion, BIG & QUIC: Sparse inverse covariance estimation for a million variables
• Michael Unser, Sparse stochastic processes: A continuous-domain statistical framework for compressed sensing

Big thanks to the organizers Volkan Cevher, Matthias Seeger for making some of the videos a reality

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.

Recovery conditions for Greedy Solvers

Boris Mailhe just sent me the following:

Hi Igor,

Here is a quick summary of our 2 papers with Bob Sturm and Mark Plumbley (the ICASSP proceedings are not on IEEExplore yet):

• http://hal.inria.fr/index.php?action_todo=search&view_this_doc=hal-00839327

• http://hal.inria.fr/index.php?action_todo=search&view_this_doc=hal-00837002

• http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6506976

In these 2 articles we analyze whether for a given dictionary, sparser signals are always easier to recover by greedy algorithms. As a background, Joel Tropp's approximations of the Exact Recovery Condition (ERC) using the coherence or cumulative coherence scale monotonically with the size of the support. However, Mark Plumbley proved in an earlier article that the ERC itself does not. In this work we proved three main results:

1) (IEEE Trans. Info. Theory) Plumbley's proof used non-normalized dictionaries, but the formulation of the ERC that gives recovery guarantees differs in that case. So while Plumbley's result is mathematically correct, it cannot be extended into a result on sparse recovery.

2) (ICASSP) There can be a support I that is uniformly recoverable by OMP and a sub-support I' included in I that is not,

3) (ICASSP) but for any dictionary Phi and any s less than spark(Phi)/2 (the only range that matters since sparse representations are not all unique for s greater or equal to spark(Phi)/2), if all supports of size s are uniformly recoverable by OMP, then all supports of smaller size also are.

....

Best wishes,

Boris

Here are the papers:

On Theorem 10 in "On Polar Polytopes and the Recovery of Sparse Representations" Bob L. Sturm, Boris Mailhé , Mark D. Plumbley

Résumé : It is shown that Theorem 10 (Non-Nestedness of ERC) in [Plumbley, IEEE Trans. Info. Theory, vol. 53, pp. 3188, Sep. 2007] neglects the derivations of the exact recovery conditions (ERCs) of constrained 1-minimization (BP) and orthogonal matching pursuit (OMP). This means that it does not reflect the recovery properties of these algorithms. Furthermore, an ERC of BP more general than that in [Tropp, IEEE Trans. Info. Theory, vol. 50, pp. 2231, Oct. 2004] is shown.

BEHAVIOR OF GREEDY SPARSE REPRESENTATION ALGORITHMS ON NESTED SUPPORTS

Boris Mailhé  1, Bob L. Sturm, Mark D. Plumbley

Résumé : In this work, we study the links between the recovery proper- ties of sparse signals for Orthogonal Matching Pursuit (OMP) and the whole General MP class over nested supports. We show that the optimality of those algorithms is not locally nested: there is a dictionary and supports I and J with J included in I such that OMP will recover all signals of sup- port I, but not all signals of support J. We also show that the optimality of OMP is globally nested: if OMP can recover all s-sparse signals, then it can recover all s′-sparse signals with s′ smaller than s. We also provide a tighter version of Donoho and Elad's spark theorem, which allows us to com- plete Tropp's proof that sparse representation algorithms can only be optimal for all s-sparse signals if s is strictly lower than half the spark of the dictionary.

On Theorem 10 in “On Polar Polytopes and the Recovery of Sparse Representations”Sturm, B.L. ;  Mailhe, B. ; Plumbley, M.D.

It is shown that Theorem 10 (Non-Nestedness of ERC) in [Plumbley, IEEE Trans. Inf. Theory, vol. 53, pp. 3188–3195, Sep. 2007] neglects the derivations of the exact recovery conditions (ERCs) of constrained $ell_{1}$ -minimization (BP) and orthogonal matching pursuit. This means that it does not reflect the recovery properties of these algorithms. Furthermore, an ERC of BP more general than that in [Tropp, IEEE Trans. Inf. Theory, vol. 50, pp. 2231–2242, Oct. 2004] is shown.

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.

Videos: Workshop on Sparsity, Compressed Sensing and Applications

Boris Mailhe sent me the following:

 the videos of the Workshop on Sparsity, Compressed Sensing and Applications are now online at http://c4dm.eecs.qmul.ac.uk/events/sparse-workshop-09-05-12/

Best wishes,

Boris

Thanks Boris

Here is the list of abstracts and videos:

Abstracts

• Rémi Gribonval, INRIA (keynote speaker): "Sparse Audio Models for Inverse Audio Problems"

• Video

• Slides

Inverse problems are ubiquitous in audio processing, from the restoration of saturated single channel audio to the localization of audio sources using arrays of two or more microphones. In the last decade, a solid mathematical and algorithmic framework has emerged to address such problems, based on the notion of sparse atomic decompositions in signal dictionaries. In practice, choosing a dictionary often requires prior expert knowledge. I will discuss some recent alternatives to pre-chosen dictionaries, including designs exploiting the underlying physics, as well as data-driven approaches where the model is learnt from a training corpus.

• Stefan Bleeck, University of Southampton:  "How Sparse Coding Can Be Used to Improve Hearing Aids and Cochlear Implants"

• Video

• Slides

Language is the key to the world. However, speech intelligibility and thus auditory communication suffers when hearing is impaired. The total annual cost of hearing impairment in the EU has been estimated to be €224 billion p.a. In the UK alone 6 million more people could benefit from a hearing aid (HA or cochlear implant CI) than currently do so (worth £18 billion p.a.). These costs are mainly because of economical inactivity due to the hearing loss. The main reason why HA are not taken up is that they do not work well in noisy situations people are often in, especially in an social environment.

In our group we aim to develop novel speech enhancing strategies based on statistical information approaches inspired by brain-physiology. Current hearing aids are mainly amplification devices with little consideration of physiological principles. We have developed and evaluated various sparse coding algorithms based on ICA, PCA and NMF. In my presentation I will give an overview of the problem, and present current approaches how to overcome the problem using sparse coding algorithms. I will also show latest results from evaluation using a ‘target machine’ which allows psychophysical experiments with cochlear implant and hearing aid users in real time.

• Wei Dai, Imperial College: "Low Rank Matrix Completion: a Smoothed L0-Search"

• Video

• Slides

Low-rank matrix completion aims at inferring the missing entries from partial observations of a low-rank matrix. While many techniques for solving this problem originate from compressed sensing reconstruction, there is a fundamental difference: how to perform the l_0 search for matrix completion is not conceptually clear. Formulating the l_0 search as an optimization problem, we shall argue that the major difficulty in solving this optimization problem, surprisingly, is not local minima or saddle points but singular points. To address the singularity issue, we propose a new objective function that is continuous everywhere. The new objective function is a good approximation of the original objective function in the sense that in the limit, the former is the best possible lower semi-continuous approximation of the latter. We formulate the matrix completion problem as the minimization of the new objective function and design a quasi-Newton method to solve it. Simulations demonstrate that the new method achieves excellent numerical performance.

• Boris Mailhé, Queen Mary University of London: "Audio Declipping Using Sparse Multiscale Representations"

• Video

• Slides

Audio acquisition devices (microphones, analog to digital converters,…) typically have a maximum input level. Recorded samples that would exceed that value are instead clipped to it. Declipping is the task of restoring the original amplitude of those clipped samples. This presentation will show how a sparse modelling of the clean samples can help to retrieve the clipped ones both on synthetic clippings and real recorded signals. It will also present a new multi-scale approach that enhances the quality of the reconstruction in the most challenging cases with more clipped samples that form longer intervals.

• Yaochun Shen, University of Liverpool: "Experimental Implementation of Compressed Terahertz Imaging"

• Video

• Slides

Over the past decade, there has been great interest in the study of compressed sensing, where the number of physical measurements is much smaller than that of pixels in the reconstructed image. Such a compressed sensing approach is particularly attractive for imaging applications at terahertz frequencies where compact and sensitive detector array is currently not readily available. Here we report the experimental implementation of a terahertz pulsed spectroscopic imaging system based on the conceptof compressed sensing. A single-point terahertz detector was used to measure the terahertz waveforms transmitted through a sample and a terahertz spatial modulator. Terahertz time- and frequency-domain images of the sample were subsequently reconstructed. Two terahertz spatial modulator were used: one is a set of optimised binary masks and the other is a spinning disk with random patterns. We demonstrate that both the spatial distribution and the spectral characteristics of a sample can be obtained by this means. Compared with conventional terahertz pulsed imaging, no raster scanning of the object is required, and ten times fewer terahertz spectra need be taken.  It is therefore ideal for real-time imaging applications. 

• Andy Stove, Thales UK: "Compressive Sensing for Radar and Electronic Warfare"

• Video

Radar data should be an ideal candidate for compressive sensing since the input data rates are very high, for example up to 1Gbit/sec or even more, being driven by the required range resolution, whereas the output information rate is very low - it can typically be transmitted down a telephone line. This can also be looked at by noting that the density of targets of interests is very low whilst the volumes which are surveyed are very large - radar is uniquely capable of long-range surveillance.

Similar issues apply to the Electronic Warfare receivers which try to intercept the radar signals, where the data rates are also very high, but much of the time/frequency space is, again, only sparsely populated with signals, but many signals are concentrated in small regions of the space.

For both applications, the most modest application of compressive sensing would be to reduce the amount of digital data which must be stored or communicated. It is desirable that the data can be stored for future analysis and it is also becoming increasingly important to be able to share it between different sensors, which, without compression, would require vast communication bandwidths.

The second approach would be that if suitable data domains can be identified, it may also be possible to pre-process the data before the analogue-to-digital converters in the receivers, to reduce the demands on these critical components.

The most ambitious use of compressive sensing would be to find ways of modifying the radar waveforms to change the domain in which the information is represented, to reduce the data rate at the 'front end' of the receiver, i.e. to make the whole receiver much better matched to the information which we really want to acquire.

The presentation will introduce the principal issues in radar design and electronic warfare receiver design. It will describe the issues where compressive sensing should be able to help. It will discuss what has already been achieved in compressive sensing in these fields and will highlight the open issues which remain.

• Jared Tanner, University of Oxford: "Empirical Testing of Sparse Approximation and Matrix Completion Algorithms on Distributed Systems"

• Video

• Slides

Empirical testing is a useful technique to explore the behaviour of algorithms for which theory has not yet fully explained their behaviour.  We discuss the type of testing that can be conducted to establish conjecture.  Results are presented for l1-regularization, for which the conjecture has been proven, as well as iterative hard thresholding algorithms for both compressed sensing and matrix completion.

• Mehrdad Yaghoobi, University of Edinburgh: "Cosparse Low-Dimensional Signal Modelling"

• Video

• Slides

A new algorithm for analysis operator learning will be presented in this talk, to generalise the framework and make it more scalable. The problem of analysis operator learning can be formulated as a constrained optimisation problem. A suitable constraint is necessary to make the optimisation well-posed. Such constraints have already been introduced and the problem has been approximately solved using the projected gradient or geometric gradient descent methods. Such constraints will briefly be explored in this talk and it will be shown why other simpler constraints are insufficient.

A relaxation for the constrained analysis operator learning framework will be proposed in this talk. The relaxation has been suggested here to, a) reduce the computational complexity of the optimisation and b) include larger set of admissible operators. It will be shown that an appropriate relaxation can be useful to present a projection-free optimisation algorithm, while preventing the problem to become ill-posed. Although the optimisation program is now unconstrained, the relaxed objective is still not convex and it is thus not always possible to find the global optimum. However, when a rich set of training samples are given, it is empirically demonstrated that the desired synthetic analysis operator is recoverable, using the new relaxed formulation.

 

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.

2nd GraphLab workshop videos release

Danny Bickson just let me know that the videos of the second GraphLab meeting that took place at the beginning of the month. All the videos are at

http://graphlab.org/graphlab-workshop-2013/preliminary-agenda/

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.

Call for Expression of Interest in hosting "SPARS15"

Remi Gribonval just sent me the following:

Dear Igor, In the follow-up of SPARS'13, the SPARS Steering Committee is looking for hosts for the following SPARS meeting.  Would you mind relaying this on Nuit-Blanche ? Thanks! All the best,  Remi.

Sure here it is:

----

Dear colleagues,

In the follow-up of the workshop SPARS'13 on Signal Processing with Adaptive/Sparse Representations, the SPARS Steering Committee is looking for hosts for the following SPARS meeting.

The SPARS workshop is expected to run about each 24 months, so the next workshop would most likely be held mid 2015.

For more about SPARS13 and previous workshops, see http://spars2013.epfl.ch/,  http://ecos.maths.ed.ac.uk/SPARS11/ ,  http://spars09.inria.fr/ and http://spars05.irisa.fr

(As a past host myself, I know that this is an exciting and rewarding opportunity to make a major contribution to the community!)

If you and/or colleagues would like to host the next workshop, or think this sounds interesting and would like to know more, please send a brief email as an "Expression of Interest" to the new chair of the SPARS Steering Committee, Jalal Fadili <Jalal.Fadili@ensicaen.fr>.

Full details are not required at this stage, but as much of the following information (if known) would be helpful:

* Proposed location / venue

* Names of organizers

* Suggested dates

* Funding strategy

* Other information if available (accommodation options, travel, costs, other special features)

Please send any initial "expressions of interest" by email to Jalal Fadili <Jalal.Fadili@ensicaen.fr> by September 15.

The SPARS Steering Committee will then discuss these expressions of interest and we will get back to you.

In the meantime, if you have any queries, please let me know.

Best wishes, 

Rémi Gribonval.

---

Note: the Steering Committee will take the following elements into particular consideration when assessing propositions:

* Format: SPARS is aimed to be a "human size" workshop: not more than 200 participants, one or at most two parallel tracks, and ample time for discussions.

* Student-friendliness: support to students, e.g., through reduced fee and/or travel grants, is expected. 

* Abstract 'only': SPARS is primarily intended to be a forum for exchanges at the frontier between applied math and electrical engineering. To accommodate the different publishing cultures of these communities,  SPARS13 only requested a mandatory 1-page abstract, but full papers  were not required. The organizers of the next edition are free to consider optional proceedings for authors who would wish to provide an extended paper.

* Funding: Bidders should show that they can provide / have applied for a grant to support the organization of the event.

* Accessibility & affordability: ease of access and affordability may be preferred to fancy locations.

* Timing: time conflicts with other sparsity related workshops such as SaMPTA, SPIE Wavelets&Sparsity, etc. should be avoided if possible.

--

----------------------------------------------------------------------

Rémi Gribonval - Directeur de Recherche Inria

PANAMA Research Group, IRISA (Inria & CNRS)

Campus de Beaulieu, 35042 Rennes cedex, France

----------------------------------------------------------------------

Mél/E-mail: remi.gribonval@inria.fr

http://people.irisa.fr/Remi.Gribonval

http://www.irisa.fr/panama/

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.

Videos of the ICML2013 meeting - part II

Via Olivier Grisel's twitter feed, here are the second of the videos of the ICML2013 meeting hosted on the TechTalks.tv site: - Part 1 is here -

Track A: Nearest Neighbor and Metric Learning

• Entropic Affinities: Properties and Efficient Numerical ComputationAuthors: Max Vladymyrov; Miguel Carreira-Perpinan
• Learning Hash Functions Using Column GenerationAuthors: Xi Li; Guosheng Lin; Chunhua Shen; Anton van den Hengel; Anthony Dick
• Robust Structural Metric LearningAuthors: Daryl Lim; Gert Lanckriet; Brian McFee
• Revisiting the Nystrom method for improved large-scale machine learningAuthors: Alex Gittens; Michael Mahoney
• That was fast! Speeding up NN search of high dimensional distributionsAuthors: Emanuele Coviello; Adeel Mumtaz; Antoni Chan; Gert Lanckriet
• Stochastic k-Neighborhood Selection for Supervised and Unsupervised LearningAuthors: Daniel Tarlow; Kevin Swersky; Ilya Sutskever; Laurent Charlin; Rich Zemel
• Predictable Dual-View HashingAuthors: Mohammad Rastegari; Jonghyun Choi; Shobeir Fakhraei; Daume Hal; Larry Davis
• A unifying framework for vector-valued manifold regularization and multi-view learningAuthors: Minh Ha Quang; Loris Bazzani; Vittorio Murino

Compressed Sensing 2

• Learning Heteroscedastic Models by Convex Programming under Group SparsityAuthors: Arnak Dalalyan; Mohamed Hebiri; Katia Meziani; Joseph Salmon
• Noisy Sparse Subspace ClusteringAuthors: Yu-Xiang Wang; Huan Xu
• One-Bit Compressed Sensing: Provable Support and Vector RecoveryAuthors: Praneeth Netrapalli, Sivakant Gopi; Prateek Jain, Aditya Nori
• Smooth Sparse Coding via Marginal Regression for Learning Sparse RepresentationsAuthors: Krishnakumar Balasubramanian; Kai Yu; Guy Lebanon
• Sparse projections onto the simplexAuthors: Anastasios Kyrillidis; Stephen Becker; Volkan Cevher; Christoph Koch
• Intersecting singularities for multi-structured estimationAuthors: Emile Richard; Francis BACH; Jean-Philippe Vert
• Sparse Uncorrelated Linear Discriminant AnalysisAuthors: Xiaowei Zhang; Delin Chu
• Estimating Unknown Sparsity in Compressed SensingAuthors: Miles Lopes

Track B: General Methods

• Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation ModelAuthors: Min Xiao; Yuhong Guo
• Maximum Variance Correction with Application to A* SearchAuthors: Wenlin Chen; Kilian Weinberger; Yixin Chen
• Learning with Marginalized Corrupted FeaturesAuthors: Laurens van der Maaten; Minmin Chen; Stephen Tyree; Kilian Weinberger
• Scaling Multidimensional Gaussian Processes using Projected Additive ApproximationsAuthors: Elad Gilboa; Yunus Saatci; John Cunningham
• Nonparametric Mixture of Gaussian Processes with ConstraintsAuthors: James Ross; Jennifer Dy
• Fast Dual Variational Inference for Non-Conjugate Latent Gaussian ModelsAuthors: Mohammad Emtiyaz Khan; Aleksandr Aravkin; Michael Friedlander; Matthias Seeger
• Gaussian Process Vine Copulas for Multivariate DependenceAuthors: David Lopez-Paz; Jose Miguel Hernandez-Lobato; Ghahramani Zoubin
• Structure Discovery in Nonparametric Regression through Compositional Kernel SearchAuthors: David Duvenaud; James Lloyd; Roger Grosse; Joshua Tenenbaum; Ghahramani Zoubin
• Sequential Bayesian SearchAuthors: Zheng Wen; Branislav Kveton; Brian Eriksson; Sandilya Bhamidipati
• Kernelized Bayesian Matrix FactorizationAuthors: Mehmet G

Reinforcement learning 2

• Concurrent Reinforcement Learning from Customer Interaction SequencesAuthors: David Silver; Leonard Newnham; David Barker; Suzanne Weller; Jason McFall
• Modelling Sparse Dynamical Systems with Compressed Predictive State RepresentationsAuthors: William Hamilton; Mahdi Milani Fard,; Joelle Pineau
• Coco-Q: Learning in Stochastic Games with Side PaymentsAuthors: Elizabeth Hilliard; Eric Sodomka; Michael Littman; Amy Greenwald
• Guided Policy SearchAuthors: Sergey Levine; Vladlen Koltun
• The Cross-Entropy Method Optimizes for QuantilesAuthors: Sergiu Goschin; Ari Weinstein; Michael Littman

Track C: Transfer Learning

• Domain Generalization via Invariant Feature RepresentationAuthors: Krikamol Muandet; David Balduzzi, ; Bernhard Schoelkopf
• A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear ClassifiersAuthors: Pascal Germain; Amaury Habrard; Fran
• Sparse coding for multitask and transfer learningAuthors: Andreas Maurer; Massi Pontil; Bernardino Romera-Paredes
• Bayesian Games for Adversarial Regression ProblemsAuthors: Michael Gro
• Joint Transfer and Batch-mode Active LearningAuthors: Rita Chattopadhyay; Wei Fan; Ian Davidson; Sethuraman Panchanathan; Jieping Ye
• Multilinear Multitask LearningAuthors: Bernardino Romera-Paredes; Hane Aung; Nadia Bianchi-Berthouze; Massimiliano Pontil
• Stability and Hypothesis Transfer LearningAuthors: Ilja Kuzborskij; Francesco Orabona
• Multi-Task Learning with Gaussian Matrix Generalized Inverse Gaussian ModeAuthors: Ming Yang; Li Yingming; Zhang Zhongfei (Mark)

Topic Modeling 1

• A Practical Algorithm for Topic Modeling with Provable GuaranteesAuthors: Sanjeev Arora; Rong Ge; Yonatan Halpern; David Mimno; Ankur Moitra; David Sontag; Yichen Wu; Michael Zhu
• Online Latent Dirichlet Allocation with Infinite VocabularyAuthors: KE ZHAI; Jordan Boyd-Graber
• Gibbs Max-Margin Topic Models with Fast Sampling AlgorithmsAuthors: Jun Zhu; Ning Chen; Hugh Perkins; Bo Zhang
• Modeling Musical Influence with Topic ModelsAuthors: Uri Shalit; Daphna Weinshall; Gal Chechik
• Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document ModelingAuthors: Amr Ahmed; Liangjie Hong; Alexander Smola
• Parallel Markov Chain Monte Carlo for Nonparametric Mixture ModelsAuthors: Sinead Williamson; Avinava Dubey; Eric Xing
• MAD-Bayes: MAP-based Asymptotic Derivations from BayesAuthors: Tamara Broderick; Brian Kulis; Michael Jordan
• Topic Model Diagnostics: Assessing Domain Relevance via Topical AlignmentAuthors: Jason Chuang; Sonal Gupta; Christopher Manning; Jeffrey Heer

Track D: Statistical Learning and Inference

• Convex Relaxations for Learning Bounded-Treewidth Decomposable GraphsAuthors: Sesh Kumar K. S.; Francis Bach
• SVM for Learning with Label ProportionsAuthors: Felix Yu; Dong Liu; Sanjiv Kumar; Jebara Tony; Shih-Fu Chang
• Consistency of Online Random ForestsAuthors: Misha Denil; David Matheson; De Freitas Nando
• Relaxed expectation propagation based on l1-penalized KL minimizationAuthors: Yuan Qi; Yandong Guo
• A Fast and Exact Energy Minimization Algorithm for Cycle MRFsAuthors: Huayan Wang; Koller Daphne
• Approximate Inference in Collective Graphical ModelsAuthors: Daniel Sheldon; Tao Sun; Akshat Kumar; Tom Dietterich
• An Adaptive Learning Rate for Stochastic Variational InferenceAuthors: Rajesh Ranganath; Chong Wang; Blei David; Eric Xing
• The Bigraphical LassoAuthors: Alfredo Kalaitzis; John Lafferty; Neil Lawrence
• Anytime Representation LearningAuthors: Zhixiang Xu; Matt Kusner; Gao Huang; Kilian Weinberger
• Inference algorithms for pattern-based CRFs on sequence dataAuthors: Rustem Takhanov; Vladimir Kolmogorov

Deep Learning and Neuroscience

• On the importance of initialization and momentum in deep learningAuthors: Ilya Sutskever; James Martens; George Dahl; Geoffrey Hinton
• A non-IID Framework for Collaborative Filtering with Restricted Boltzmann MachinesAuthors: Kostadin Georgiev; Preslav Nakov
• Parsing epileptic events using a Markov switching process model for correlated time seriesAuthors: Drausin Wulsin; Emily Fox; Brian Litt
• Exploring the Mind: Integrating Questionnaires and fMRIAuthors: Esther Salazar; Ryan Bogdan; Adam Gorka; Ahmad Hariri; Lawrence Carin
• Gated Autoencoders with Tied Input WeightsAuthors: Alain Droniou; Olivier Sigaud
• Simple Sparsification Improves Sparse Denoising Autoencoders in Denoising Highly Corrupted ImagesAuthors: Kyunghyun Cho
• Natural Image Bases to Represent Neuroimaging DataAuthors: Ashish Gupta; Murat Ayhan; Anthony Maida
• Direct Modeling of Complex Invariances for Visual Object FeaturesAuthors: Ka Yu Hui
• Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging SchemesAuthors: Ohad Shamir; Tong Zhang
• Optimal rates for stochastic convex optimization under Tsybakov noise conditionAuthors: Aaditya Ramdas; Aarti Singh
• Fast Semidifferential-based Submodular Function OptimizationAuthors: Rishabh Iyer; Stefanie Jegelka; Jeff Bilmes
• Composite Self-concordant MinimizationAuthors: Quoc Tran Dinh; Anastasios Kyrillidis; Volkan Cevher
• Mini-Batch Primal and Dual Methods for SVMsAuthors: Martin Takac; Avleen Bijral; Peter Richtarik; Nati Srebro
• Stochastic Alternating Direction Method of MultipliersAuthors: Hua Ouyang; Niao He; Long Tran; Alexander Gray
• Optimization with First-Order Surrogate FunctionsAuthors: Julien Mairal
• Fast Probabilistic Optimization from Noisy GradientsAuthors: Philipp Hennig

Compressed Sensing 3

• Spectral Compressed Sensing via Structured Matrix CompletionAuthors: Yuxin Chen; Yuejie Chi
• Sparse PCA through Low-rank ApproximationsAuthors: Dimitris Papailiopoulos; Alexandros Dimakis; Stavros Korokythakis
• Efficient Sparse Group Feature Selection via Nonconvex OptimizationAuthors: Shuo Xiang; Xiaotong Shen; Jieping Ye
• A General Iterative Shrinkage and Thresholding Algorithm for Non-convex Regularized Optimization ProblemsAuthors: Pinghua Gong; Changshui Zhang; Zhaosong Lu; Jianhua Huang; Jieping Ye
• Robust Sparse Regression under Adversarial CorruptionAuthors: Yudong Chen; Constantine Caramanis; Shie Mannor
• A Local Algorithm for Finding Well-Connected ClustersAuthors: Silvio Lattanzi; Vahab Mirrokni; Zeyuan Allen Zhu
• Monochromatic Bi-ClusteringAuthors: Sharon Wulff; Ruth Urner; Shai Ben-David
• Constrained fractional set programs and their application in local clustering and community detectionAuthors: Thomas B
• Graph Clustering: Big Clusters and Small ClustersAuthors: Yudong Chen; Nir Ailon; Huan Xu
• Strict Monotonicity of Sum of Squares Error and Normalized Cut in the Lattice of ClusteringsAuthors: Nicola Rebagliati
• Learning Multiple Behaviors from Unlabeled Demonstrations in a Latent Controller SpaceAuthors: Javler Almingol ; Luis Montesano; Manuel Lopes
• Precision-recall space to correct external indices for biclusteringAuthors: Blaise Hanczar; Mohamed Nadif
• Semi-supervised Clustering by Input Pattern Assisted Pairwise Similarity Matrix CompletionAuthors: Jinfeng Yi; Rong Jin; Qi Qian; Anil Jain

Reinforcement Learning and Time Series

• ABC Reinforcement LearningAuthors: Christos Dimitrakakis; Nikolaos Tziortziotis
• Mean Reversion with a Variance ThresholdAuthors: Marco Cuturi; Alexandre d
• Gaussian Process Kernels for Pattern Discovery and ExtrapolationAuthors: Andrew Wilson; Ryan Adams
• Average Reward Optimization Objective In Partially Observable DomainsAuthors: Yuri Grinberg; Doina Precup
• Planning by Prioritized Sweeping with Small BackupsAuthors: Harm van Seijen; Rich Sutton
• Dynamic Covariance Models for Multivariate Financial Time SeriesAuthors: Yue Wu; Jose Miguel Hernandez-Lobato; Ghahramani Zoubin
• Learning Sparse Penalties for Change-point Detection using Max Margin Interval RegressionAuthors: Toby Hocking; Guillem Rigaill; Jean-Philippe VERT; Francis BACH
• Hierarchically-coupled hidden Markov models for learning kinetic rates from single-molecule dataAuthors: Jan-Willem Van de Meent; Jonathan Bronson; Frank Wood; Ruben Gonzalez, Jr.; Chris Wiggins
• Learning Connections in Financial Time SeriesAuthors: Gartheeban Ganeshapillai; John Guttag; Andrew Lo
• The Extended Parameter Filter, Yusuf Bugra ErolAuthors: Lei Li; Bharath Ramsundar; Russell Stuart
• Transition Matrix Estimation in High Dimensional Time SeriesAuthors: Fang Han; Han Liu
• Track D: Learning Theory 1
• Margins, Shrinkage and BoostingAuthors: Matus Telgarsky
• Sharp Generalization Error Bounds for Randomly-projected ClassifiersAuthors: Robert Durrant; Ata Kaban
• Risk Bounds and Learning Algorithms for the Regression Approach to Structured Output PredictionAuthors: S
• Collective Stability in Structured Prediction: Generalization from One ExampleAuthors: Ben London; Bert Huang; Ben Taskar; Lise Getoor
• Hierarchical Regularization Cascade for Joint LearningAuthors: Alon Zweig; Daphna Weinshall
• Learning Fair RepresentationsAuthors: Rich Zemel; Yu Wu; Kevin Swersky; Toniann Pitassi; Cynthia Dwork
• Differentially Private Learning with KernelsAuthors: Prateek Jain; Abhradeep Thakurta
• Rounding Methods for Discrete Linear ClassificationAuthors: Yann Chevaleyre; Frederick Koriche; Jean-Daniel Zucker

Topic Modeling 2

• Dependent Normalized Random MeasuresAuthors: Changyou Chen; Vinayak Rao; Yee Whye Teh; Wray Buntine
• Topic Discovery through Data Dependent and Random ProjectionsAuthors: Weicong Ding; Mohammad Hossein Rohban; Prakash Ishwar; Venkatesh Saligrama
• Factorial Multi-Task Learning : A Bayesian Nonparametric ApproachAuthors: Sunil Gupta; Dinh Phung; Svetha Venkatesh
• Scaling the Indian Buffet Process via Submodular MaximizationAuthors: Colorado Reed; Ghahramani Zoubin
• A Variational Approximation for Topic Modeling of Hierarchical CorporaAuthors: Do-kyum Kim; Geoffrey Voelker; Lawrence Saul
• Manifold Preserving Hierarchical Topic Models for Quantization and ApproximationAuthors: Minje Kim; Paris Smaragdis
• Subtle Topic Models and Discovering Subtly Manifested Software Concerns AutomaticallyAuthors: Mrinal Das; Suparna Bhattacharya; Chiranjib Bhattacharyya; Gopinath Kanchi
• Latent Dirichlet Allocation Topic Model with Soft Assignment of Descriptors to WordsAuthors: Daphna Weinshall; Gal Levi; Dmitri Hanukaev
• Efficient Multi-label Classification with Many LabelsAuthors: Wei Bi; James Kwok
• A Randomized Mirror Descent Algorithm for Large Scale Multiple Kernel LearningAuthors: Arash Afkanpour; Andras Gyorgy; Csaba Szepesvari; Michael Bowling

Track A: Invited Orals

• Beyond Label PropagationAuthors: Xiaojin (Jerry) Zhu, Zoubin Ghahramani, and John Lafferty
• Classic Paper Prize Talk: Online convex programming and generalized infinitesimal gradient ascent, ICML 2003Authors: Martin Zinkevich
• Learning Natural Language SemanticsAuthors: Percy Liang
• Machine Learning CompetitionsAuthors: Ben Hamner
• Large-Scale Bandit Problems and KWIK Learning,Authors: Jacob Abernethy; Kareem Amin; Michael Kearns; Moez Draief

Track A: Online Learning 1

• Online Kernel Learning with a Near Optimal Sparsity BoundAuthors: Lijun Zhang; Rong Jin; Xiaofei He
• On the Generalization Ability of Online Learning Algorithms for Pairwise Loss FunctionsAuthors: Prateek Jain; Bharath Sriperumbudur; Purushottam Kar; Harish Karnick
• Thompson Sampling for Contextual Bandits with Linear PayoffsAuthors: Shipra Agrawal; Navin Goyal
• Online Learning under Delayed FeedbackAuthors: Pooria Joulani; Andras Gyorgy; Csaba Szepesvari
• Almost Optimal Exploration in Multi-Armed BanditsAuthors: Zohar Karnin; Tomer Koren; Oren Somekh

Track A: Dimensionality Reduction and Semi-Supervised Learning

• Squared-loss Mutual Information RegularizationAuthors: Gang Niu; Wittawat Jitkrittum; Bo Dai, ; Hirotaka Hachiya; Masashi Sugiyama
• Ellipsoidal Multiple Instance LearningAuthors: Gabriel Krummenacher; Cheng Soon Ong; Joachim Buhmann
• Infinitesimal Annealing for Training Semi-Supervised Support Vector MachineAuthors: Kohei Ogawa; Motoki Imamura; Ichiro Takeuchi; Masashi Sugiyama
• Sparse Gaussian Conditional Random Fields: Algorithms, and Application to Energy ForecastingAuthors: Matt Wytock; Zico Kolter
• Adaptive Hamiltonian and Riemann Manifold Monte Carlo SamplersAuthors: Ziyu Wang; Shakir Mohamed; Nando de Freitas

Session

Track B: Feature Learning

• Forecastable Component AnalysisAuthors: Georg Goerg
• Multi-View Clustering and Feature Learning via Structured SparsityAuthors: Hua Wang; Feiping Nie; Heng Huang
• Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain AdaptationAuthors: Boqing Gong; Kristen Grauman; Fei Sha
• On Nonlinear Generalization of Sparse Coding and Dictionary LearningAuthors: Jeffrey Ho; Yuchen Xie; Baba Vemuri
• Feature Multi-Selection among Subjective FeaturesAuthors: Sivan Sabato; Adam Kalai
• Sparsity-Based Generalization Bounds for Predictive Sparse CodingAuthors: Nishant Mehta; Alexander Gray
• A Unified Robust Regression Model for Lasso-like AlgorithmsAuthors: Wenzhuo Yang; Huan Xu

Track B: Optimization and Integration

• Stochastic Simultaneous Optimistic OptimizationAuthors: Michal Valko; Alexandra Carpentier; Remi Munos
• Block-Coordinate Frank-Wolfe Optimization for Structural SVMs,Authors: Simon Lacoste-Julien; Martin Jaggi; Mark Schmidt; Patrick Pletscher
• Taming the Curse of Dimensionality: Discrete Integration by Hashing and OptimizationAuthors: Stefano Ermon; Carla Gomes; Ashish Sabharwal; Bart Selman
• Expensive Function Optimization with Stochastic Binary OutcomesAuthors: Matthew Tesch; Howie Choset; Jeff Schneider
• O(logT) Projections for Stochastic Optimization of Smooth and Strongly Convex FunctionsAuthors: Lijun Zhang; Tianbao Yang; Rong Jin; Xiaofei He
• Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization,Authors: Martin Jaggi
• Algorithms for Direct 0-1 Loss Optimization in Binary ClassificationAuthors: Tan Nguyen; Scott Sanner
• Local Deep Kernel Learning for Efficient Non-Linear SVM PreDictionAuthors: Cijo Jose; Prasoon Goyal; Parv Aggrwal; Manik Varma

Tutorials

• Tensor Decomposition Algorithms for Latent Variable Model EstimationAuthors:Anima Anandkumar, Daniel Hsu, Sham M Kakade
• Copulas in Machine LearningAuthors: Gal Elidan
• Deep LearningAuthors: Yann LeCun, Marc Aurelio Anzato
• Topological Data Analysis Part IAuthors: Primoz Skraba, Sayan Mukherjee
• Multi-Target PredictionAuthors: Willem Wargman, Krysztof Dembczybski, Eyke Hullermeier
• Submodularity in Machine Learning: New Directions Part IAuthors: Andreas Krause, Stefanie Jegelka
• Discovering Multiple Clustering Solutions: Grouping Objects in Different ViewsAuthors: Emmanuel Muller, Stephan Gunnemann, Ines Farber, Thomas Seidl
• Copulas in Machine LearningAuthors: Gal Elidan
• Energy Based Unsupervised LearningAuthors:
• Topological Data Analysis Part IIAuthors: Primoz Skraba and Sayan Mukherjee
• Music Information Research Based on Machine LearningAuthors: Masataka Goto and Kazuyoshi Yoshii
• Submodularity in Machine Learning: New Directions Part IIAuthors: Andreas Krause, Stefanie Jegelka

Track C: General SVM and Decision Tree Methods

• Multi-Class Classification with Maximum Margin Multiple KernelAuthors: Corinna Cortes; Mehryar Mohri; Afshin Rostamizadeh
• Top-down particle filtering for Bayesian decision treesAuthors: Balaji Lakshminarayanan; Daniel Roy; Yee Whye Teh
• Cost-Sensitive Tree of ClassifiersAuthors: Zhixiang Xu; Matt Kusner; Kilian Weinberger; Minmin Chen
• On the Statistical Consistency of Algorithms for Binary Classification under Class ImbalanceAuthors: Aditya Menon; Harikrishna Narasimhan; Shivani Agarwal; Sanjay Chawla
• Quickly Boosting Decision Trees - Pruning Underachieving Features EarlyAuthors: Ron Appel; Thomas Fuchs; Piotr Dollar; Pietro Perona
• Tree-Independent Dual-Tree AlgorithmsAuthors: Ryan Curtin; William March; Parikshit Ram; David Anderson; Alexander Gray; Charles Isbell
• Loss-Proportional Subsampling for Subsequent ERMAuthors: Paul Mineiro; Nikos Karampatziakis
• Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base LearnerAuthors: Peng Sun; Jie Zhou
• Safe Screening of Non-Support Vectors in Pathwise SVM ComputationAuthors: Kohei Ogawa; Yoshiki Suzuki; Ichiro Takeuchi
• Convex formulations of radius-margin based Support Vector MachinesAuthors: Huyen Do; Alexandros Kalousis
• The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear ClassificationAuthors: Ofir Pele; Ben Taskar; Amir Globerson; Michael Werman

Track C: Vision

• An Optimal Policy for Target Localization with Application to Electron MicroscopyAuthors: Raphael Sznitman; Aurelien Lucchi; Peter Frazier; Bruno Jedynak; Pascal Fua
• Fast Image TaggingAuthors: Minmin Chen; Alice Zheng; Kilian Weinberger
• An Efficient Posterior Regularized Latent Variable Model for Interactive Sound Source SeparationAuthors: Nicholas Bryan; Gautham Mysore
• Max-Margin Multiple-Instance Dictionary LearningAuthors: Xinggang Wang; Baoyuan Wang; Xiang Bai; Wenyu Liu; Zhuowen Tu
• Parameter Learning and Convergent Inference for Dense Random FieldsAuthors: Philipp Kraehenbuehl; Vladlen Koltun
• Robust and Discriminative Self-Taught LearningAuthors: Hua Wang; Feiping Nie; Heng Huang
• Learning Spatio-Temporal Structure from RGB-D Videos for Human Activity Detection and AnticipationAuthors: Hema Koppula; Ashutosh Saxena
• A Spectral Learning Approach to Range-Only SLAMAuthors: Byron Boots; Geoff Gordon
• Non-Linear Stationary Subspace Analysis with Application to Video ClassificationAuthors: Mahsa Baktashmotlagh; Mehrtash Harandi; Abbas Bigdeli; Brian Lovell; Mathieu Salzmann
• On Compact Codes for Spatially Pooled FeaturesAuthors: Yangqing Jia; Oriol Vinyals; Trevor Darrell
• Analogy-preserving Semantic Embedding for Visual Object CategorizationAuthors: Sung Ju Hwang; Kristen Grauman; Fei Sha

Track D: Spectral Learning and Tensors

• Spectral Learning of Hidden Markov Models from Dynamic and Static DataAuthors: Tzu-Kuo Huang; Jeff Schneider
• Spectral Experts for Estimating Mixtures of Linear RegressionsAuthors: Arun Tejasvi Chaganty; Percy Liang
• Learning Linear Bayesian Networks with Latent Variables,Authors: Animashree Anandkumar; Daniel Hsu; Adel Javanmard; Sham Kakade
• On learning parametric-output HMMsAuthors: Aryeh Kontorovich; Boaz Nadler; Roi Weiss
• Tensor Analyzers, Yichuan Tang; Ruslan SalakhutdinovAuthors: Geoffrey Hinton
• Unfolding Latent Tree Structures using 4th Order TensorsAuthors: Mariya Ishteva; Haesun Park; Le Song
• Infinite Positive Semidefinite Tensor Factorization with Application to Music Signal AnalysisAuthors: Kazuyoshi Yoshii; Ryota Tomioka; Daichi Mochihashi; Masataka Goto

Track D: Learning Theory II

• Exploiting Ontology Structures and Unlabeled Data for Learning,Authors: Maria-Florina Balcan; Avrim Blum; Yishay Mansour
• One-Pass AUC OptimizationAuthors: Wei Gao; Rong Jin; Shenghuo Zhu; Zhi-Hua Zhou
• Near-Optimal Bounds for Cross-Validation via Loss StabilityAuthors: Ravi Kumar; Daniel Lokshtanov; Sergei Vassilvitskii; Andrea Vattani
• Algebraic Classifiers: generic parallel training, online training, and fast cross-validationAuthors: Michael Izbicki
• Top-k Selection based on Adaptive Sampling of Noisy Preferences,Authors: Robert Busa-Fekete; Balazs Szorenyi; Paul Weng; Weiwei Cheng; Eyke Hullermeier
• Enhanced statistical rankings via targeted data collectionAuthors: Braxton Osting; Christoph Brune; Stanley Osher
• Efficient Ranking from Pairwise ComparisonsAuthors: Fabian Wauthier; Michael Jordan; Nebojsa Jojic
• Stable Coactive Learning via PerturbationAuthors: Karthik Raman; Thorsten Joachims,; Pannaga Shivaswamy; Tobias Schnabel
  
  
  
  
  Image Credit: NASA/JPL/Space Science Institute
  Full-Res: W00083229.jpg
  
  W00083229.jpg was taken on July 20, 2013 and received on Earth July 20, 2013. The camera was pointing toward SATURN-ERING at approximately 623,956 miles (1,004,160 kilometers) away, and the image was taken using the CL1 and GRN filters. This image has not been validated or calibrated.

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.