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Внешние ссылки главной страницы ( 128 ) | |
web.media.mit.edu/~sra | Misha Sra |
lids.mit.edu/ | Laboratory for Inf. & Decision Systems (LIDS) |
idss.mit.edu/ | IDSS |
ml.mit.edu | ML Group |
stat.mit.edu | Center for Statistics, |
mifods.mit.edu | MIT Institute for Foundations of Data Science |
twitter.com/optiML | - |
linkedin.com/in/suvrit-sra-3668671 | - |
mathoverflow.net/users/8430/suvrit | <img> |
opt-ml.org | OPT2015: Optimization for Machine Learning, NIPS, Montreal |
arxiv.org/find/all/1/all:+suvrit/0/1/0/all/0/1 | arXiv |
scholar.google.com/citations?user=eyCw9goAAAAJ&hl=en | Google Scholar |
learning-modules.mit.edu/class/index.html?uuid=/course/6/fa1... | 6.867 F'18 |
nips.cc/Conferences/2018/Schedule?showEvent=11440 | Exponentiated Strongly Rayleigh Measures. |
arxiv.org/abs/1805.00521 | Direct Runge-Kutta Discretization Achieves Acceleration |
arxiv.org/abs/1809.10858 | Efficiently testing local optimality and escaping saddles for ReLU networks |
arxiv.org/ | Finite sample expressive power of small-width ReLU networks |
bu.edu/cs/algorithms-and-theory-seminar/ | BU Algorithms and Theory seminar |
arxiv.org/abs/1806.02812 | An Estimate Sequence for Geodesically Convex Optimization |
tandfonline.com/toc/glma20/current | LMLA |
optml.lehigh.edu/events/dimacs-tripods-mopta-2018/ | At the DIMACS/TRIPODS/MOPTA 2018 joint conference and workshop |
ifds.wisc.edu/workshops/nonconvex-formulations/ | At the IFDS TRIPODS workshop on non-convex optimization |
arxiv.org/abs/1806.10077 | Random Shuffling Beats SGD after Finite Epochs |
arxiv.org/abs/1805.00521 | Direct Runge-Kutta Discretization Achieves Acceleration |
zelda.lids.mit.edu | Zelda |
research.googleblog.com/2018/04/announcing-2018-google-phd-f... | 2018 Google Research Fellowship! |
arxiv.org/abs/1803.10141 | New concavity and convexity results for symmetric polynomials and their ratios |
arxiv.org/abs/1803.11064 | Non-Linear Temporal Subspace Representations for Activity Recognition |
arxiv.org/abs/1802.05649 | Learning Determinantal Point Processes by Sampling Inferred Negatives |
arxiv.org/abs/1802.03487 | A critical view of global optimality in deep learning |
ismp2018.sciencesconf.org | ISMP 2018 |
learningtheory.org/colt2018/ | COLT 2018 |
aaai.org | AAAI 2018 |
nips.cc | NIPS 2016 |
padl.ws/ | ICML 2017 Workshop on Principled Approaches to Deep Learning |
arxiv.org/abs/1709.01434 | A Generic Approach for Escaping Saddle points |
eecs.mit.edu/news-events/calendar/events/geometric-tools-non... | EECS Special Seminar, MIT |
simons.berkeley.edu/workshops/optimization2017-4 | Optimization, Statistics, and Uncertainty. |
simons.berkeley.edu/workshops/optimization2017-2 | Fast Iterative Methods in Optimization |
nsf.gov/awardsearch/showAward?AWD_ID=1741341&HistoricalAward... | NSF BIGDATA |
learning-modules.mit.edu/class/index.html?uuid=/course/6/fa1... | 6.867 Machine Learning |
arxiv.org/abs/ | Markov Chains for Cardinality Restricted Strongly Rayleigh Measures via Chain Combination |
arxiv.org/abs/1710.10770 | Frank-Wolfe methods for geodesically convex optimization |
arxiv.org/abs/1509.05902v2 | Inequalities under elementary symmetric polynomial dominance |
arxiv.org/abs/1703.02674 | Column Subset Selection via Polynomial Time Dual Volume Sampling |
arxiv.org/abs/1705.09677 | Elementary Symmetric Polynomials for Optimal Experimental Design |
bicmr.pku.edu.cn/~dongbin/Data/2017DataScienceSS.pdf | Optimization for Machine Learning: Convex and Nonconvex. |
bicmr.pku.edu.cn | Beijing International Center for Mathematical Research (BICMR); Peking University |
arxiv.org/abs/1706.09549 | Distributional Adversarial Networks |
github.com/ChengtaoLi/dan | Code Repo |
arxiv.org/abs/1706.03267 | An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization |
mlss.tuebingen.mpg.de | Max Planck Institute for Intelligent Systems, Tübingen, Germany |
research.criteo.com/2017-criteo-faculty-research-award-recip... | Criteo faculty research award. |
arxiv.org/abs/1705.08583 | Sequence Summarization Using Order-constrained Kernelized Feature Subspaces |
sites.google.com/view/bayopt17/ | Bayopt 2017. |
simons.berkeley.edu/programs/machinelearning2017 | Foundations of Machine Learning |
simons.berkeley.edu | Simons Institute, UC Berkeley, CA |
wifo.eecs.berkeley.edu/Seminar/index.html | BLISS Seminar, UC Berkeley Information Systems and Sciences |
ljk.imag.fr/membres/Jerome.Malick/osl2017/program.html | Optimization and Statistical Learning (OSL' 17) |
arxiv.org/abs/1604.02027 | Combinatorial Topic Models using Small–Variance Asymptotics |
nips.cc/Conferences/2016/Schedule?showEvent=6215 | Nonconvex Optimization for Machine Learning: Theory and Practice |
nips.cc/Conferences/2016/Schedule?showEvent=6200 | Large-Scale Optimization: Beyond Stochastic Gradient Descent and Convexity |
lids.mit.edu/news-and-events/lids-seminar-series | LIDS Seminar, MIT |
learning-modules.mit.edu/class/index.html?uuid=/course/6/fa1... | 6.867 Machine Learning |
arxiv.org/abs/1608.01008 | Markov Chain Sampling in Discrete Probabilistic Models with Constraints |
arxiv.org/abs/1605.08374 | Kronecker Determinantal Point Processes |
arxiv.org/abs/1605.07147 | Fast stochastic optimization on Riemannian manifolds |
arxiv.org/abs/1605.06900 | Fast Stochastic Methods for Nonsmooth Nonconvex Optimization |
arxiv.org/abs/1607.08254 | Stochastic Frank-Wolfe Methods for Nonconvex Optimization |
arxiv.org/abs/1607.03559 | Fast sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes |
siam.org/meetings/an16/ | SIAM Annual Meeting 2016, Boston. |
meetings.siam.org/sess/dsp_programsess.cfm?SESSIONCODE=60203 | Advances in large-scale optimization. |
sites.google.com/site/noncvxicml16/ | Nonconvex Optimization Workshop |
icml.cc | ICML 2016 |
arxiv.org/abs/1507.08366 | Matrix square roots via geometric optimization |
arxiv.org/abs/1602.06053 | First-order methods for geodesically convex optimization |
proceedings.mlr.press/v48/lig16.pdf | Gaussian quadrature for matrix inverse forms with applications |
arxiv.org/abs/1603.06052 | Fast DPP sampling for Nyström with application to Kernel Methods |
people.csail.mit.edu/jsolomon/assets/gw.pdf | Entropic Metric Alignment for Correspondence Problems |
arxiv.org/abs/1603.06159 | arXiv |
arxiv.org/abs/1410.4812v2 | Inference and mixture modeling with the Elliptical Gamma distribution |
arxiv.org/abs/1603.06160 | arXiv |
arxiv.org/abs/1511.05077 | Diversity Networks |
www-rech.telecom-lille.fr/diff-cv2016/index.html | DIFF-CVML'16 |
courses.csail.mit.edu/6.036/spring_2016/info.html | 6.036 Introduction to Machine Learning |
him.uni-bonn.de | Hausdorff Institute for Mathematics |
arxiv.org/abs/1508.04039 | Sum-of-squared logarithms inequality |
kdd.org | KDD 2016 |
him.uni-bonn.de/programs/current-trimester-program/signal-pr... | Math of Signal Processing |
aistats.org | AISTATS 2016 |
arxiv.org/abs/1508.05003 | Delay sensitive distributed optimization |
arxiv.org/abs/1509.01618 | Efficient Sampling for K-Determinantal Point Processes |
arxiv.org/abs/1507.02772v2 | Riemannian dictionary learning and sparse coding |
arxiv.org/abs/1507.08366v2 | Matrix square roots via geometric optimization |
arxiv.org/abs/1512.01904 | Bounds on bilinear inverse forms via Gaussian quadrature with applications |
arxiv.org/abs/1509.05902 | Inequalities via elementary symmetric polynomial monotonicity |
arxiv.org/abs/1509.02447 | Efficient structured low rank minimization |
ismp2015.org | ISMP, 2015 |
arxiv.org/abs/1507.02772 | Riemannian dictionary learning and sparse coding |
arxiv.org/abs/1506.07677 | Manifold optimization for mixture models |
arxiv.org/abs/1506.06840 | Variance reduction in stochastic gradient and asynchronous algorithms |
research.microsoft.com/en-us/events/ss2015/ | MSR Summer School on Machine Learning, Bangalore |
arxiv.org/abs/1411.4107v2 | Explicit diagonalization of an anti-triangular Cesaró matrix |
math.mit.edu/seminars/combin/ | MIT Combinatorics Seminar!! |
arxiv.org/abs/1312.1039v3 | Conic geometric optimisation on the manifold of positive definite matrices |
arxiv.org/abs/1411.0065v2 | Hlawka-Popoviciu inequalities on positive definite tensors |
github.com/albarji/proxTV | Fast total-variation toolbox |
arxiv.org/abs/1410.4812 | Statistical inference with elliptical distributions |
arxiv.org/abs/1410.1958 | Completely strong superadditivity of generalized matrix functions |
arxiv.org/abs/1409.6086 | Asynchronous Parallel Block-Coordinate Frank-Wolfe |
arxiv.org/abs/1409.2617v3 | Randomized coordinate descent methods with linear constraints |
arxiv.org/abs/1312.1039v2 | New arXiv version of |
cs.cmu.edu/~suvrit/teach/aopt.html | Advanced Optimization |
tandfonline.com/loi/goms20#.UsY6_EOFqb8 | Optimization Methods and Software |
arxiv.org/abs/1110.1773v4 | new arXiv version |
arxiv.org/abs/1110.1773v3 | new arXiv version |
arxiv.org/abs/1312.1039 | arXiv version |
arxiv-web3.library.cornell.edu/abs/1311.4296 | arXiv version |
ml.cmu.edu/ | ML Dept., School of CS, Carnegie Mellon University |
nips.cc/Conferences/2013/Committees | Area Chair for NIPS 2013 |
eecs.berkeley.edu/~elghaoui/Teaching/EE227A/index.html | EECS, UC Berkeley |
dx.doi.org/10.1016/j.jmva.2012.08.010 | The multivariate Watson distribution: Maximum likelihood and other aspects" |
open.nims.re.kr/new/event/event.php?workType=home&Idx=77 | NIMS Hot Topics Workshop on Positive Matrices and Operators |
link.aip.org/link/?SML/30/375 | Metric Nearness |
siam.org/prizes/sponsored/outstanding_paper.php | SIAM Outstanding Paper Prize, 2011 |
mitpress.mit.edu/catalog/author/default.asp?aid=29276 | is available here |
amazon.com/Optimization-Machine-Learning-Information-Process... | Amazon |
barnesandnoble.com/w/optimization-for-machine-learning-suvri... | Barnes and Noble |
Внутренние ссылки главной страницы ( 38 ) | |
index.html | Home |
overview.html | Click here for more! |
research.html#sdiv | A new distance metric on the manifold of positive definite matrices |
teaching.html | Teaching |
students.html | Students |
software.html | Software |
./stuff/about/about.html | About me |
gopt.html | non-Euclidean and geometric optimization |
teach/nips18tut.html | NIPS'18 tutorial |
talks/madison_talk.pdf | Tractable nonconvex optimization via geometry |
./talks/pkuLectIntro.pdf | 1-3 |
./talks/pkuLectAlgo1.pdf | 4-5 |
./talks/pkuLectAlgo4.pdf | 6 |
./talks/pkuLectAlgo2.pdf | 7 |
./talks/pkuLectAlgo3.pdf | 8 |
./talks/pkuLectStoch.pdf | 9 |
./talks/pkuLectStoch2.pdf | 10 |
# | Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms |
./talks/mlss_lect1.pdf | [Lect 1] |
./talks/mlss_lect2.pdf | [Lect 2] |
./talks/mlss_lect3a.pdf | [Lect 3A] |
./talks/mlss_lect3b.pdf | [Lect 3B]. |
./talks/nips16_gopt.pdf | Taming nonconvexity via geometry |
./talks/vr_nips16_bach.pdf | [slides part 1] |
./talks/vr_nips16_sra.pdf | [slides part 2] |
./talks/lids16.pdf | Geometric Optimization |
./papers/ncsaga.pdf | Fast incremental method for smooth nonconvex optimization |
./papers/GMML.pdf | Geometric Mean Metric Learning |
./papers/nonconvex_svrg.pdf | Stochastic variance reduction for nonconvex optimization |
teach/him2016/index.html | Aspects of Convex, Nonconvex, and Geometric Optimization. |
./papers/sra_dirchap.pdf | Directional Statistics in Machine Learning: a brief review |
./papers/sra_hosseini_chapter.pdf | Geometric Optimization in Machine Learning |
suvrit.de/papers/cherian_sra_chapter.pdf | Positive Definite Matrices: Data Representation and Applications to Computer Vision |
mit/optml++ | OPTML++ research seminar plus reading group |
teach/msr2015/index.html | are now available |
./work/soft/tv.html | TV webpage |
papers/goptArxiv.pdf | Conic geometric optimisation on the manifold of positive definite matrices |
./teach/ee227a/ | EE227A: Convex Optimization |
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