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http-equivX-UA-Compatible contentIEedge>title>Sirisha Rambhatla/title>meta namedescription contentResearch website of Sirisha Rambhatla>!-- Open Graph -->!-- Bootstrap & MDB -->link hrefhttps://stackpath.bootstrapcdn.com/bootstrap/4.5.2/css/bootstrap.min.css relstylesheet integritysha512-MoRNloxbStBcD8z3M/2BmnT+rg4IsMxPkXaGh2zD6LGNNFE80W3onsAhRcMAMrSoyWL9xD7Ert0men7vR8LUZg crossoriginanonymous>link relstylesheet hrefhttps://cdnjs.cloudflare.com/ajax/libs/mdbootstrap/4.19.1/css/mdb.min.css integritysha512-RO38pBRxYH3SoOprtPTD86JFOclM51/XTIdEPh5j8sj4tp8jmQIx26twG52UaLi//hQldfrh7e51WzP9wuP32Q crossoriginanonymous />!-- Fonts & Icons -->link relstylesheet hrefhttps://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.14.0/css/all.min.css integritysha512-1PKOgIY59xJ8Co8+NE6FZ+LOAZKjy+KY8iq0G4B3CyeY6wYHN3yt9PW0XpSriVlkMXe40PTKnXrLnZ9+fkDaog crossoriginanonymous>link relstylesheet hrefhttps://cdnjs.cloudflare.com/ajax/libs/academicons/1.9.0/css/academicons.min.css 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src/assets/js/theme.js>/script> !-- Load DarkMode JS -->script src/assets/js/dark_mode.js>/script> !-- MathJax -->script typetext/javascript> window.MathJax { tex: { tags: ams } };/script>script defer typetext/javascript idMathJax-script srchttps://cdn.jsdelivr.net/npm/mathjax@3.1.2/es5/tex-mml-chtml.js>/script>script defer srchttps://cdnjs.cloudflare.com/polyfill/v3/polyfill.min.js?featureses6>/script> /head> body classfixed-top-nav > !-- Header --> header> !-- Nav Bar --> nav idnavbar classnavbar navbar-light navbar-expand-sm fixed-top> div classcontainer> !-- Social Icons --> div classnavbar-brand social> a href/cdn-cgi/l/email-protection#604557534556594557524556594557534556584556514e455752455651455624455652455658455651455754455623455651204557554557574556514557544556554557524556234556264556264e455653455651>i classfas fa-envelope>/i>/a>a hrefhttps://scholar.google.com/citations?userEOSZeBMAAAAJ&hl target_blank titleGoogle Scholar>i classai ai-google-scholar>/i>/a>a hrefhttps://github.com/srambhatla target_blank titleGitHub>i classfab fa-github>/i>/a>a hrefhttps://www.linkedin.com/in/sirisharambhatla target_blank titleLinkedIn>i classfab fa-linkedin>/i>/a>a hrefhttps://twitter.com/siri_r target_blank titleTwitter>i classfab fa-twitter>/i>/a>a hrefhttps://mastodon.social/@sirisha target_blank titleMastodon>i classfab fa-mastodon>/i>/a>a hrefhttps://www.youtube.com/channel/UCU04jFU8jpXRs26YPFxKV1Q target_blank titleYoutube>i classfab fa-youtube>/i>/a> /div> !-- Navbar Toggle --> button classnavbar-toggler collapsed ml-auto typebutton data-togglecollapse data-target#navbarNav aria-controlsnavbarNav aria-expandedfalse aria-labelToggle navigation> span classsr-only>Toggle navigation/span> span classicon-bar top-bar>/span> span classicon-bar middle-bar>/span> span classicon-bar bottom-bar>/span> /button> div classcollapse navbar-collapse text-right idnavbarNav> ul classnavbar-nav ml-auto flex-nowrap> !-- About --> li classnav-item active> a classnav-link href/> About /a> /li> !-- Other pages --> li classnav-item > a classnav-link href/criticalml/> Critical ML Lab /a> /li> li classnav-item > a classnav-link href/publications/> Publications /a> /li> li classnav-item > a classnav-link href/media/> Media /a> /li> li classnav-item > a classnav-link href/teaching/> Teaching /a> /li> !-- Blog --> li classnav-item > a classnav-link href/blog/> Blog /a> /li> div classtoggle-container> a idlight-toggle> i classfas fa-moon>/i> i classfas fa-sun>/i> /a> /div> /ul> /div> /div> /nav>/header> !-- Content --> div classcontainer mt-5> div classpost> header classpost-header> a href/criticalml> img classimg-fluid z-depth-1 rounded src/assets/img/logo.png stylefloat: left; margin-right: 20px; margin-bottom: 20px; height: 100px> /a> h1 classpost-title> span classfont-weight-bold>Sirisha/span> Rambhatla /h1> p classdesc>Theory-guided Machine Learning for the Real World/p> /header> article> div classprofile float-right> img classimg-fluid z-depth-1 rounded src/assets/img/Sirisha-1.jpg> div classaddress> p>CPH 4358, 200 University Ave. W., Waterloo, ON, Canada/p> /div> /div> div classclearfix> p>br>br>I am an Assistant Professor at the a hrefhttps://uwaterloo.ca>University of Waterloo/a> with appointments in the/p>ul> li>a hrefhttps://uwaterloo.ca/management-sciences/>Management Science and Engineering Department/a>, Faculty of Engineering,/li> li>a hrefhttps://uwaterloo.ca/systems-design-engineering/>Systems Design Engineering/a>, Faculty of Engineering, and/li> li>a hrefhttps://cs.uwaterloo.ca>David R. Cheriton School of Computer Science/a>, Faculty of Mathematics./li>/ul>p>I lead the a href/criticalml/>Critical ML lab/a> at the University of Waterloo. I am also affiliated with:/p>ul> li>The a hrefhttps://uwaterloo.ca/artificial-intelligence-institute/>Waterloo Artificial Intelligence Institute (Waterloo.AI)/a>/li> li>The a hrefhttps://uwaterloo.ca/sustainable-aeronautics/>Waterloo Institute for Sustainable Aeronautics (WISA)/a>/li> li>The a hrefhttps://uwaterloo.ca/cybersecurity-privacy-institute/>Waterloo Cybersecurity and Privacy Institute (CPI)/a>/li>/ul>p>More details about my background at here: a href/docs/CV.pdf target\_blank>Curriculum Vitae/a>/p>h4 idareas-of-interests>Areas of Interests/h4>p>| Statistical Machine Learning | Sparse Signal Processing | Spatiotemporal Data Analysis | AI for Surgery and Healthcare | Interpretability of Deep Learning Models | Intelligent Automation and Manufacturing | Computer Vision |/p> /div> div classnews> h2>news/h2> div classtable-responsive> table classtable table-sm table-borderless> tr> th scoperow stylewidth: 100px;>Oct 27, 2023/th> td> Our work on em>Are all classes created equal? Domain Generalization for Domain-Linked Classes/em> is accepted to the DistShift Workshop at NeurIPS 2023. img classemoji title:star: alt:star: srchttps://github.githubassets.com/images/icons/emoji/unicode/2b50.png height20 width20> /td> /tr> tr> th scoperow stylewidth: 100px;>Aug 10, 2023/th> td> Our work on domain-guided spatio-temporal transformers for egocentric 3D pose estimation is accepted at KDD 2023. img classemoji title:star: alt:star: srchttps://github.githubassets.com/images/icons/emoji/unicode/2b50.png height20 width20> /td> /tr> tr> th scoperow stylewidth: 100px;>Feb 2, 2023/th> td> Do we really need stylization for Domain Adaptation, our workshop paper at the DistShift Workshop at ICLR 2023 investigates this question and develop a simple implicit stylization for domain adaptation. img classemoji title:star: alt:star: srchttps://github.githubassets.com/images/icons/emoji/unicode/2b50.png height20 width20> /td> /tr> tr> th scoperow stylewidth: 100px;>Jun 6, 2022/th> td> Our work on spatio-temporal transformers for egocentric 3D pose estimation is accepted for oral presentation in the EPIC and Ego4D workshop at CVPR 2022. img classemoji title:star: alt:star: srchttps://github.githubassets.com/images/icons/emoji/unicode/2b50.png height20 width20> /td> /tr> tr> th scoperow stylewidth: 100px;>Mar 30, 2022/th> td> My work in collaboration with UHN, Toronto on forecasting trajectories of the waitlisted NASH patients using deep learning has been accepted for oral presentation at the International Liver Transplant Society’s annual meeting! img classemoji title:star: alt:star: srchttps://github.githubassets.com/images/icons/emoji/unicode/2b50.png height20 width20> /td> /tr> /table> /div> /div> div classpublications> h2>selected publications/h2> ol classbibliography>li>div classrow stylemargin-bottom: 40px;margin-top: 40px; > div classcol-sm-3 col-push-7 abbr styletext-align:center;> div classfigure stylebackground-color:white-color; text-align:center;> img src/assets/img/NOODL.png stylewidth: 100%; display: block; margin: 0 auto; background-color:white;> /div> abbr classbadge stylewidth: 100%>ICLR/abbr> div classabbr> 🏆 abbr classbadge stylewidth: 85%> Travel Award/abbr> /div> /div> div idRambhatla2019 classcol-sm-7 col-pull-7> div classtitle>NOODL: Provable Online Dictionary Learning and Sparse Coding/div> div classauthor> em>Rambhatla, S./em>, Li, X., and Haupt, J. /div> div classperiodical> em>International Conference on Learning Representations (ICLR)/em> 2019 /div> div classlinks> a classabstract btn btn-sm z-depth-0 rolebutton>Abs/a> a classbibtex btn btn-sm z-depth-0 rolebutton>Bib/a> a hrefhttp://arxiv.org/abs/1902.11261 classbtn btn-sm z-depth-0 rolebutton target_blank>arXiv/a> a hrefhttps://openreview.net/pdf?idHJeu43ActQ classbtn btn-sm z-depth-0 rolebutton target_blank>PDF/a> a hrefhttps://github.com/srambhatla/NOODL classbtn btn-sm z-depth-0 rolebutton target_blank>Code/a> a href/assets/pdf//posters/NOODL_poster_ICLR.pdf classbtn btn-sm z-depth-0 rolebutton target_blank>Slides/a> /div> !-- Hidden abstract block --> div classabstract hidden> p>We consider the dictionary learning problem, where the aim is to model the givendata as a linear combination of a few columns of a matrix known as adictionary,where the sparse weights forming the linear combination are known ascoeffi-cients. Since the dictionary and coefficients, parameterizing the linear model areunknown, the corresponding optimization is inherently non-convex. This was amajor challenge until recently, when provable algorithms for dictionary learningwere proposed. Yet, these provide guarantees only on the recovery of the dic-tionary, without explicit recovery guarantees on the coefficients. Moreover, anyestimation error in the dictionary adversely impacts the ability to successfully lo-calize and estimate the coefficients. This potentially limits the utility of existingprovable dictionary learning methods in applications where coefficient recoveryis of interest. To this end, we develop NOODL: a simple Neurally plausible alter-nating Optimization-based Online Dictionary Learning algorithm, which recoversboththe dictionary and coefficientsexactlyat a geometric rate, when initializedappropriately. Our algorithm, NOODL, is also scalable and amenable for largescale distributed implementations in neural architectures, by which we mean thatit only involves simple linear and non-linear operations. Finally, we corroboratethese theoretical results via experimental evaluation of the proposed algorithmwith the current state-of-the-art techniques./p> /div> div classbibtex hidden> p>@article{Rambhatla2019, abbr {ICLR}, title {{NOODL}: Provable Online Dictionary Learning and Sparse Coding}, author {Rambhatla, S. and Li, X. and Haupt, J.}, journal {International Conference on Learning Representations (ICLR)}, year {2019}, url {https://openreview.net/forum?idHJeu43ActQ}, selected {true}, arxiv {1902.11261}, slides {/posters/NOODL_poster_ICLR.pdf}, pdf {https://openreview.net/pdf?idHJeu43ActQ}, code {https://github.com/srambhatla/NOODL}, img {NOODL.png}, award {Travel Award}}/p> /div> /div>/div>/li>li>div classrow stylemargin-bottom: 40px;margin-top: 40px; > div classcol-sm-3 col-push-7 abbr styletext-align:center;> div classfigure stylebackground-color:white-color; text-align:center;> img src/assets/img/TensorNOODL.png stylewidth: 100%; display: block; margin: 0 auto; background-color:white;> /div> abbr classbadge stylewidth: 100%>NeurIPS/abbr> div classabbr> /div> /div> div idRambhatla19Tensor classcol-sm-7 col-pull-7> div classtitle>Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning /div> div classauthor> em>Rambhatla, S./em>, Li, X., and Haupt, J. /div> div classperiodical> em>Advances in Neural Information Processing Systems (NeurIPS)/em> 2020 /div> div classlinks> a classabstract btn btn-sm z-depth-0 rolebutton>Abs/a> a classbibtex btn btn-sm z-depth-0 rolebutton>Bib/a> a hrefhttp://arxiv.org/abs/2006.16442 classbtn btn-sm z-depth-0 rolebutton target_blank>arXiv/a> a hrefhttps://papers.nips.cc/paper/2020/file/85b42dd8aae56e01379be5736db5b496-Paper.pdf classbtn btn-sm z-depth-0 rolebutton target_blank>PDF/a> a hrefhttps://github.com/srambhatla/TensorNOODL classbtn btn-sm z-depth-0 rolebutton target_blank>Code/a> a href/assets/pdf//posters/Poster_TensorNOODL.pdf classbtn btn-sm z-depth-0 rolebutton target_blank>Slides/a> /div> !-- Hidden abstract block --> div classabstract hidden> p>We consider the problem of factorizing a structured 3-way tensor into itsconstituent Canonical Polyadic (CP) factors. This decomposition, whichcan be viewed as a generalization of singular value decomposition (SVD)for tensors, reveals how the tensor dimensions (features) interact with eachother. However, since the factors area prioriunknown, the correspondingoptimization problems are inherently non-convex. The existing guaranteedalgorithms which handle this non-convexity incur an irreducible error (bias),and only apply to cases where all factors have the same structure. To thisend, we develop a provable algorithm for online structured tensor factor-ization, wherein one of the factors obeys some incoherence conditions, andthe others are sparse. Specifically we show that, under some relatively mildconditions on initialization, rank, and sparsity, our algorithm recovers thefactorsexactly(up to scaling and permutation) at a linear rate. Comple-mentary to our theoretical results, our synthetic and real-world data eval-uations showcase superior performance compared to related techniques./p> /div> div classbibtex hidden> p>@article{Rambhatla19Tensor, abbr {NeurIPS}, title {Provable Online {CP/PARAFAC} Decomposition of a Structured Tensor via Dictionary Learning }, author {Rambhatla, S. and Li, X. and Haupt, J.}, journal {Advances in Neural Information Processing Systems (NeurIPS)}, slides {/posters/Poster_TensorNOODL.pdf}, img {TensorNOODL.png}, url {https://papers.nips.cc/paper/2020/hash/85b42dd8aae56e01379be5736db5b496-Abstract.html}, pdf {https://papers.nips.cc/paper/2020/file/85b42dd8aae56e01379be5736db5b496-Paper.pdf}, arxiv {2006.16442}, code {https://github.com/srambhatla/TensorNOODL}, selected {true}, year {2020}}/p> /div> /div>/div>/li>li>div classrow stylemargin-bottom: 40px;margin-top: 40px; > div classcol-sm-3 col-push-7 abbr styletext-align:center;> div classfigure stylebackground-color:white-color; text-align:center;> img src/assets/img/archipelago.png stylewidth: 100%; display: block; margin: 0 auto; background-color:white;> /div> abbr classbadge stylewidth: 100%>NeurIPS/abbr> div classabbr> /div> /div> div idtsang2020does classcol-sm-7 col-pull-7> div classtitle>How does this interaction affect me? Interpretable attribution for feature interactions/div> div classauthor> Tsang, M., em>Rambhatla, S./em>, and Liu, Y. /div> div classperiodical> em>Advances in Neural Information Processing Systems (NeurIPS)/em> 2020 /div> div classlinks> a classabstract btn btn-sm z-depth-0 rolebutton>Abs/a> a classbibtex btn btn-sm z-depth-0 rolebutton>Bib/a> a hrefhttp://arxiv.org/abs/2006.10965 classbtn btn-sm z-depth-0 rolebutton target_blank>arXiv/a> a hrefhttps://proceedings.neurips.cc/paper/2020/file/443dec3062d0286986e21dc0631734c9-Paper.pdf classbtn btn-sm z-depth-0 rolebutton target_blank>PDF/a> a hrefhttps://github.com/mtsang/archipelago classbtn btn-sm z-depth-0 rolebutton target_blank>Code/a> /div> !-- Hidden abstract block --> div classabstract hidden> p>Machine learning transparency calls for interpretable explanations of how inputs relate to predictions. Feature attribution is a way to analyze the impact of features on predictions. Feature interactions are the contextual dependence between features that jointly impact predictions. There are a number of methods that extract feature interactions in prediction models; however, the methods that assign attributions to interactions are either uninterpretable, model-specific, or non-axiomatic. We propose an interaction attribution and detection framework called Archipelago which addresses these problems and is also scalable in real-world settings. Our experiments on standard annotation labels indicate our approach provides significantly more interpretable explanations than comparable methods, which is important for analyzing the impact of interactions on predictions. We also provide accompanying visualizations of our approach that give new insights into deep neural networks./p> /div> div classbibtex hidden> p>@article{tsang2020does, abbr {NeurIPS}, title {How does this interaction affect me? Interpretable attribution for feature interactions}, author {Tsang, M. and Rambhatla, S. and Liu, Y.}, journal {Advances in Neural Information Processing Systems (NeurIPS)}, arxiv {2006.10965}, pdf {https://proceedings.neurips.cc/paper/2020/file/443dec3062d0286986e21dc0631734c9-Paper.pdf}, url {https://proceedings.neurips.cc/paper/2020/hash/443dec3062d0286986e21dc0631734c9-Abstract.html}, code {https://github.com/mtsang/archipelago}, img {archipelago.png}, selected {true}, year {2020}}/p> /div> /div>/div>/li>li>div classrow stylemargin-bottom: 40px;margin-top: 40px; > div classcol-sm-3 col-push-7 abbr styletext-align:center;> div classfigure stylebackground-color:white-color; text-align:center;> img src/assets/img/LplusDA_hyper_spectral.gif stylewidth: 100%; display: block; margin: 0 auto; background-color:white;> /div> abbr classbadge stylewidth: 100%>TSP/abbr> div classabbr> /div> /div> div idRambhatla20LrTheoApp classcol-sm-7 col-pull-7> div classtitle>A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging/div> div classauthor> em>Rambhatla, S./em>, Li, X., Ren, J., and Haupt, J. /div> div classperiodical> em>IEEE Tran. on Signal Processing/em> 2020 /div> div classlinks> a classabstract btn btn-sm z-depth-0 rolebutton>Abs/a> a classbibtex btn btn-sm z-depth-0 rolebutton>Bib/a> a hrefhttp://arxiv.org/abs/1902.08304v3 classbtn btn-sm z-depth-0 rolebutton target_blank>arXiv/a> a hrefhttps://arxiv.org/pdf/1902.08304v3.pdf classbtn btn-sm z-depth-0 rolebutton target_blank>PDF/a> a href/blog/2017/HyperSpectral/ classbtn btn-sm z-depth-0 rolebutton target_blank>Blog/a> a hrefhttps://github.com/srambhatla/Dictionary-based-Robust-PCA classbtn btn-sm z-depth-0 rolebutton target_blank>Code/a> /div> !-- Hidden abstract block --> div classabstract hidden> p> We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing method. We consider two sparsity structures for the sparse factor of the dictionary sparse component, namely entry-wise and column-wise sparsity, and provide a unified analysis, encompassing both undercomplete and the overcomplete dictionary cases, to show that the constituent matrices can be successfully recovered under some relatively mild conditions on incoherence, sparsity, and rank. We leverage these results to localize targets of interest in a hyperspectral (HS) image based on their spectral signature(s) using the a priori known characteristic spectral responses of the target. We corroborate our theoretical results and analyze target localization performance of our approach via experimental evaluations and comparisons to related techniques./p> /div> div classbibtex hidden> p>@article{Rambhatla20LrTheoApp, abbr {TSP}, author {{Rambhatla}, S. and {Li}, X. and {Ren}, J. and {Haupt}, J.}, journal {IEEE Tran. on Signal Processing}, title {A Dictionary-Based Generalization of Robust PCA With Applications to Target Localization in Hyperspectral Imaging}, year {2020}, volume {68}, code {https://github.com/srambhatla/Dictionary-based-Robust-PCA}, number {}, pdf {https://arxiv.org/pdf/1902.08304v3.pdf}, arxiv {1902.08304v3}, img {LplusDA_hyper_spectral.gif}, url {https://arxiv.org/abs/1902.08304}, pages {1760-75}, selected {true}, blog {2017/HyperSpectral/}}/p> /div> /div>/div>/li>/ol>/div> !-- div classsocial> div classcontact-icons> a hrefmailto:%73%69%72%69%73%68%61.%72%61%6D%62%68%61%74%6C%61@%75%77%61%74%65%72%6C%6F%6F.%63%61>i classfas fa-envelope>/i>/a>a hrefhttps://scholar.google.com/citations?userEOSZeBMAAAAJ&hl target_blank titleGoogle Scholar>i classai ai-google-scholar>/i>/a>a hrefhttps://github.com/srambhatla target_blank titleGitHub>i classfab fa-github>/i>/a>a hrefhttps://www.linkedin.com/in/sirisharambhatla target_blank titleLinkedIn>i classfab fa-linkedin>/i>/a>a hrefhttps://twitter.com/siri_r target_blank titleTwitter>i classfab fa-twitter>/i>/a>a hrefhttps://mastodon.social/@sirisha target_blank titleMastodon>i classfab fa-mastodon>/i>/a>a hrefhttps://www.youtube.com/channel/UCU04jFU8jpXRs26YPFxKV1Q target_blank titleYoutube>i classfab fa-youtube>/i>/a> /div> div classcontact-note>/div> /div> --> /article>/div> /div> !-- Footer --> footer classfixed-bottom> div classcontainer mt-0> © Copyright 2024 Sirisha Rambhatla. 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