Publications

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Publications sorted chronologically (workshop papers / tech reports below)



Preprints and Working Papers

Press

  • Can computers help us synthesize new materials? (MIT News)
  • Fabrication of new materials: Designing "recipes" using artificial intelligence (Energy Futures)
  • Data diversity (MIT News | Comm. ACM)


Published/Accepted Papers




Abstracts and Contributions to Workshops

  • Z. Mariet, J. Robinson, J. Smith, S. Sra, S. Jegelka. Optimal Batch Variance with Second-Order Marginals. ICML workshop on Real World Experiment Design and Active Learning, 2020.
  • M. El Halabi, S. Jegelka. Minimizing approximately submodular functions. OPT 2019, Optimization for Machine Learning, 2019. Oral presentation
  • Z. Xu. C. Li, S. Jegelka. Exploring the Robustness of GANs to Internal Perturbations. ICML workshop on Security and Privacy on Machine Learning, 2019. (arXiv)
  • C.-Y. Chuang, A. Torralba, S. Jegelka. The Role of Embedding-complexity in Domain-invariant Representations. ICML workshop on Adaptive and Multitask Learning, 2019.
  • C. Bunne, D. Alvarez Melis, A. Krause and S. Jegelka. Learning Generative Models Across Incomparable Spaces. NIPS workshop on Relational Representation Learning, 2018. Oral presentation, Best Paper Award
  • M. Staib, B. Wilder and S. Jegelka. Distributionally Robust Submodular Maximization. ICML 2018 Workshop on Modern Trends in Nonconvex Optimization for Machine Learning.Spotlight.
  • M. Staib and S. Jegelka. Distributionally Robust Deep Learning as a Generalization of Adversarial Training. NIPS Machine Learning and Computer Security Workshop, 2017.
  • A. Lenail, L. Schmidt, J. Li, T. Ehrenberger, K. Sachs, S. Jegelka and E. Fraenkel. Graph-Sparse Logistic Regression. NIPS workshop on Discrete Structure in Machine Learning (DISCML), 2017.
  • Z. Wang, C. Gehring, P. Kohli, S. Jegelka. Batched Large-scale Bayesian Optimization in High-dimensional Spaces. NIPS workshop on Bayesian Optimization (BayesOpt), 2017.
  • D. Alvarez Melis, T. Jaakkola and S. Jegelka. Structured Optimal Transport. NIPS workshop on Optimal Transport and Machine Learning (OTML), 2017. Oral presentation.
  • M. Cohen, L. Schmidt, C. Hegde, S. Jegelka. Efficiently Optimizing over (Non-Convex) Cones via Approximate Projections. NIPS workshop on Optimization in Machine Learning (OPTML), 2017. Oral presentation.
  • M. Staib and S. Jegelka. Wasserstein k-means++ for Cloud Regime Histogram Clustering. Climate Informatics, 2017.
  • Z. Wang, B. Zhou and S. Jegelka. Optimization as Estimation with Gaussian Processes in Bandit Settings. NIPS workshop on Bayesian Optimization: Scalability and Flexibility, 2015.
  • E. Shelhamer, S. Jegelka and T. Darrell. Communal Cuts: sharing cuts across images. NIPS workshop on Discrete Optimization in Machine Learning, 2014.
  • V. Strnadova, A. Buluc, L. Oliker, J. Gonzalez, S. Jegelka, J. Chapman and J. R. Gilbert. Fast Clustering Methods for Genetic Mapping in Plants. 16th SIAM Conference on Parallel Processing for Scientific Computing, 2014.
  • R. Iyer, S. Jegelka and J. Bilmes. Mirror Descent-Like Algorithms for Submodular Optimization. NIPS 2012 Workshop on Discrete Optimization in Machine Learning.
  • S. Jegelka and J. Bilmes. Multi-label Cooperative Cuts. CVPR 2011 Workshop on Inference in Graphical Models with Structured Potentials.
  • S. Jegelka and J. Bilmes. Coupling Edges in Graph Cuts. SIAM Conference on Optimization, 2011.
  • S. Jegelka and J. Bilmes. Online Algorithms for Submodular Minimization with Combinatorial Constraints. NIPS 2010 Workshop Discrete Optimization in Machine Learning.
  • S. Jegelka and J. Bilmes. Cooperative Cuts: Graph Cuts with Submodular Edge Weights. EURO XXIV (24th European Conference on Operational Research), 2010.
  • S. Jegelka and J. Bilmes. Notes on Graph Cuts with Submodular Edge Weights. NIPS 2009 Workshop Discrete Optimization in Machine Learning.
  • S. Jegelka, A. Gretton and D. Achlioptas. Kernel ICA for Large Scale Problems. NIPS 2005 Workshop on Large Scale Kernel Machines.

Technical Reports


Theses