@inproceedings{PokuttaSinghTorrico2020, author = {Pokutta, Sebastian and Singh, M. and Torrico, A.}, title = {On the Unreasonable Effectiveness of the Greedy Algorithm: Greedy Adapts to Sharpness}, booktitle = {Proceedings of ICML}, arxiv = {http://arxiv.org/abs/2002.04063}, year = {2020}, language = {en} } @inproceedings{DiakonikolasCardereraPokutta2020, author = {Diakonikolas, Jelena and Carderera, Alejandro and Pokutta, Sebastian}, title = {Locally Accelerated Conditional Gradients}, booktitle = {Proceedings of AISTATS}, arxiv = {http://arxiv.org/abs/1906.07867}, year = {2020}, language = {en} } @article{FaenzaMunozPokutta2020, author = {Faenza, Yuri and Mu{\~n}oz, Gonzalo and Pokutta, Sebastian}, title = {New Limits of Treewidth-based tractability in Optimization}, volume = {191}, journal = {Mathematical Programming}, arxiv = {http://arxiv.org/abs/1807.02551}, doi = {10.1007/s10107-020-01563-5}, pages = {559 -- 594}, year = {2020}, language = {en} } @article{AnariHaghtalabNaoretal.2020, author = {Anari, N. and Haghtalab, N. and Naor, S. and Pokutta, Sebastian and Singh, M. and Torrico, A.}, title = {Structured Robust Submodular Maximization: Offline and Online Algorithms}, journal = {INFORMS Journal on Computing}, arxiv = {http://arxiv.org/abs/1710.04740}, year = {2020}, language = {en} } @inproceedings{CombettesPokutta2020, author = {Combettes, Cyrille W. and Pokutta, Sebastian}, title = {Boosting Frank-Wolfe by Chasing Gradients}, booktitle = {Proceedings of ICML}, arxiv = {http://arxiv.org/abs/2003.06369}, year = {2020}, language = {en} } @inproceedings{Pokutta2020, author = {Pokutta, Sebastian}, title = {Restarting Algorithms: Sometimes there is Free Lunch}, booktitle = {Proceedings of CPAIOR}, arxiv = {http://arxiv.org/abs/2006.14810}, year = {2020}, language = {en} } @article{LaydonSunkaraBoelenetal.2020, author = {Laydon, Daniel J. and Sunkara, Vikram and Boelen, Lies and Bangham, Charles R. M. and Asquith, Becca}, title = {The relative contributions of infectious and mitotic spread to HTLV-1 persistence}, journal = {PLOS Computational Biology}, doi = {10.1371/journal.pcbi.1007470}, year = {2020}, language = {en} } @article{KossenHirzelMadaietal.2022, author = {Kossen, Tabea and Hirzel, Manuel A. and Madai, Vince I. and Boenisch, Franziska and Hennemuth, Anja and Hildebrand, Kristian and Pokutta, Sebastian and Sharma, Kartikey and Hilbert, Adam and Sobesky, Jan and Galinovic, Ivana and Khalil, Ahmed A. and Fiebach, Jochen B. and Frey, Dietmar}, title = {Towards Sharing Brain Images: Differentially Private TOF-MRA Images with Segmentation Labels Using Generative Adversarial Networks}, journal = {Frontiers in Artificial Intelligence}, doi = {https://doi.org/10.3389/frai.2022.813842}, year = {2022}, abstract = {Sharing labeled data is crucial to acquire large datasets for various Deep Learning applications. In medical imaging, this is often not feasible due to privacy regulations. Whereas anonymization would be a solution, standard techniques have been shown to be partially reversible. Here, synthetic data using a Generative Adversarial Network (GAN) with differential privacy guarantees could be a solution to ensure the patient's privacy while maintaining the predictive properties of the data. In this study, we implemented a Wasserstein GAN (WGAN) with and without differential privacy guarantees to generate privacy-preserving labeled Time-of-Flight Magnetic Resonance Angiography (TOF-MRA) image patches for brain vessel segmentation. The synthesized image-label pairs were used to train a U-net which was evaluated in terms of the segmentation performance on real patient images from two different datasets. Additionally, the Fr{\´e}chet Inception Distance (FID) was calculated between the generated images and the real images to assess their similarity. During the evaluation using the U-Net and the FID, we explored the effect of different levels of privacy which was represented by the parameter ϵ. With stricter privacy guarantees, the segmentation performance and the similarity to the real patient images in terms of FID decreased. Our best segmentation model, trained on synthetic and private data, achieved a Dice Similarity Coefficient (DSC) of 0.75 for ϵ = 7.4 compared to 0.84 for ϵ = ∞ in a brain vessel segmentation paradigm (DSC of 0.69 and 0.88 on the second test set, respectively). We identified a threshold of ϵ <5 for which the performance (DSC <0.61) became unstable and not usable. Our synthesized labeled TOF-MRA images with strict privacy guarantees retained predictive properties necessary for segmenting the brain vessels. Although further research is warranted regarding generalizability to other imaging modalities and performance improvement, our results mark an encouraging first step for privacy-preserving data sharing in medical imaging.}, language = {en} } @article{RaharinirinaAcevedoTrejosMerico2022, author = {Raharinirina, N. Alexia and Acevedo-Trejos, Esteban and Merico, Agostino}, title = {Modelling the acclimation capacity of coral reefs to a warming ocean}, journal = {PLOS COMPUTATIONAL BIOLOGY}, doi = {10.1371/journal.pcbi.1010099}, year = {2022}, abstract = {The symbiotic relationship between corals and photosynthetic algae is the foundation of coral reef ecosystems. This relationship breaks down, leading to coral death, when sea temperature exceeds the thermal tolerance of the coral-algae complex. While acclimation via phenotypic plasticity at the organismal level is an important mechanism for corals to cope with global warming, community-based shifts in response to acclimating capacities may give valuable indications about the future of corals at a regional scale. Reliable regional-scale predictions, however, are hampered by uncertainties on the speed with which coral communities will be able to acclimate. Here we present a trait-based, acclimation dynamics model, which we use in combination with observational data, to provide a first, crude estimate of the speed of coral acclimation at the community level and to investigate the effects of different global warming scenarios on three iconic reef ecosystems of the tropics: Great Barrier Reef, South East Asia, and Caribbean. The model predicts that coral acclimation may confer some level of protection by delaying the decline of some reefs such as the Great Barrier Reef. However, the current rates of acclimation will not be sufficient to rescue corals from global warming. Based on our estimates of coral acclimation capacities, the model results suggest substantial declines in coral abundances in all three regions, ranging from 12\% to 55\%, depending on the region and on the climate change scenario considered. Our results highlight the importance and urgency of precise assessments and quantitative estimates, for example through laboratory experiments, of the natural acclimation capacity of corals and of the speed with which corals may be able to acclimate to global warming.}, language = {en} } @article{BeckerTeepleCharlesetal.2022, author = {Becker, Kaitlyn P and Teeple, Clark and Charles, Nicholas and Jung, Yeonsu and Baum, Daniel and Weaver, James C and Mahadevan, L. and Wood, Robert J}, title = {Active entanglement enables stochastic, topological grasping}, volume = {119}, journal = {PNAS}, number = {42}, doi = {10.1073/pnas.2209819119}, pages = {e2209819119}, year = {2022}, abstract = {Grasping, in both biological and engineered mechanisms, can be highly sensitive to the gripper and object morphology, as well as perception and motion planning. Here we circumvent the need for feedback or precise planning by using an array of fluidically-actuated slender hollow elastomeric filaments to actively entangle with objects that vary in geometric and topological complexity. The resulting stochastic interactions enable a unique soft and conformable grasping strategy across a range of target objects that vary in size, weight, and shape. We experimentally evaluate the grasping performance of our strategy, and use a computational framework for the collective mechanics of flexible filaments in contact with complex objects to explain our findings. Overall, our study highlights how active collective entanglement of a filament array via an uncontrolled, spatially distributed scheme provides new options for soft, adaptable grasping.}, language = {en} } @inproceedings{BernerRichterUllrich2024, author = {Berner, Julius and Richter, Lorenz and Ullrich, Karen}, title = {An optimal control perspective on diffusion-based generative modeling}, booktitle = {Transactions on Machine Learning Research}, year = {2024}, abstract = {We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs) such as recently developed diffusion probabilistic models. In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. This perspective allows to transfer methods from optimal control theory to generative modeling. First, we show that the evidence lower bound is a direct consequence of the well-known verification theorem from control theory. Further, we develop a novel diffusion-based method for sampling from unnormalized densities -- a problem frequently occurring in statistics and computational sciences.}, language = {en} }