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2020


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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach

Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution (conference) Accepted

ei

arXiv [BibTex]

2020


arXiv [BibTex]


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Self-Paced Deep Reinforcement Learning

Klink, P., D’Eramo, C., Peters, J., Pajarinen, J.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Probabilistic Linear Solvers for Machine Learning

Wenger, J., Hennig, P.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Barking up the right tree: an approach to search over molecule synthesis DAGs

Bradshaw, J., Paige, B., Kusner, M., Segler, M., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Learning Kernel Tests Without Data Splitting

Kübler, J., Jitkrittum, W., Schölkopf, B., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Dual Instrumental Variable Regression

Muandet, K., Mehrjou, A., Lee, S. K., Raj, A.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings

Park, J., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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MATE: Plugging in Model Awareness to Task Embedding for Meta Learning

Chen, X., Wang, Z., Tang, S., Muandet, K.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Object-Centric Learning with Slot Attention

Locatello, F., Weissenborn, D., Unterthiner, T., Mahendran, A., Heigold, G., Uszkoreit, J., Dosovitskiy, A., Kipf, T.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Relative gradient optimization of the Jacobian term in unsupervised deep learning

Gresele, L., Fissore, G., Javaloy, A., Schölkopf, B., Hyvarinen, A.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Causal analysis of Covid-19 Spread in Germany

Mastakouri, A., Schölkopf, B.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Modeling Shared responses in Neuroimaging Studies through MultiView ICA

Richard, H., Gresele, L., Hyvarinen, A., Thirion, B., Gramfort, A., Ablin, P.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Stochastic Stein Discrepancies

Gorham, J., Raj, A., Mackey, L.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


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Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining

Tripp, A., Daxberger, E., Hernández-Lobato, J. M.

Advances in Neural Information Processing Systems 33, 34th Annual Conference on Neural Information Processing Systems, December 2020 (conference) Accepted

ei

[BibTex]

[BibTex]


Grasping Field: Learning Implicit Representations for Human Grasps
Grasping Field: Learning Implicit Representations for Human Grasps

Karunratanakul, K., Yang, J., Zhang, Y., Black, M., Muandet, K., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Robotic grasping of house-hold objects has made remarkable progress in recent years. Yet, human grasps are still difficult to synthesize realistically. There are several key reasons: (1) the human hand has many degrees of freedom (more than robotic manipulators); (2) the synthesized hand should conform to the surface of the object; and (3) it should interact with the object in a semantically and physically plausible manner. To make progress in this direction, we draw inspiration from the recent progress on learning-based implicit representations for 3D object reconstruction. Specifically, we propose an expressive representation for human grasp modelling that is efficient and easy to integrate with deep neural networks. Our insight is that every point in a three-dimensional space can be characterized by the signed distances to the surface of the hand and the object, respectively. Consequently, the hand, the object, and the contact area can be represented by implicit surfaces in a common space, in which the proximity between the hand and the object can be modelled explicitly. We name this 3D to 2D mapping as Grasping Field, parameterize it with a deep neural network, and learn it from data. We demonstrate that the proposed grasping field is an effective and expressive representation for human grasp generation. Specifically, our generative model is able to synthesize high-quality human grasps, given only on a 3D object point cloud. The extensive experiments demonstrate that our generative model compares favorably with a strong baseline and approaches the level of natural human grasps. Furthermore, based on the grasping field representation, we propose a deep network for the challenging task of 3D hand-object interaction reconstruction from a single RGB image. Our method improves the physical plausibility of the hand-object contact reconstruction and achieves comparable performance for 3D hand reconstruction compared to state-of-the-art methods. Our model and code are available for research purpose at https://github.com/korrawe/grasping_field.

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pdf arXiv code [BibTex]


{GIF}: Generative Interpretable Faces
GIF: Generative Interpretable Faces

Ghosh, P., Gupta, P. S., Uziel, R., Ranjan, A., Black, M. J., Bolkart, T.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
Photo-realistic visualization and animation of expressive human faces have been a long standing challenge. 3D face modeling methods provide parametric control but generates unrealistic images, on the other hand, generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control. Recent methods gain partial control, either by attempting to disentangle different factors in an unsupervised manner, or by adding control post hoc to a pre-trained model. Unconditional GANs, however, may entangle factors that are hard to undo later. We condition our generative model on pre-defined control parameters to encourage disentanglement in the generation process. Specifically, we condition StyleGAN2 on FLAME, a generative 3D face model. While conditioning on FLAME parameters yields unsatisfactory results, we find that conditioning on rendered FLAME geometry and photometric details works well. This gives us a generative 2D face model named GIF (Generative Interpretable Faces) that offers FLAME's parametric control. Here, interpretable refers to the semantic meaning of different parameters. Given FLAME parameters for shape, pose, expressions, parameters for appearance, lighting, and an additional style vector, GIF outputs photo-realistic face images. We perform an AMT based perceptual study to quantitatively and qualitatively evaluate how well GIF follows its conditioning. The code, data, and trained model are publicly available for research purposes at http://gif.is.tue.mpg.de

ps

pdf project code [BibTex]

pdf project code [BibTex]


{PLACE}: Proximity Learning of Articulation and Contact in {3D} Environments
PLACE: Proximity Learning of Articulation and Contact in 3D Environments

Zhang, S., Zhang, Y., Ma, Q., Black, M. J., Tang, S.

In International Conference on 3D Vision (3DV), November 2020 (inproceedings)

Abstract
High fidelity digital 3D environments have been proposed in recent years, however, it remains extremely challenging to automatically equip such environment with realistic human bodies. Existing work utilizes images, depth or semantic maps to represent the scene, and parametric human models to represent 3D bodies. While being straight-forward, their generated human-scene interactions often lack of naturalness and physical plausibility. Our key observation is that humans interact with the world through body-scene contact. To synthesize realistic human-scene interactions, it is essential to effectively represent the physical contact and proximity between the body and the world. To that end, we propose a novel interaction generation method, named PLACE(Proximity Learning of Articulation and Contact in 3D Environments), which explicitly models the proximity between the human body and the 3D scene around it. Specifically, given a set of basis points on a scene mesh, we leverage a conditional variational autoencoder to synthesize the minimum distances from the basis points to the human body surface. The generated proximal relationship exhibits which region of the scene is in contact with the person. Furthermore, based on such synthesized proximity, we are able to effectively obtain expressive 3D human bodies that interact with the 3D scene naturally. Our perceptual study shows that PLACE significantly improves the state-of-the-art method, approaching the realism of real human-scene interaction. We believe our method makes an important step towards the fully automatic synthesis of realistic 3D human bodies in 3D scenes. The code and model are available for research at https://sanweiliti.github.io/PLACE/PLACE.html

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pdf arXiv project code [BibTex]

pdf arXiv project code [BibTex]


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MYND: Unsupervised Evaluation of Novel BCI Control Strategies on Consumer Hardware

Hohmann, M. R., Konieczny, L., Hackl, M., Wirth, B., Zaman, T., Enficiaud, R., Grosse-Wentrup, M., Schölkopf, B.

Proceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology (UIST), October 2020 (conference) Accepted

ei

arXiv DOI [BibTex]

arXiv DOI [BibTex]


Learning a statistical full spine model from partial observations
Learning a statistical full spine model from partial observations

Meng, D., Keller, M., Boyer, E., Black, M., Pujades, S.

In Shape in Medical Imaging, pages: 122,133, (Editors: Reuter, Martin and Wachinger, Christian and Lombaert, Hervé and Paniagua, Beatriz and Goksel, Orcun and Rekik, Islem), Springer International Publishing, October 2020 (inproceedings)

Abstract
The study of the morphology of the human spine has attracted research attention for its many potential applications, such as image segmentation, bio-mechanics or pathology detection. However, as of today there is no publicly available statistical model of the 3D surface of the full spine. This is mainly due to the lack of openly available 3D data where the full spine is imaged and segmented. In this paper we propose to learn a statistical surface model of the full-spine (7 cervical, 12 thoracic and 5 lumbar vertebrae) from partial and incomplete views of the spine. In order to deal with the partial observations we use probabilistic principal component analysis (PPCA) to learn a surface shape model of the full spine. Quantitative evaluation demonstrates that the obtained model faithfully captures the shape of the population in a low dimensional space and generalizes to left out data. Furthermore, we show that the model faithfully captures the global correlations among the vertebrae shape. Given a partial observation of the spine, i.e. a few vertebrae, the model can predict the shape of unseen vertebrae with a mean error under 3 mm. The full-spine statistical model is trained on the VerSe 2019 public dataset and is publicly made available to the community for non-commercial purposes. (https://gitlab.inria.fr/spine/spine_model)

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Gitlab Code PDF DOI [BibTex]

Gitlab Code PDF DOI [BibTex]


A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition
A Gamified App that Helps People Overcome Self-Limiting Beliefs by Promoting Metacognition

Amo, V., Lieder, F.

SIG 8 Meets SIG 16, September 2020 (conference) Accepted

Abstract
Previous research has shown that approaching learning with a growth mindset is key for maintaining motivation and overcoming setbacks. Mindsets are systems of beliefs that people hold to be true. They influence a person's attitudes, thoughts, and emotions when they learn something new or encounter challenges. In clinical psychology, metareasoning (reflecting on one's mental processes) and meta-awareness (recognizing thoughts as mental events instead of equating them to reality) have proven effective for overcoming maladaptive thinking styles. Hence, they are potentially an effective method for overcoming self-limiting beliefs in other domains as well. However, the potential of integrating assisted metacognition into mindset interventions has not been explored yet. Here, we propose that guiding and training people on how to leverage metareasoning and meta-awareness for overcoming self-limiting beliefs can significantly enhance the effectiveness of mindset interventions. To test this hypothesis, we develop a gamified mobile application that guides and trains people to use metacognitive strategies based on Cognitive Restructuring (CR) and Acceptance Commitment Therapy (ACT) techniques. The application helps users to identify and overcome self-limiting beliefs by working with aversive emotions when they are triggered by fixed mindsets in real-life situations. Our app aims to help people sustain their motivation to learn when they face inner obstacles (e.g. anxiety, frustration, and demotivation). We expect the application to be an effective tool for helping people better understand and develop the metacognitive skills of emotion regulation and self-regulation that are needed to overcome self-limiting beliefs and develop growth mindsets.

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A gamified app that helps people overcome self-limiting beliefs by promoting metacognition [BibTex]


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Model-Agnostic Counterfactual Explanations for Consequential Decisions

Karimi, A., Barthe, G., Balle, B., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 895-905, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

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arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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More Powerful Selective Kernel Tests for Feature Selection

Lim, J. N., Yamada, M., Jitkrittum, W., Terada, Y., Matsui, S., Shimodaira, H.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 820-830, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

ei

arXiv link (url) [BibTex]

arXiv link (url) [BibTex]


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Bayesian Online Prediction of Change Points

Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B., Peters, J.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 320-329, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Semi-supervised learning, causality, and the conditional cluster assumption

von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI) , 124, pages: 1-10, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Kernel Conditional Moment Test via Maximum Moment Restriction

Muandet, K., Jitkrittum, W., Kübler, J. M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 41-50, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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On the design of consequential ranking algorithms

Tabibian, B., Gómez, V., De, A., Schölkopf, B., Gomez Rodriguez, M.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 171-180, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Importance Sampling via Local Sensitivity

Raj, A., Musco, C., Mackey, L.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 3099-3109, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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A Continuous-time Perspective for Modeling Acceleration in Riemannian Optimization

F Alimisis, F., Orvieto, A., Becigneul, G., Lucchi, A.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 1297-1307, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Fair Decisions Despite Imperfect Predictions

Kilbertus, N., Gomez Rodriguez, M., Schölkopf, B., Muandet, K., Valera, I.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 277-287, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

ei plg

link (url) [BibTex]

link (url) [BibTex]


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Integrals over Gaussians under Linear Domain Constraints

Gessner, A., Kanjilal, O., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 2764-2774, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


STAR: Sparse Trained Articulated Human Body Regressor
STAR: Sparse Trained Articulated Human Body Regressor

Osman, A. A. A., Bolkart, T., Black, M. J.

In European Conference on Computer Vision (ECCV) , LNCS 12355, pages: 598-613, August 2020 (inproceedings)

Abstract
The SMPL body model is widely used for the estimation, synthesis, and analysis of 3D human pose and shape. While popular, we show that SMPL has several limitations and introduce STAR, which is quantitatively and qualitatively superior to SMPL. First, SMPL has a huge number of parameters resulting from its use of global blend shapes. These dense pose-corrective offsets relate every vertex on the mesh to all the joints in the kinematic tree, capturing spurious long-range correlations. To address this, we define per-joint pose correctives and learn the subset of mesh vertices that are influenced by each joint movement. This sparse formulation results in more realistic deformations and significantly reduces the number of model parameters to 20% of SMPL. When trained on the same data as SMPL, STAR generalizes better despite having many fewer parameters. Second, SMPL factors pose-dependent deformations from body shape while, in reality, people with different shapes deform differently. Consequently, we learn shape-dependent pose-corrective blend shapes that depend on both body pose and BMI. Third, we show that the shape space of SMPL is not rich enough to capture the variation in the human population. We address this by training STAR with an additional 10,000 scans of male and female subjects, and show that this results in better model generalization. STAR is compact, generalizes better to new bodies and is a drop-in replacement for SMPL. STAR is publicly available for research purposes at http://star.is.tue.mpg.de.

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Project Page Code Video paper supplemental DOI [BibTex]

Project Page Code Video paper supplemental DOI [BibTex]


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Modular Block-diagonal Curvature Approximations for Feedforward Architectures

Dangel, F., Harmeling, S., Hennig, P.

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), 108, pages: 799-808, Proceedings of Machine Learning Research, (Editors: Silvia Chiappa and Roberto Calandra), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Monocular Expressive Body Regression through Body-Driven Attention
Monocular Expressive Body Regression through Body-Driven Attention

Choutas, V., Pavlakos, G., Bolkart, T., Tzionas, D., Black, M. J.

In Computer Vision – ECCV 2020, LNCS 12355, pages: 20-40, Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
To understand how people look, interact, or perform tasks,we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de.

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code Short video Long video arxiv pdf suppl link (url) DOI Project Page Project Page [BibTex]

code Short video Long video arxiv pdf suppl link (url) DOI Project Page Project Page [BibTex]


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Testing Goodness of Fit of Conditional Density Models with Kernels

Jitkrittum, W., Kanagawa, H., Schölkopf, B.

Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI), 124, pages: 221-230, Proceedings of Machine Learning Research, (Editors: Jonas Peters and David Sontag), PMLR, August 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


GRAB: A Dataset of Whole-Body Human Grasping of Objects
GRAB: A Dataset of Whole-Body Human Grasping of Objects

Taheri, O., Ghorbani, N., Black, M. J., Tzionas, D.

In Computer Vision – ECCV 2020, LNCS 12355, pages: 581-600, Springer International Publishing, Cham, August 2020 (inproceedings)

Abstract
Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time. While "grasping" is commonly thought of as a single hand stably lifting an object, we capture the motion of the entire body and adopt the generalized notion of "whole-body grasps". Thus, we collect a new dataset, called GRAB (GRasping Actions with Bodies), of whole-body grasps, containing full 3D shape and pose sequences of 10 subjects interacting with 51 everyday objects of varying shape and size. Given MoCap markers, we fit the full 3D body shape and pose, including the articulated face and hands, as well as the 3D object pose. This gives detailed 3D meshes over time, from which we compute contact between the body and object. This is a unique dataset, that goes well beyond existing ones for modeling and understanding how humans grasp and manipulate objects, how their full body is involved, and how interaction varies with the task. We illustrate the practical value of GRAB with an example application; we train GrabNet, a conditional generative network, to predict 3D hand grasps for unseen 3D object shapes. The dataset and code are available for research purposes at https://grab.is.tue.mpg.de.

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pdf suppl video (long) video (short) link (url) DOI Project Page [BibTex]

pdf suppl video (long) video (short) link (url) DOI Project Page [BibTex]


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How to navigate everyday distractions: Leveraging optimal feedback to train attention control

Wirzberger, M., Lado, A., Eckerstorfer, L., Oreshnikov, I., Passy, J., Stock, A., Shenhav, A., Lieder, F.

Annual Meeting of the Cognitive Science Society, July 2020 (conference)

Abstract
To stay focused on their chosen tasks, people have to inhibit distractions. The underlying attention control skills can improve through reinforcement learning, which can be accelerated by giving feedback. We applied the theory of metacognitive reinforcement learning to develop a training app that gives people optimal feedback on their attention control while they are working or studying. In an eight-day field experiment with 99 participants, we investigated the effect of this training on people's productivity, sustained attention, and self-control. Compared to a control condition without feedback, we found that participants receiving optimal feedback learned to focus increasingly better (f = .08, p < .01) and achieved higher productivity scores (f = .19, p < .01) during the training. In addition, they evaluated their productivity more accurately (r = .12, p < .01). However, due to asymmetric attrition problems, these findings need to be taken with a grain of salt.

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How to navigate everyday distractions: Leveraging optimal feedback to train attention control DOI Project Page [BibTex]


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Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization

Negiar, G., Dresdner, G., Tsai, A. Y., El Ghaoui, L., Locatello, F., Freund, R. M., Pedregosa, F.

37th International Conference on Machine Learning (ICML), pages: 296-305, July 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Variational Autoencoders with Riemannian Brownian Motion Priors

Kalatzis, D., Eklund, D., Arvanitidis, G., Hauberg, S.

37th International Conference on Machine Learning (ICML), pages: 6789-6799, July 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Variational Bayes in Private Settings (VIPS) (Extended Abstract)

Foulds, J. R., Park, M., Chaudhuri, K., Welling, M.

Proceedings of the 29th International Joint Conference on Artificial Intelligence, (IJCAI-PRICAI), pages: 5050-5054, (Editors: Christian Bessiere), International Joint Conferences on Artificial Intelligence Organization, July 2020, Journal track (conference)

ei

link (url) DOI [BibTex]

link (url) DOI [BibTex]


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Weakly-Supervised Disentanglement Without Compromises

Locatello, F., Poole, B., Rätsch, G., Schölkopf, B., Bachem, O., Tschannen, M.

37th International Conference on Machine Learning (ICML), pages: 7753-7764, July 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks

Kristiadi, A., Hein, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 1226-1236, July 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Constant Curvature Graph Convolutional Networks

Bachmann*, G., Becigneul*, G., Ganea, O.

37th International Conference on Machine Learning (ICML), pages: 9118-9128, July 2020, *equal contribution (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


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Leveraging Machine Learning to Automatically Derive Robust Planning Strategies from Biased Models of the Environment

Kemtur, A., Jain, Y. R., Mehta, A., Callaway, F., Consul, S., Stojcheski, J., Lieder, F.

CogSci 2020, July 2020, Anirudha Kemtur and Yash Raj Jain contributed equally to this publication. (conference)

Abstract
Teaching clever heuristics is a promising approach to improve decision-making. We can leverage machine learning to discover clever strategies automatically. Current methods require an accurate model of the decision problems people face in real life. But most models are misspecified because of limited information and cognitive biases. To address this problem we develop strategy discovery methods that are robust to model misspecification. Robustness is achieved by model-ing model-misspecification and handling uncertainty about the real-world according to Bayesian inference. We translate our methods into an intelligent tutor that automatically discovers and teaches robust planning strategies. Our robust cognitive tutor significantly improved human decision-making when the model was so biased that conventional cognitive tutors were no longer effective. These findings highlight that our robust strategy discovery methods are a significant step towards leveraging artificial intelligence to improve human decision-making in the real world.

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Project Page [BibTex]

Project Page [BibTex]


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Differentiable Likelihoods for Fast Inversion of ‘Likelihood-Free’ Dynamical Systems

Kersting, H., Krämer, N., Schiegg, M., Daniel, C., Tiemann, M., Hennig, P.

37th International Conference on Machine Learning (ICML), pages: 2655-2665, July 2020 (conference)

ei

link (url) [BibTex]

link (url) [BibTex]


Learning of sub-optimal gait controllers for magnetic walking soft millirobots
Learning of sub-optimal gait controllers for magnetic walking soft millirobots

Culha, U., Demir, S. O., Trimpe, S., Sitti, M.

In Proceedings of Robotics: Science and Systems, 2020 (inproceedings)

Abstract
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time,which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

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link (url) DOI [BibTex]


Actively Learning Gaussian Process Dynamics
Actively Learning Gaussian Process Dynamics

Buisson-Fenet, M., Solowjow, F., Trimpe, S.

2nd Annual Conference on Learning for Dynamics and Control, June 2020 (conference) Accepted

Abstract
Despite the availability of ever more data enabled through modern sensor and computer technology, it still remains an open problem to learn dynamical systems in a sample-efficient way. We propose active learning strategies that leverage information-theoretical properties arising naturally during Gaussian process regression, while respecting constraints on the sampling process imposed by the system dynamics. Sample points are selected in regions with high uncertainty, leading to exploratory behavior and data-efficient training of the model. All results are verified in an extensive numerical benchmark.

ics

ArXiv [BibTex]

ArXiv [BibTex]


Learning to Dress 3D People in Generative Clothing
Learning to Dress 3D People in Generative Clothing

Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., Black, M. J.

In Computer Vision and Pattern Recognition (CVPR), pages: 6468-6477, IEEE, June 2020 (inproceedings)

Abstract
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shape. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term on SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.

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Project page Code Short video Long video arXiv DOI [BibTex]

Project page Code Short video Long video arXiv DOI [BibTex]