You are all cordially invited to the AMLab seminar on Thursday 20th February at 16:00 in C3.163, where Jan Günter Wöhlke from Boschwill give a talk titled “Tackling Sparse Rewards in Reinforcement Learning”.
Abstract: Sparse reward problems present a challenge for reinforcement learning (RL) agents. Previous work has shown that choosing start states according to a curriculum can significantly improve the learning performance. Many existing curriculum generation algorithms rely on two key components: Performance measure estimation and a start selection policy. In our recently accepted AAMAS paper, we therefore propose a unifying framework for performance-based start state curricula in RL, which allows analyzing and comparing the influence of the key components. Furthermore, a new start state selection policy is introduced. With extensive empirical evaluations, we demonstrate state-of-the-art performance of our novel curriculum on difficult robotic navigation tasks as well as a high-dimensional robotic manipulation task.
You are all cordially invited to the AMLab seminar on Thursday 21st November at 14:00 in C3.163, where Herke van Hoof will give a talk titled “Gradient estimation algorithms”. There are the usual drinks and snacks!
Abstract: In many cases, we cannot calculate exact gradients. This is the case if we cannot evaluate how well the model was expected to have done for different parameter values, for example if the model generates a sequence of stochastic decisions. Thus, many gradient estimators have been developed, from classical techniques from reinforcement learning to modern techniques such as the relax estimator. In e.g. meta-learning second derivative estimators have also been proposed. In this talk, I will attempt to give an overview of the properties of these techniques.
You are all cordially invited to the AMLab seminar on Thursday 24th October at 14:00 in D1.113, where Maurice Weiler will give a talk titled “Gauge Equivariant Convolutional Networks”. There are the usual drinks and snacks!
Abstract: The idea of equivariance to symmetry transformations provides one of the first theoretically grounded principles for neural network architecture design. Equivariant networks have shown excellent performance and data efficiency on vision and medical imaging problems that exhibit symmetries. We extend this principle beyond global symmetries to local gauge transformations, thereby enabling the development of equivariant convolutional networks on general manifolds. We show that gauge equivariant convolutional networks give a unified description of equivariant and geometric deep learning by deriving a wide range of models as special cases of our theory. To illustrate our theory on a simple example and highlight the interplay between local and global symmetries we discuss an implementation for signals defined on the icosahedron, which provides a reasonable approximation of spherical signals. We evaluate the Icosahedral CNN on omnidirectional image segmentation and climate pattern segmentation, and find that it outperforms previous methods.
You are all cordially invited to the AMLab seminar on Thursday 14th November at 14:00 in C3.163, where Sindy Löwe will give a talk titled “Putting An End to End-to-End: Gradient-Isolated Learning of Representations”. There are the usual drinks and snacks!
Abstract: We propose a novel deep learning method for local self-supervised representation learning that does not require labels nor end-to-end backpropagation but exploits the natural order in data instead. Inspired by the observation that biological neural networks appear to learn without backpropagating a global error signal, we split a deep neural network into a stack of gradient-isolated modules. Each module is trained to maximally preserve the information of its inputs using the InfoNCE bound from Oord et al. . Despite this greedy training, we demonstrate that each module improves upon the output of its predecessor, and that the representations created by the top module yield highly competitive results on downstream classiﬁcation tasks in the audio and visual domain. The proposal enables optimizing modules asynchronously, allowing large-scale distributed training of very deep neural networks on unlabelled datasets.
You are all cordially invited to the UvA-Bosch Delta lab seminar on Thursday October 17th October at 15:00 on the Roeterseilandcampus A2.11 , where David Blei, well known for his fantastic work on LDA, Bayesian nonparametrics, and variational inference. He will give a talk on “The Blessings of Multiple Causes”.
Causal inference from observational data is a vital problem, but itcomes with strong assumptions. Most methods require that we observeall confounders, variables that affect both the causal variables andthe outcome variables. But whether we have observed all confounders isa famously untestable assumption. We describe the deconfounder, a wayto do causal inference with weaker assumptions than the classicalmethods require.
How does the deconfounder work? While traditional causal methodsmeasure the effect of a single cause on an outcome, many modernscientific studies involve multiple causes, different variables whoseeffects are simultaneously of interest. The deconfounder uses thecorrelation among multiple causes as evidence for unobservedconfounders, combining unsupervised machine learning and predictivemodel checking to perform causal inference. We demonstrate thedeconfounder on real-world data and simulation studies, and describethe theoretical requirements for the deconfounder to provide unbiasedcausal estimates.
This is joint work with Yixin Wang.
David Blei is a Professor of Statistics and Computer Science atColumbia University, and a member of the Columbia Data ScienceInstitute. He studies probabilistic machine learning, including itstheory, algorithms, and application. David has received several awardsfor his research, including a Sloan Fellowship (2010), Office of NavalResearch Young Investigator Award (2011), Presidential Early CareerAward for Scientists and Engineers (2011), Blavatnik Faculty Award(2013), ACM-Infosys Foundation Award (2013), a Guggenheim fellowship(2017), and a Simons Investigator Award (2019). He is theco-editor-in-chief of the Journal of Machine Learning Research. He isa fellow of the ACM and the IMS.
You are all cordially invited to the special AMLab seminar on Tuesday 15th October at 12:00 in C1.112, where Will Grathwohl, from David Duvenaud’s group in Toronto will give a talk titled “The many virtues of Incorporating energy-based generative models into discriminative learning”.
Will is one of the authors behind many great recent papers. To name a few:
Abstract: Generative models have long been promised to benefit downstream discriminative machine learning applications such as out-of-distribution detection, adversarial robustness, uncertainty quantification, semi-supervised learning and many others. Yet, except for a few notable exceptions, methods for these tasks based on generative models are considerably outperformed by hand-tailored methods for each specific task. In this talk, I will advocate for the incorporation of energy-based generative models into the standard discriminative learning framework. Energy-Based Models (EBMs) can be much more easily incorporated into discriminative models than alternative generative modeling approaches and can benefit from network architectures designed for discriminative performance. I will present a novel method for jointly training EBMs alongside classifiers and demonstrate that this approach allows us to build models which rival the performance of state-of-the-art generative models and discriminative models within a single model. Further, we demonstrate our joint model gains many desirable properties such as a built-in mechanism for out-of-distribution detection, improved calibration, and improved robustness to adversarial examples — rivaling or improving upon hand-designed methods for each task.
You are all cordially invited to the AMLab seminar on Thursday 10th October at 14:00 in D1.113, where Andy Keller will give a talk titled “Approaches to Learning Approximate Equivariance”. There are the usual drinks and snacks!
Abstract: In this talk we will discuss a few proposed approaches to learning approximate equivariance directly from data. These approaches range from weakly supervised to fully unsupervised, relying on either mutual information bounds or inductive biases respectively. Critical discussion will be encouraged as much of the work is in early phases. Preliminary results will be shown to demonstrate validity of concepts.
You are all cordially invited to the AMLab seminar on Thursday 3rd October at 14:00 in B0.201, where Bhaskar Rao (visiting researcher: bio below)will give a talk titled “Scale Mixture Modeling of Priors for Sparse Signal Recovery”. There are the usual drinks and snacks!
Abstract: This talk will discuss Bayesian approaches to solving the sparse signal recovery problem. In particular, methods based on priors that admit a scale mixture representation will be discussed with emphasis on Gaussian scale mixture modeling. In the context of MAP estimation, iterative reweighted approaches will be developed. The scale mixture modeling naturally leads a hierarchical framework and empirical Bayesian methods motivated by this hierarchy will be highlighted. The pros and cons of the two approaches, MAP versus Empirical Bayes, will be a subject of discussion.
You are all cordially invited to the AMLab seminar on Thursday 12th September at 14:00 in C4.174, where Marco Federici will give a talk titled “Towards Robust Representations by Exploiting Multiple Data Views”. There are the usual drinks and snacks!
Abstract: The problem of creating data representations can be
formulated as the definition of an encoding function which maps
observations into a predefined code space. Whenever the encoding is used
as an intermediate step for a predictive task, among the possible
encodings, we are generally interested in the ones that retain the
desired target information. Furthermore, recent literature has shown
that discarding irrelevant factors of variation in the data (minimality)
yield robustness and invariance to nuisances of the task. Following
these two general guidelines, in this work, we introduce an
information-theoretical method that exploits some known properties of
the predictive task to create robust data representations without
requiring direct supervision signals. By exploiting pairs of joint
observations, our model learns representations that are as
discriminative as the original data for the predictive task while being
more robust than the raw-signal. The proposed theory builds upon
well-known self-supervised algorithms (such as Contrastive Predictive
Coding and the InfoMax principle), bridging the gap between information
bottleneck and probabilistic invariance. Empirical evidence shows the
applicability of our model for both multi-view and single-view datasets.
You are all cordially invited to the AMLab seminar on Thursday September 5th at 16:00 in C3.163, where Wouter van Amsterdam will give a talk titled “Controlling for Biasing Signals in Images for Prognostic Models: Survival Predictions for Lung Cancer with Deep Learning”. Afterwards there are the usual drinks and snacks!
Abstract: Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. Treatment recommendations are predictions with an inherently causal interpretation. To use deep learning for these applications, deep learning methods must be promoted from the level of mere associations to causal questions. We present a scenario with real-world medical images (CT-scans of lung cancers) and simulated outcome data. Through the data simulation scheme, the images contain two distinct factors of variation that are associated with survival, but represent a collider (tumor size) and a prognostic factor (tumor heterogeneity) respectively. We show that when this collider can be quantified, unbiased individual prognosis predictions are attainable with deep learning. This is achieved by (1) setting a dual task for the network to predict both the outcome and the collider and (2) enforcing a form of independence of the activation distributions of the last layer. Our method provides an example of combining deep learning and structural causal models to achieve unbiased individual prognosis predictions. Extensions of machine learning methods for applications to causal questions are required to attain the long standing goal of personalized medicine supported by artificial intelligence.