You are all cordially invited to the AMLab seminar on Tuesday October 31 at 16:00 in C3.163, where Elise van der Pol will give a talk titled “Graph-based Sequential Decision Making Under Uncertainty”. Afterwards there are the usual drinks and snacks!
Abstract: In sequential decision making under uncertainty, an agent attempts to find some function that maps from states to actions, such that a reward signal is maximized, taking both immediate and future reward into account. Under the graph-based perspective, we view the problem of optimal sequential decision making as doing inference in a graphical model.
In this talk I will present some of the research related to this perspective and connect it to recent work in Deep Learning such as Value Iteration Networks and Graph Convolutional Networks.
You are all cordially invited to the AMLab seminar on Tuesday October 24 at 16:00 in C3.163, where Sara Magliacane will give a talk titled “Joint Causal Inference from Observational and Experimental Datasets”. Afterwards there are the usual drinks and snacks!
Abstract: Joint Causal Inference (JCI) is a recently proposed causal discovery framework that aims to discover causal relations based on multiple observational and experimental datasets, also in the presence of latent variables. Compared with current methods for causal inference, JCI allows to jointly learn both the causal structure and intervention targets by pooling data from different experimental conditions in a systematic way. This systematic pooling also improves the statistical power of the independence tests used to recover the causal relations, while the introduction of context variables can improve the identifiability of causal relations. In this talk I will introduce JCI and show two possible implementations using three recent causal discovery methods from literature, Ancestral Causal Inference [Magliacane et al. 2016], [Hyttinen et al. 2014] and Greedy Fast Causal Inference [Ogarrio et al. 2016]. Moreover, I will show the benefits of JCI in an evaluation on synthetic data and in an application to the flow cytometry dataset from [Sachs et al. 2005].
You are all cordially invited to the AMLab seminar on Tuesday October 10 at 16:00 in C3.163, where Peter O’Connor will give a talk titled “Towards Event-Based online Learning”. Afterwards there are the usual drinks and snacks!
Abstract: The world that our brains experience is quite different from the world that most of our ML models experience. Most models in machine learning are now trained by randomly sampling data from some training set, updating the model, then repeating. When temporal data is considered, it is usually split into short sequences, where each sequence is considered to be a sample from some underlying distribution of sequences, which we wish to learn. Humans on the other hand, learn online – we receive a single, never-ending sequence of inputs. Moreover, these inputs come in asynchronously, and rather than representing the state of the world at a given time, represent that some aspect of the state of the world has changed.
In this talk, I’ll discuss some work we are doing close this gap, and allow us to apply the methods used in deep learning to the more natural online-learning setting.