Causal Transfer for Imitation Learning and Decision Making under Sensor-shift

Learning from demonstrations (LfD) is an efficient paradigm to train AI agents. But major issues arise when there are differences between (a) the demonstrator's own sensory input, (b) our sensors that observe the demonstrator and (c) the sensory input of the agent we train. In this paper, we propose a causal model-based framework for transfer learning under such "sensor-shifts", for two common LfD tasks: (1) inferring the effect of the demonstrator's actions and (2) imitation learning. First we rigorously analyze, on the population-level, to what extent the relevant underlying mechanisms (the action effects and the demonstrator policy) can be identified and transferred from the available observations together with prior knowledge of sensor characteristics. And we device an algorithm to infer these mechanisms. Then we introduce several proxy methods which are easier to calculate, estimate from finite data and interpret than the exact solutions, alongside theoretical bounds on their closeness to the exact ones. We validate our two main methods on simulated and semi-real world data.Comment: It appears in AAAI-202

Stochastic Yield Catastrophes and Robustness in Self-Assembly

A guiding principle in self-assembly is that, for high production yield, nucleation of structures must be significantly slower than their growth. However, details of the mechanism that impedes nucleation are broadly considered irrelevant. Here, we analyze self-assembly into finite-sized target structures employing mathematical modeling. We investigate two key scenarios to delay nucleation: (i) by introducing a slow activation step for the assembling constituents and, (ii) by decreasing the dimerization rate. These scenarios have widely different characteristics. While the dimerization scenario exhibits robust behavior, the activation scenario is highly sensitive to demographic fluctuations. These demographic fluctuations ultimately disfavor growth compared to nucleation and can suppress yield completely. The occurrence of this stochastic yield catastrophe does not depend on model details but is generic as soon as number fluctuations between constituents are taken into account. On a broader perspective, our results reveal that stochasticity is an important limiting factor for self-assembly and that the specific implementation of the nucleation process plays a significant role in determining the yield

Causal models for decision making via integrative inference

Understanding causes and effects is important in many parts of life, especially when decisions have to be made. The systematic inference of causal models remains a challenge though. In this thesis, we study (1) "approximative" and "integrative" inference of causal models and (2) causal models as a basis for decision making in complex systems. By "integrative" here we mean including and combining settings and knowledge beyond the outcome of perfect randomization or pure observation for causal inference, while "approximative" means that the causal model is only constrained but not uniquely identified. As a basis for the study of topics (1) and (2), which are closely related, we first introduce causal models, discuss the meaning of causation and embed the notion of causation into a broader context of other fundamental concepts. Then we begin our main investigation with a focus on topic (1): we consider the problem of causal inference from a non-experimental multivariate time series X, that is, we integrate temporal knowledge. We take the following approach: We assume that X together with some potential hidden common cause - "confounder" - Z forms a first order vector autoregressive (VAR) process with structural transition matrix A. Then we examine under which conditions the most important parts of A are identifiable or approximately identifiable from only X, in spite of the effects of Z. Essentially, sufficient conditions are (a) non-Gaussian, independent noise or (b) no influence from X to Z. We present two estimation algorithms that are tailored towards conditions (a) and (b), respectively, and evaluate them on synthetic and real-world data. We discuss how to check the model using X. Still focusing on topic (1) but already including elements of topic (2), we consider the problem of approximate inference of the causal effect of a variable X on a variable Y in i.i.d. settings "between" randomized experiments and observational studies. Our approach is to first derive approximations (upper/lower bounds) on the causal effect, in dependence on bounds on (hidden) confounding. Then we discuss several scenarios where knowledge or beliefs can be integrated that in fact imply bounds on confounding. One example is about decision making in advertisement, where knowledge on partial compliance with guidelines can be integrated. Then, concentrating on topic (2), we study decision making problems that arise in cloud computing, a computing paradigm and business model that involves complex technical and economical systems and interactions. More specifically, we consider the following two problems: debugging and control of computing systems with the help of sandbox experiments, and prediction of the cost of "spot" resources for decision making of cloud clients. We first establish two theoretical results on approximate counterfactuals and approximate integration of causal knowledge, which we then apply to the two problems in toy scenarios

THz conductivity of Sr1−x_{1-x}Cax_xRuO3_3

We investigate the optical conductivity of Sr$_{1-x}$Ca$_x$RuO$_3$ across the ferromagnetic to paramagnetic transition that occurs at $x=0.8$. The thin films were grown by metalorganic aerosol deposition with $0 \leq x \leq 1$ onto NdGaO$_3$ substrates. We performed THz frequency domain spectroscopy in a frequency range from 3~cm$^{-1}$ to 40~cm$^{-1}$ (100~GHz to 1.4~THz) and at temperatures ranging from 5~K to 300~K, measuring transmittivity and phase shift through the films. From this we obtained real and imaginary parts of the optical conductivity. The end-members, ferromagnetic SrRuO$_3$ and paramagnetic CaRuO$_3$, show a strongly frequency-dependent metallic response at temperatures below 20~K. Due to the high quality of these samples we can access pronounced intrinsic electronic contributions to the optical scattering rate, which at 1.4~THz exceeds the residual scattering rate by more than a factor of three. Deviations from a Drude response start at about 0.7~THz for both end-members in a remarkably similar way. For the intermediate members a higher residual scattering originating in the compositional disorder leads to a featureless optical response, instead. The relevance of low-lying interband transitions is addressed by a calculation of the optical conductivity within density functional theory in the local density approximation (LDA)