Out-of-Variable Generalization for Discriminative Models

The ability of an agent to do well in new environments is a critical aspect of intelligence. In machine learning, this ability is known as $\textit{strong}$ or $\textit{out-of-distribution}$ generalization. However, merely considering differences in data distributions is inadequate for fully capturing differences between learning environments. In the present paper, we investigate $\textit{out-of-variable}$ generalization, which pertains to an agent's generalization capabilities concerning environments with variables that were never jointly observed before. This skill closely reflects the process of animate learning: we, too, explore Nature by probing, observing, and measuring $\textit{subsets}$ of variables at any given time. Mathematically, $\textit{out-of-variable}$ generalization requires the efficient re-use of past marginal information, i.e., information over subsets of previously observed variables. We study this problem, focusing on prediction tasks across environments that contain overlapping, yet distinct, sets of causes. We show that after fitting a classifier, the residual distribution in one environment reveals the partial derivative of the true generating function with respect to the unobserved causal parent in that environment. We leverage this information and propose a method that exhibits non-trivial out-of-variable generalization performance when facing an overlapping, yet distinct, set of causal predictors

Generating entanglement between microwave photons and qubits in multiple cavities coupled by a superconducting qutrit

We discuss how to generate entangled coherent states of four \textrm{microwave} resonators \textrm{(a.k.a. cavities)} coupled by a superconducting qubit. We also show \textrm{that} a GHZ state of four superconducting qubits embedded in four different resonators \textrm{can be created with this scheme}. In principle, \textrm{the proposed method} can be extended to create an entangled coherent state of $n$ resonators and to prepare a Greenberger-Horne-Zeilinger (GHZ) state of $n$ qubits distributed over $n$ cavities in a quantum network. In addition, it is noted that four resonators coupled by a coupler qubit may be used as a basic circuit block to build a two-dimensional quantum network, which is useful for scalable quantum information processing.Comment: 13 pages, 7 figure

On the Interventional Kullback-Leibler Divergence

Modern machine learning approaches excel in static settings where a large amount of i.i.d. training data are available for a given task. In a dynamic environment, though, an intelligent agent needs to be able to transfer knowledge and re-use learned components across domains. It has been argued that this may be possible through causal models, aiming to mirror the modularity of the real world in terms of independent causal mechanisms. However, the true causal structure underlying a given set of data is generally not identifiable, so it is desirable to have means to quantify differences between models (e.g., between the ground truth and an estimate), on both the observational and interventional level. In the present work, we introduce the Interventional Kullback-Leibler (IKL) divergence to quantify both structural and distributional differences between models based on a finite set of multi-environment distributions generated by interventions from the ground truth. Since we generally cannot quantify all differences between causal models for every finite set of interventional distributions, we propose a sufficient condition on the intervention targets to identify subsets of observed variables on which the models provably agree or disagree

From Artifacts to Outcomes: Comparison of HMD VR, Desktop, and Slides Lectures for Food Microbiology Laboratory Instruction

Despite the value of VR (Virtual Reality) for educational purposes, the instructional power of VR in Biology Laboratory education remains under-explored. Laboratory lectures can be challenging due to students' low motivation to learn abstract scientific concepts and low retention rate. Therefore, we designed a VR-based lecture on fermentation and compared its effectiveness with lectures using PowerPoint slides and a desktop application. Grounded in the theory of distributed cognition and motivational theories, our study examined how learning happens in each condition from students' learning outcomes, behaviors, and perceptions. Our result indicates that VR facilitates students' long-term retention to learn by cultivating their longer visual attention and fostering a higher sense of immersion, though students' short-term retention remains the same across all conditions. This study extends current research on VR studies by identifying the characteristics of each teaching artifact and providing design implications for integrating VR technology into higher education