In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: We propose Adversarial Inductive Transfer Learning (AITL), a method for addressing discrepancies in input and output spaces between source and target domains. AITL utilizes adversarial domain adaptation and multi-task learning to address these discrepancies. Our motivating application is pharmacogenomics where the goal is to predict drug response in patients using their genomic information. The challenge is that clinical data (i.e. patients) with drug response outcome is very limited, creating a need for transfer learning to bridge the gap between large pre-clinical pharmacogenomics datasets (e.g. cancer cell lines) and clinical datasets. Discrepancies exist between 1) the genomic data of pre-clinical and clinical datasets (the input space), and 2) the different measures of the drug response (the output space). To the best of our knowledge, AITL is the first adversarial inductive transfer learning method to address both input and output discrepancies. Experimental results indicate that AITL outperforms state-of-the-art pharmacogenomics and transfer learning baselines and may guide precision oncology more accurately.
[A]: A novel method of inductive transfer learning that employs adversarial learning and multi-task learning to address the discrepancy in input and output space

[Q]: The interpretability of an AI agent's behavior is of utmost importance for effective human-AI interaction. To this end, there has been increasing interest in characterizing and generating interpretable behavior of the agent. An alternative approach to guarantee that the agent generates interpretable behavior would be to design the agent's environment such that uninterpretable behaviors are either prohibitively expensive or unavailable to the agent. To date, there has been work under the umbrella of goal or plan recognition design exploring this notion of environment redesign in some specific instances of interpretable of behavior. In this position paper, we scope the landscape of interpretable behavior and environment redesign in all its different flavors. Specifically, we focus on three specific types of interpretable behaviors -- explicability, legibility, and predictability -- and present a general framework for the problem of environment design that can be instantiated to achieve each of the three interpretable behaviors. We also discuss how specific instantiations of this framework correspond to prior works on environment design and identify exciting opportunities for future work.
[A]: We present an approach to redesign the environment such that uninterpretable agent behaviors are minimized or eliminated.

[Q]: Current practice in machine learning is to employ deep nets in an overparametrized limit, with the nominal number of parameters typically exceeding the number of measurements. This resembles the situation in compressed sensing, or in sparse regression with $l_1$ penalty terms, and provides a theoretical avenue for understanding phenomena that arise in the context of deep nets. One such phenonemon is the success of deep nets in providing good generalization in an interpolating regime with zero training error. Traditional statistical practice calls for regularization or smoothing to prevent "overfitting" (poor generalization performance). However, recent work shows that there exist data interpolation procedures which are statistically consistent and provide good generalization performance\cite{belkin2018overfitting} ("perfect fitting"). In this context, it has been suggested that "classical" and "modern" regimes for machine learning are separated by a peak in the generalization error ("risk") curve, a phenomenon dubbed "double descent"\cite{belkin2019reconciling}. While such overfitting peaks do exist and arise from ill-conditioned design matrices, here we challenge the interpretation of the overfitting peak as demarcating the regime where good generalization occurs under overparametrization.

We propose a model of Misparamatrized Sparse Regression (MiSpaR) and analytically compute the GE curves for $l_2$ and $l_1$ penalties. We show that the overfitting peak arising in the interpolation limit is dissociated from the regime of good generalization. The analytical expressions are obtained in the so called "thermodynamic" limit. We find an additional interesting phenomenon: increasing overparametrization in the fitting model increases sparsity, which should intuitively improve performance of $l_1$ penalized regression. However, at the same time, the relative number of measurements decrease compared to the number of fitting parameters, and eventually overparametrization does lead to poor generalization. Nevertheless, $l_1$ penalized regression can show good generalization performance under conditions of data interpolation even with a large amount of overparametrization. These results provide a theoretical avenue into studying inverse problems in the interpolating regime using overparametrized fitting functions such as deep nets.
[A]:
Proposes an analytically tractable model and inference procedure (misparametrized sparse regression, inferred using L_1 penalty and studied in the data-interpolation limit) to study deep-net related phenomena in the context of inverse problems.