Uniform inference for bounds on the distribution and quantile functions of treatment effects in randomized experiments. (arXiv:1911.10215v1 [econ.EM])

This paper develops a novel approach to uniform inference for functions that bound the distribution and quantile functions of heterogeneous treatment effects in randomized experiments when only marginal treatment and control distributions are observed and the joint distribution of outcomes is unobserved. These bounds are nonlinear maps of the marginal distribution functions of control and…

A singular stochastic control approach for optimal pairs trading with proportional transaction costs. (arXiv:1911.10450v1 [q-fin.TR])

Optimal trading strategies for pairs trading have been studied by models that try to find either optimal shares of stocks by assuming no transaction costs or optimal timing of trading fixed numbers of shares of stocks with transaction costs. To find optimal strategies which determine optimally both trade times and number of shares in pairs…

Topologically Mapping the Macroeconomy. (arXiv:1911.10476v1 [econ.EM])

An understanding of the economic landscape in a world of ever increasing data necessitates representations of data that can inform policy, deepen understanding and guide future research. Topological Data Analysis offers a set of tools which deliver on all three calls. Abstract two-dimensional snapshots of multi-dimensional space readily capture non-monotonic relationships, inform of similarity between…

Weighted Laplacian and Its Theoretical Applications. (arXiv:1911.10311v1 [cs.LG])

In this paper, we develop a novel weighted Laplacian method, which is partially inspired by the theory of graph Laplacian, to study recent popular graph problems, such as multilevel graph partitioning and balanced minimum cut problem, in a more convenient manner. Since the weighted Laplacian strategy inherits the virtues of spectral methods, graph algorithms designed…

Compressing Representations for Embedded Deep Learning. (arXiv:1911.10321v1 [cs.LG])

Despite recent advances in architectures for mobile devices, deep learning computational requirements remains prohibitive for most embedded devices. To address that issue, we envision sharing the computational costs of inference between local devices and the cloud, taking advantage of the compression performed by the first layers of the networks to reduce communication costs. Inference in…

Meta Adaptation using Importance Weighted Demonstrations. (arXiv:1911.10322v1 [cs.LG])

Imitation learning has gained immense popularity because of its high sample-efficiency. However, in real-world scenarios, where the trajectory distribution of most of the tasks dynamically shifts, model fitting on continuously aggregated data alone would be futile. In some cases, the distribution shifts, so much, that it is difficult for an agent to infer the new…

Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya. (arXiv:1911.10339v1 [stat.AP])

Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge socioeconomic costs. Acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and human cost. However, existing EWS tend only to monitor current, rather than forecast future, environmental and socioeconomic indicators of drought, and…

GRASPEL: Graph Spectral Learning at Scale. (arXiv:1911.10373v1 [cs.LG])

Learning meaningful graphs from data plays important roles in many data mining and machine learning tasks, such as data representation and analysis, dimension reduction, data clustering, and visualization, etc. In this work, for the first time, we present a highly-scalable spectral approach (GRASPEL) for learning large graphs from data. By limiting the precision matrix to…

Bayesian nonparametric estimation in the current status continuous mark model. (arXiv:1911.10387v1 [math.ST])

In this paper we consider the current status continuous mark model where, if the event takes place before an inspection time $T$ a “continuous mark” variable is observed as well. A Bayesian nonparametric method is introduced for estimating the distribution function of the joint distribution of the event time ($X$) and mark ($Y$). We consider…

Scaling active inference. (arXiv:1911.10601v1 [cs.LG])

In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. ‘Active inference’ is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of how biological agents achieve this. On…