Exploratory Control with Tsallis Entropy for Latent Factor Models

We study optimal control in models with latent factors where the agent controls the distribution over actions, rather than actions themselves, in both discrete and continuous time. To encourage exploration of the state space, we reward exploration with Tsallis entropy and derive the optimal distribution over states—which we prove is q-Gaussian distributed with location characterized through the solution of an BSΔE and BSDE in discrete and continuous time, respectively. We discuss the relation between the solutions of the optimal exploration problems and the standard dynamic optimal control solution. Finally, we develop the optimal policy in a model-agnostic setting along the lines of soft Q-learning. The approach may be applied in, e.g., developing more robust statistical arbitrage trading strategies

The CoNLL 2007 shared task on dependency parsing

The Conference on Computational Natural Language Learning features a shared task, in which participants train and test their learning systems on the same data sets. In 2007, as in 2006, the shared task has been devoted to dependency parsing, this year with both a multilingual track and a domain adaptation track. In this paper, we define the tasks of the different tracks and describe how the data sets were created from existing treebanks for ten languages. In addition, we characterize the different approaches of the participating systems, report the test results, and provide a first analysis of these results

Hedging Non-Tradable Risks with Transaction Costs and Price Impact

A risk-averse agent hedges her exposure to a non-tradable risk factor $U$ using a correlated traded asset $S$ and accounts for the impact of her trades on both factors. The effect of the agent's trades on $U$ is referred to as cross-impact. By solving the agent's stochastic control problem, we obtain a closed-form expression for the optimal strategy when the agent holds a linear position in $U$. When the exposure to the non-tradable risk factor $\psi(U_T)$ is non-linear, we provide an approximation to the optimal strategy in closed-form, and prove that the value function is correctly approximated by this strategy when cross-impact and risk-aversion are small. We further prove that when $\psi(U_T)$ is non-linear, the approximate optimal strategy can be written in terms of the optimal strategy for a linear exposure with the size of the position changing dynamically according to the exposure's "Delta" under a particular probability measure.Comment: Originally posted to SSRN April 27, 2018. Forthcoming in Mathematical Financ

Path-Specific Objectives for Safer Agent Incentives

We present a general framework for training safe agents whose naive incentives are unsafe. As an example, manipulative or deceptive behaviour can improve rewards but should be avoided. Most approaches fail here: agents maximize expected return by any means necessary. We formally describe settings with 'delicate' parts of the state which should not be used as a means to an end. We then train agents to maximize the causal effect of actions on the expected return which is not mediated by the delicate parts of state, using Causal Influence Diagram analysis. The resulting agents have no incentive to control the delicate state. We further show how our framework unifies and generalizes existing proposals.Comment: Presented at AAAI 202

Sustainable Rooftop Technologies: A Structural Analysis of Buildings at WPI

This project evaluated the feasibility of the installation of sustainable rooftop technologies on selected buildings at Worcester Polytechnic Institute (WPI). This report includes the structural analysis and design of three sustainable rooftop technologies: solar panels, green roofs, and solar collectors. These technologies have the ability to save energy, while contributing to WPIs sustainability plan. Additionally, an economic analysis is prepared to show the simple payback periods of installing these sustainable rooftop technologies

Decision heuristic or preference? Attribute non-attendance in discrete choice problems

Funded by National Clinical Assessment Service (NCAS) and Institute of Applied Health SciencePeer reviewedPostprin

Morphologically Aware Word-Level Translation

We propose a novel morphologically aware probability model for bilingual lexicon induction, which jointly models lexeme translation and inflectional morphology in a structured way. Our model exploits the basic linguistic intuition that the lexeme is the key lexical unit of meaning, while inflectional morphology provides additional syntactic information. This approach leads to substantial performance improvements - 19% average improvement in accuracy across 6 language pairs over the state of the art in the supervised setting and 16% in the weakly supervised setting. As another contribution, we highlight issues associated with modern BLI that stem from ignoring inflectional morphology, and propose three suggestions for improving the task.Comment: COLING 202