Combining Outcome-Based and Preference-Based Matching: The g-Constrained Priority Mechanism. (arXiv:1902.07355v2 [econ.GN] UPDATED)

We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold…

Dynamics of Value-Tracking in Financial Markets. (arXiv:1903.09898v2 [q-fin.TR] UPDATED)

The efficiency of a modern economy depends on what we call the Value-Tracking Hypothesis: that market prices of key assets broadly track some underlying value. This can be expected if a sufficient weight of market participants are valuation-based traders, buying and selling an asset when its price is, respectively, below and above their well-informed private…

Towards more effective consumer steering via network analysis. (arXiv:1903.11469v2 [cs.SI] UPDATED)

Increased data gathering capacity, together with the spread of data analytics techniques, has prompted an unprecedented concentration of information related to the individuals’ preferences in the hands of a few gatekeepers. In the present paper, we show how platforms’ performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance…

A simulation of the insurance industry: The problem of risk model homogeneity. (arXiv:1907.05954v2 [econ.GN] UPDATED)

We develop an agent-based simulation of the catastrophe insurance and reinsurance industry and use it to study the problem of risk model homogeneity. The model simulates the balance sheets of insurance firms, who collect premiums from clients in return for ensuring them against intermittent, heavy-tailed risks. Firms manage their capital and pay dividends to their…

The value of power-related options under spectrally negative L\’evy processes. (arXiv:1910.07971v2 [q-fin.PR] UPDATED)

We provide analytical tools for pricing power options with exotic features (capped or log payoffs, gap options …) in the framework of exponential L\’evy models driven by one-sided stable or tempered stable processes. Pricing formulas take the form of fast converging series of powers of the log-forward moneyness and of the time-to-maturity; these series are…

Six Degree-of-Freedom Hovering using LIDAR Altimetry via Reinforcement Meta-Learning. (arXiv:1911.08553v1 [eess.SY])

We optimize a six degrees of freedom hovering policy using reinforcement meta-learning. The policy maps flash LIDAR measurements directly to on/off spacecraft body-frame thrust commands, allowing hovering at a fixed position and attitude in the asteroid body-fixed reference frame. Importantly, the policy does not require position and velocity estimates, and can operate in environments with…

Classification as Decoder: Trading Flexibility for Control in Medical Dialogue. (arXiv:1911.08554v1 [cs.AI])

Generative seq2seq dialogue systems are trained to predict the next word in dialogues that have already occurred. They can learn from large unlabeled conversation datasets, build a deeper understanding of conversational context, and generate a wide variety of responses. This flexibility comes at the cost of control, a concerning tradeoff in doctor/patient interactions. Inaccuracies, typos,…

Exploiting Oxide Based Resistive RAM Variability for Probabilistic AI Hardware Design. (arXiv:1911.08555v1 [cs.ET])

Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which do not overtly represent any uncertainty in its structure or output, Bayesian deep networks are being currently investigated where the network is envisaged as a set of plausible models learnt by the Bayes’ formulation in response to…

Towards Reducing Bias in Gender Classification. (arXiv:1911.08556v1 [cs.LG])

Societal bias towards certain communities is a big problem that affects a lot of machine learning systems. This work aims at addressing the racial bias present in many modern gender recognition systems. We learn race invariant representations of human faces with an adversarially trained autoencoder model. We show that such representations help us achieve less…