Integrality of Linearizations of Polynomials over Binary Variables using Additional Monomials. (arXiv:1911.06894v1 [cs.DM])

Polynomial optimization problems over binary variables can be expressed as integer programs using a linearization with extra monomials in addition to those arising in the given polynomial. We characterize when such a linearization yields an integral relaxation polytope, generalizing work by Del Pia and Khajavirad (SIAM Journal on Optimization, 2018). We also present an algorithm…

Delta-stepping SSSP: from Vertices and Edges to GraphBLAS Implementations. (arXiv:1911.06895v1 [cs.DS])

GraphBLAS is an interface for implementing graph algorithms. Algorithms implemented using the GraphBLAS interface are cast in terms of linear algebra-like operations. However, many graph algorithms are canonically described in terms of operations on vertices and/or edges. Despite the known duality between these two representations, the differences in the way algorithms are described using the…

Functional Sequential Treatment Allocation. (arXiv:1812.09408v5 [econ.EM] UPDATED)

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when…

The Impact of Renewable Energy Forecasts on Intraday Electricity Prices. (arXiv:1903.09641v2 [econ.GN] UPDATED)

In this paper we study the impact of errors in wind and solar power forecasts on intraday electricity prices. We develop a novel econometric model which is based on day-ahead wholesale auction curves data and errors in wind and solar power forecasts. The model shifts day-ahead supply curves to calculate intraday prices. We apply our…

Solving the Reswitching Paradox in the Sraffian Theory of Capital. (arXiv:1907.01189v4 [econ.TH] UPDATED)

The possibility of re-switching of techniques in Piero Sraffa’s intersectoral model, namely the returning capital-intensive techniques with monotonic changes in the profit rate, is traditionally considered as a paradox putting at stake the viability of the neoclassical theory of production. It is argued here that this phenomenon can be rationalized within the neoclassical paradigm. Sectoral…

Analysing Global Fixed Income Markets with Tensors. (arXiv:1908.02101v3 [q-fin.PM] UPDATED)

Global fixed income returns span across multiple maturities and economies, that is, they naturally reside on multi-dimensional data structures referred to as tensors. In contrast to standard “flat-view” multivariate models that are agnostic to data structure and only describe linear pairwise relationships, we introduce a tensor-valued approach to model the global risks shared by multiple…

Time-consistent decisions and rational expectation equilibrium existence in DSGE models. (arXiv:1909.10915v3 [econ.TH] UPDATED)

We demonstrate that if all agents in an economy make time-consistent decisions and policies, then there exists no rational expectation equilibrium in a dynamic stochastic general equilibrium (DSGE) model, unless under very restrictive and special circumstances. Some time-consistent interest rate rules, such as Taylor rule, worsen the equilibrium non-existence issue in general circumstances. Monetary policy…

Identification and inference in discrete choice models with imperfect information. (arXiv:1911.04529v2 [econ.EM] UPDATED)

In this paper we study identification and inference of preference parameters in a single-agent, static, discrete choice model where the decision maker may face attentional limits precluding her to exhaustively process information about the payoffs of the available alternatives. By leveraging on the notion of one-player Bayesian Correlated Equilibrium in Bergemann and Morris (2016), we…

Predicting Drug-Drug Interactions from Molecular Structure Images. (arXiv:1911.06356v1 [cs.LG])

Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view. Almost all of the machine learning approaches have focused on text data or textual representation of the structural data of drugs. We present the first work that uses drug structure images…

Solving Inverse Problems by Joint Posterior Maximization with a VAE Prior. (arXiv:1911.06379v1 [stat.ML])

In this paper we address the problem of solving ill-posed inverse problems in imaging where the prior is a neural generative model. Specifically we consider the decoupled case where the prior is trained once and can be reused for many different log-concave degradation models without retraining. Whereas previous MAP-based approaches to this problem lead to…