Learning Contextual Bandits in a Non-stationary Environment

Multi-armed bandit algorithms have become a reference solution for handling the explore/exploit dilemma in recommender systems, and many other important real-world problems, such as display advertisement. However, such algorithms usually assume a stationary reward distribution, which hardly holds in practice as users' preferences are dynamic. This inevitably costs a recommender system consistent suboptimal performance. In this paper, we consider the situation where the underlying distribution of reward remains unchanged over (possibly short) epochs and shifts at unknown time instants. In accordance, we propose a contextual bandit algorithm that detects possible changes of environment based on its reward estimation confidence and updates its arm selection strategy respectively. Rigorous upper regret bound analysis of the proposed algorithm demonstrates its learning effectiveness in such a non-trivial environment. Extensive empirical evaluations on both synthetic and real-world datasets for recommendation confirm its practical utility in a changing environment.Comment: 10 pages, 13 figures, To appear on ACM Special Interest Group on Information Retrieval (SIGIR) 201

Factors Influencing Household Solar Adoption in Santiago, Chile

In Santiago, Chile, the market conditions are seemingly excellent for the household adoption of photovoltaic (PV) technology, yet the uptake is negligible. To explore this paradox, the authors conducted a Delphi study to solicit the knowledge of a panel of Chilean PV experts. These efforts yielded 26 factors—both motivations and barriers—impacting the diffusion of PV in Santiago. Of the 26, experts were in consensus on the relative importance of 21. The literature suggests that diffusion of PV technologies is influenced by complex technical, economic, and social factors. Similarly, the experts saw influence from financial, environmental, and energy supply (e.g., electrical reliability) factors. They saw emergent barriers to adoption as being financial, technical, institutional, and knowledge factors. They considered the most important factors influencing adoption to be financial motivations (e.g., subsidies) and financial barriers (e.g., high upfront costs); they considered the least important factors to be environmental motivations (e.g., environmental stewardship) and technical barriers (e.g., concerns with roof mounting). With this knowledge, the authors develop an adoption framework for household PV that describes the interaction among the identified motivations and barriers. This framework informs policy recommendations for Santiago, Chile, and contributes to the body of literature exploring the interconnected systems of factors that influence civil infrastructure in general and PV adoption in particular

Diversity-induced resonance

We present conclusive evidence showing that different sources of diversity, such as those represented by quenched disorder or noise, can induce a resonant collective behavior in an ensemble of coupled bistable or excitable systems. Our analytical and numerical results show that when such systems are subjected to an external subthreshold signal, their response is optimized for an intermediate value of the diversity. These findings show that intrinsic diversity might have a constructive role and suggest that natural systems might profit from their diversity in order to optimize the response to an external stimulus.Comment: 4 pages, 3 figure

Algebraic characterization of anomalies in chiral WW_{3} gravity

The anomalies which occur in chiral WW_{3} gravity are characterized by solving the BRS consistency condition.Comment: 25 pages, report CBPF-NF-042/9

Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations. Our proposed family of measures corresponds to the long-established formulations of cardinal social welfare in economics, and is justified by the Rawlsian conception of fairness behind a veil of ignorance. The convex formulation of our welfare-based measures of fairness allows us to integrate them as a constraint into any convex loss minimization pipeline. Our empirical analysis reveals interesting trade-offs between our proposal and (a) prediction accuracy, (b) group discrimination, and (c) Dwork et al.'s notion of individual fairness. Furthermore and perhaps most importantly, our work provides both heuristic justification and empirical evidence suggesting that a lower-bound on our measures often leads to bounded inequality in algorithmic outcomes; hence presenting the first computationally feasible mechanism for bounding individual-level inequality.Comment: Conference: Thirty-second Conference on Neural Information Processing Systems (NIPS 2018

Magnetic translation algebra with or without magnetic field in the continuum or on arbitrary Bravais lattices in any dimension

The magnetic translation algebra plays an important role in the quantum Hall effect. Murthy and Shankar, arXiv:1207.2133, have shown how to realize this algebra using fermionic bilinears defined on a two-dimensional square lattice. We show that, in any dimension $d$, it is always possible to close the magnetic translation algebra using fermionic bilinears, whether in the continuum or on the lattice. We also show that these generators are complete in even, but not odd, dimensions, in the sense that any fermionic Hamiltonian in even dimensions that conserves particle number can be represented in terms of the generators of this algebra, whether or not time-reversal symmetry is broken. As an example, we reproduce the $f$-sum rule of interacting electrons at vanishing magnetic field using this representation. We also show that interactions can significantly change the bare bandwidth of lattice Hamiltonians when represented in terms of the generators of the magnetic translation algebra.Comment: 14 page