Browsing by Author "Silva, J"

Now showing 1 - 3 of 3

Open Access
Hypergraph-based anomaly detection in very large networks
(IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009) Silva, J; Willett, R
Open Access
Psychology, behavioral economics, and public policy
(Marketing Letters, 2005-12-01) Amir, O; Ariely, D; Cooke, A; Dunning, D; Epley, N; Gneezy, U; Koszegi, B; Lichtenstein, D; Mazar, N; Mullainathan, S; Prelec, D; Shafir, E; Silva, J
Economics has typically been the social science of choice to inform public policy and policymakers. In the current paper we contemplate the role behavioral science can play in enlightening policymakers. In particular, we provide some examples of research that has and can be used to inform policy, reflect on the kind of behavioral science that is important for policy, and approaches for convincing policy-makers to listen to behavioral scientists. We suggest that policymakers are unlikely to invest the time translating behavioral research into its policy implications, and researchers interested in influencing public policy must therefore invest substantial effort, and direct that effort differently than in standard research practices. © 2005 Springer Science + Business Media, Inc.
Open Access
Sequential anomaly detection in the presence of noise and limited feedback
(IEEE Transactions on Information Theory, 2012-07-23) Raginsky, M; Willett, RM; Horn, C; Silva, J; Marcia, RF
This paper describes a methodology for detecting anomalies from sequentially observed and potentially noisy data. The proposed approach consists of two main elements: 1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations and 2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on the available feedback from an end user. Our algorithms, which combine universal prediction with recent work on online convex programming, do not require computing posterior distributions given all current observations and involve simple primal-dual parameter updates. At the heart of the proposed approach lie exponential-family models which can be used in a wide variety of contexts and applications, and which yield methods that achieve sublinear per-round regret against both static and slowly varying product distributions with marginals drawn from the same exponential family. Moreover, the regret against static distributions coincides with the minimax value of the corresponding online strongly convex game. We also prove bounds on the number of mistakes made during the hedging step relative to the best offline choice of the threshold with access to all estimated beliefs and feedback signals. We validate the theory on synthetic data drawn from a time-varying distribution over binary vectors of high dimensionality, as well as on the Enron email dataset. © 1963-2012 IEEE.