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- Wei Chen, Alex Collins, +7 authors Yifei Yuan
- SDM
- 2011

Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003), is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. In this paper, we propose an extension to the independent cascade model that incorporates the emergence and propagation of negative… (More)

The traditional notion of generalization—i.e., learning a hypothesis whose empirical error is close to its true error—is surprisingly brittle. As has recently been noted [DFH+15b], even if several algorithms have this guarantee in isolation, the guarantee need not hold if the algorithms are composed adaptively. In this paper, we study three notions of… (More)

- Ho-Lin Chen, Rachel Cummings, David Doty, David Soloveichik
- Distributed Computing
- 2014

Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. Assuming a fixed molecular population size and bimolecular reactions, CRNs are formally equivalent to population protocols, a model of distributed computing introduced by Angluin, Aspnes, Diamadi, Fischer, and Peralta (PODC 2004). The challenge of fast computation by CRNs… (More)

- Rachel Cummings, David Doty, David Soloveichik
- Natural Computing
- 2014

The computational power of stochastic chemical reaction networks (CRNs) varies significantly with the output convention and whether or not error is permitted. Focusing on probability 1 computation, we demonstrate a striking difference between stable computation that converges to a state where the output cannot change, and the notion of limit-stable… (More)

- Kareem Amin, Rachel Cummings, Lili Dworkin, Michael Kearns, Aaron Roth
- AAAI
- 2015

We consider the problem of learning from revealed preferences in an online setting. In our framework, each period a consumer buys an optimal bundle of goods from a merchant according to her (linear) utility function and current prices, subject to a budget constraint. The merchant observes only the purchased goods, and seeks to adapt prices to optimize his… (More)

- Rachel Cummings, Stratis Ioannidis, Katrina Ligett
- COLT
- 2015

We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to report their data to the analyst truthfully constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differentially privacy to model individuals’ privacy losses. This… (More)

We study how privacy technologies affect user and advertiser behavior in a simple economic model of targeted advertising. In our model, a consumer first decides whether or not to buy a good, and then an advertiser chooses an advertisement to show the consumer. The consumer's value for the good is correlated with her type, which determines which ad the… (More)

We consider the problem of a data analyst who may purchase an unbiased estimate of some statistic from multiple data providers. From each provider i, the analyst has a choice: she may purchase an estimate from that provider that has variance chosen from a finite menu of options. Each level of variance has a cost associated with it, reported (possibly… (More)

- Rachel Cummings, Michael Kearns, Aaron Roth, Zhiwei Steven Wu
- WINE
- 2015

We study a very general class of games — multi-dimensional aggregative games — which in particular generalize both anonymous games and weighted congestion games. For any such game that is also large (meaning that the influence that any single player’s action has on the utility of others is diminishing with the number of players in the game), we solve the… (More)

This paper initiates the study of the testable implications of choice data in settings where agents have privacy preferences. We adapt the standard conceptualization of consumer choice theory to a situation where the consumer is aware of, and has preferences over, the information revealed by her choices. The main message of the paper is that little can be… (More)