5 citations · 5 across the 7 of their papers we have counts for
8 papers
Provably Optimal Learning Algorithms for Assistance Games
Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan +2
This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over timesteps to optimize a common…
Signaling in Data Markets via Free Samples
Nivasini Ananthakrishnan, Alireza Fallah, Michael I. Jordan
We study a setting in which a data buyer seeks to estimate an unknown parameter by purchasing samples from one of K data sellers. Each seller has privately known data quality (e.g.…
Power and Limitations of Aggregation in Compound AI Systems
Nivasini Ananthakrishnan, Meena Jagadeesan
When designing compound AI systems, a common approach is to query multiple copies of the same model and aggregate the responses to produce a synthesized output. Given the homogenei…
Learning Local Stackelberg Equilibria from Repeated Interactions with a Learning Agent
Nivasini Ananthakrishnan, Yuval Dagan, Kunhe Yang
Motivated by the question of how a principal can maximize its utility in repeated interactions with a learning agent, we study repeated games between an principal and an agent empl…
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata +1
When learning in strategic environments, a key question is whether agents can overcome uncertainty about their preferences to achieve outcomes they could have achieved absent any u…
Privacy Can Arise Endogenously in an Economic System with Learning Agents
Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner +2
We study price-discrimination games between buyers and a seller where privacy arises endogenously--that is, utility maximization yields equilibrium strategies where privacy occurs…
Delegating Data Collection in Decentralized Machine Learning
Nivasini Ananthakrishnan, Stephen Bates, Michael I. Jordan +1
Motivated by the emergence of decentralized machine learning (ML) ecosystems, we study the delegation of data collection. Taking the field of contract theory as our starting point,…
Impossibility results for fair representations
Tosca Lechner, Shai Ben-David, Sushant Agarwal +1
With the growing awareness to fairness in machine learning and the realization of the central role that data representation has in data processing tasks, there is an obvious intere…