71 citations · 130 across the 47 of their papers we have counts for
5 papers · 1 filter
On-Demand Sampling: Learning Optimally from Multiple Distributions
Nika Haghtalab, Michael I. Jordan, Eric Zhao
Social and real-world considerations such as robustness, fairness, social welfare and multi-agent tradeoffs have given rise to multi-distribution learning paradigms, such as collab…
Competition, Alignment, and Equilibria in Digital Marketplaces
Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in da…
Learning in Stackelberg Games with Non-myopic Agents
Nika Haghtalab, Thodoris Lykouris, Sloan Nietert +1
We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent, without knowing the agent's payoff function. Although learning in Stackelberg…
Communicating with Anecdotes
Nika Haghtalab, Nicole Immorlica, Brendan Lucier +2
We study a communication game between a sender and a receiver. The sender chooses one of her signals about the state of the world (i.e., anecdotes) and communicates to the receiver…
Oracle-Efficient Online Learning for Beyond Worst-Case Adversaries
Nika Haghtalab, Yanjun Han, Abhishek Shetty +1
In this paper, we study oracle-efficient algorithms for beyond worst-case analysis of online learning. We focus on two settings. First, the smoothed analysis setting of [RST11,HRS2…