11 citations · 23 across the 5 of their papers we have counts for
11 papers
One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
Avrim Blum, Nika Haghtalab, Richard Lanas Phillips +1
In recent years, federated learning has been embraced as an approach for bringing about collaboration across large populations of learning agents. However, little is known about ho…
Smoothed Analysis with Adaptive Adversaries
Nika Haghtalab, Tim Roughgarden, Abhishek Shetty
We prove novel algorithmic guarantees for several online problems in the smoothed analysis model. In this model, at each time an adversary chooses an input distribution with densit…
Noise in Classification
Maria-Florina Balcan, Nika Haghtalab
This chapter considers the computational and statistical aspects of learning linear thresholds in presence of noise. When there is no noise, several algorithms exist that efficient…
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
Nika Haghtalab, Nicole Immorlica, Brendan Lucier +1
Motivated by applications such as college admission and insurance rate determination, we propose an evaluation problem where the inputs are controlled by strategic individuals who…
Smoothed Analysis of Online and Differentially Private Learning
Nika Haghtalab, Tim Roughgarden, Abhishek Shetty
Practical and pervasive needs for robustness and privacy in algorithms have inspired the design of online adversarial and differentially private learning algorithms. The primary qu…
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
Lydia T. Liu, Ashia Wilson, Nika Haghtalab +3
The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individu…