Person:

Narasimhan, Harikrishna

We use statistical machine learning to develop methods for automatically designing mechanisms in domains without money. Our goal is to find a mechanism that best approximates a given target function subject to a design constraint such as strategy-proofness or stability. The proposed approach involves identifying a rich parametrized class of mechanisms that resemble discriminant-based multiclass classifiers, and relaxing the resulting search problem into an SVM-style surrogate optimization problem. We use this methodology to design strategy-proof mechanisms for social choice problems with single-peaked preferences, and stable mechanisms for two-sided matching problems. To the best of our knowledge, ours is the first automated approach for designing stable matching rules. Experiments on synthetic and real-world data confirm the usefulness of our methods.

We develop a statistical framework for the design of a strategy-proof assignment mechanism that closely approximates a target outcome rule. The framework can handle settings with and without money, and allows the designer to employ techniques from machine learning to control the space of strategy-proof mechanisms searched over, by providing a rule class with appropriate capacity. We solve a sample-based optimization problem over a space of mechanisms that correspond to agent-independent price functions (virtual prices in the case of settings without money), subject to a feasibility constraint on the sample. A transformation is applied to the obtained mechanism to ensure feasibility on all type profiles, and strategy-proofness. We derive a sample complexity bound for our approach in terms of the capacity of the chosen rule class and provide applications for our results.

We establish PAC learnability of influence functions for three common influence models, namely, the Linear Threshold (LT), Independent Cascade (IC) and Voter models, and present concrete sample complexity results in each case. Our results for the LT model are based on interesting connections with neural networks; those for the IC model are based an interpretation of the influence function as an expectation over random draw of a subgraph and use covering number arguments; and those for the Voter model are based on a reduction to linear regression. We show these results for the case in which the cascades are only partially observed and we do not see the time steps in which a node has been influenced. We also provide efficient polynomial time learning algorithms for a setting with full observation, i.e. where the cascades also contain the time steps in which nodes are influenced.

Designing an auction that maximizes expected revenue is an intricate task. Despite major efforts, only the single-item case is fully understood. We explore the use of tools from deep learning on this topic. The design objective is revenue optimal, dominant-strategy incentive compatible auctions. For a baseline, we show that multi-layer neural networks can learn almost-optimal auctions for a variety of settings for which there are analytical solutions, and even without encoding characterization results into the design of the network. Our research also demonstrates the potential that deep nets have for deriving auctions with high revenue for poorly understood problems.