Abhimanyu Das

Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising

Many estimation tasks come in groups and hierarchies of related problems. In this paper we propose a hierarchical model and a scalable algorithm to perform inference for multitask learning. It infers task correlation and subtask structure in a joint sparse setting. Implementation is achieved by a distributed subgradient oracle and the successive application of prox-operators pertaining to groups and subgroups of variables. We apply this algorithm to conversion optimization in display advertising. Experimental results on over 1TB data for up to 1 billion observations and 1 million attributes show that the algorithm provides significantly better prediction accuracy while simultaneously beingefficiently scalable by distributed parameter synchronization.

Approximate Modularity

A set function on a ground set of size n is approximately modular if it satisfies every modularity requirement to within an additive error, approximate modularity is the set analog of approximate linearity. In this paper we study how close, in additive error, can approximately modular functions be to truly modular functions. We first obtain a polynomial time algorithm that makes O(n2 log n) queries to any approximately modular function to reconstruct a modular function that is O(√n)-close. We also show an almost matching lower bound: any algorithm world need super polynomially many queries to construct a modular function that is o(√(n/log n))-close. In a striking contrast to these near-tight computational reconstruction bounds, we then show that for any approximately modular function, there exists a modular function that is O(log n)-close.

Ranking Twitter discussion groups

A discussion group is a repeated, synchronized conversation organized around a specific topic. Groups are extremely valuable to the attendees, creating a sense of community among like-minded users. While groups may involve many users, there are many outside the group that would benefit from participation. However, finding the right group is not easy given their quantity and given topic overlap. We study the following problem: given a search query, find a good ranking of discussion groups. We describe a random walk model for how users select groups: starting with a group relevant to the query, a hypothetical user repeatedly selects an authoritative user in the group and then moves to a group according to what the authoritative user prefers. The stationary distribution of this walk yields a group ranking. We analyze this random walk model, demonstrating that it enjoys many natural properties of a desirable ranking algorithm. We study groups on Twitter where conversations can be organized via pre-designated hashtags. These groups are an emerging phenomenon and there are at least tens of thousands in existence today according to our calculations. Via an extensive collection of experiments on one year of tweets, we show that our model effectively ranks groups, outperforming several baseline solutions.

Minimizing Latency in Online Ride and Delivery Services

Motivated by the popularity of online ride and delivery services, we study natural variants of classical multi-vehicle minimum latency problems where the objective is to route a set of vehicles located at depots to serve requests located on a metric space so as to minimize the total latency. In this paper, we consider point-to-point requests that come with source-destination pairs and release-time constraints that restrict when each request can be served. The point-to-point requests and release-time constraints model taxi rides and deliveries. For all the variants considered, we show constant-factor approximation algorithms based on a linear programming framework. To the best of our knowledge, these are the first set of results for the aforementioned variants of the minimum latency problems. Furthermore, we provide an empirical study of heuristics based on our theoretical algorithms on a real data set of taxi rides.