A Constant-Factor Approximation for Directed Latency in Quasi-Polynomial Time

no code implementations • 12 Dec 2019 • Zachary Friggstad, Chaitanya Swamy

We also give a better approximation guarantee in the special case of Directed Latency in regret metrics where the goal is to find a path $P$ minimize the average time a node $v$ waits in excess of $c_{rv}$, i. e. $\frac{1}{|V|} \cdot \sum_{v \in V} (c_v(P)-c_{rv})$.

Data Structures and Algorithms

Learning Arbitrary Statistical Mixtures of Discrete Distributions

no code implementations • 10 Apr 2015 • Jian Li, Yuval Rabani, Leonard J. Schulman, Chaitanya Swamy

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets.

Collaborative Filtering Topic Models

Learning Mixtures of Arbitrary Distributions over Large Discrete Domains

no code implementations • 7 Dec 2012 • Yuval Rabani, Leonard Schulman, Chaitanya Swamy

(In the topic-model learning setting, the mixture constituents correspond to the topic distributions.)

2k Dimensionality Reduction +1