Algorithms
Approximating the Nash Social Welfare with Indivisible Items
We study the problem of allocating a set of indivisible items among agents with additive valuations with the goal of maximizing the geometric mean of the agents’ valuations, i.e., the Nash social welfare. This problem…
2nd-order Optimization for Neural Network Training
Neural networks have become the main workhorse of supervised learning, and their efficient training is an important technical challenge which has received a lot of attention. While stochastic gradient descent (SGD) with momentum works well…
Maximal Sparsity with Deep Networks?
Lab Tutorial: Multi-Objective Decision Making
Many real-world problems require making decisions that involve multiple possibly conflicting objectives. To succeed in such tasks, intelligent agents need algorithms that can efficiently find different ways of balancing the trade-offs that such objectives present.…
Doubly Stochastic Primal-Dual Coordinate Method for Empirical Risk Minimization
We proposed a doubly stochastic primal-dual coordinate optimization algorithm for regularized empirical risk minimization that can be formulated as a saddle-point problem using convex conjugate functions. Different from the existing coordinate methods, the proposed method…
