We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a wide variety of r...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
We consider the problem of learning mixtures of arbitrary symmetric distributions. We formulate sufficient separation conditions and present a learning algorithm with provable gua...
Anirban Dasgupta, John E. Hopcroft, Jon M. Kleinbe...
Abstract. The Focal language (formerly FoC) allows one to incrementally build modules and to formally prove their correctness. In this paper, we present two formal semantics for en...
An additive spanner of an unweighted undirected graph G with distortion d is a subgraph H such that for any two vertices u, v ∈ G, we have δH(u, v) ≤ δG(u, v) + d. For every...
We give a new model of learning motivated by smoothed analysis (Spielman and Teng, 2001). In this model, we analyze two new algorithms, for PAC-learning DNFs and agnostically learn...
Adam Tauman Kalai, Alex Samorodnitsky, Shang-Hua T...