Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among ...
Kedar Dhamdhere, Vineet Goyal, R. Ravi, Mohit Sing...
At the core of the seminal Graph Minor Theory of Robertson and Seymour is a powerful structural theorem capturing the structure of graphs excluding a fixed minor. This result is ...
Erik D. Demaine, Mohammad Taghi Hajiaghayi, Ken-ic...
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...
We initiate the study of two-party cryptographic primitives with unconditional security, assuming that the adversary’s quantum memory is of bounded size. We show that oblivious ...
We further develop the group-theoretic approach to fast matrix multiplication introduced by Cohn and Umans, and for the first time use it to derive algorithms asymptotically fast...
We establish a new connection between the two most common traditions in the theory of real computation, the Blum-Shub-Smale model and the Computable Analysis approach. We then use...
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...
This paper studies the computational complexity of the following type of quadratic programs: given an arbitrary matrix whose diagonal elements are zero, find x ∈ {−1, +1}n th...
Sanjeev Arora, Eli Berger, Elad Hazan, Guy Kindler...
d Abstract] Mikhail Alekhnovich ∗ Subhash A. Khot † Guy Kindler ‡ Nisheeth K. Vishnoi § We show that, unless NP⊆DTIME(2poly log(n) ), the closest vector problem with pre-...
Mikhail Alekhnovich, Subhash Khot, Guy Kindler, Ni...