We study the complexity of optimizing nonsmooth nonconvex Lipschitz func...
We study the problem of covering and learning sums X = X_1 + ⋯ + X_n
of ...
Min-max optimization problems involving nonconvex-nonconcave objectives ...
In this paper, we present several new results on minimizing a nonsmooth ...
Truncated linear regression is a classical challenge in Statistics, wher...
Introduced by the celebrated works of Debreu and Rosen in the 1950s and ...
In the classical setting of self-selection, the goal is to learn k model...
We provide efficient estimation methods for first- and second-price auct...
We consider the problem of computing an equilibrium in a class of nonlin...
Several widely-used first-order saddle point optimization methods yield ...
We study the problem of learning revenue-optimal multi-bidder auctions f...
We show a statistical version of Taylor's theorem and apply this result ...
We provide a computationally and statistically efficient estimator for t...
A soft-max function has two main efficiency measures: (1) approximation ...
Despite its important applications in Machine Learning, min-max optimiza...
As in standard linear regression, in truncated linear regression, we are...
We analyze the finite sample mean squared error (MSE) performance of
reg...
Generative neural networks have been empirically found very promising in...
The classes PPA-p have attracted attention lately, because they are the
...
In the ε-Consensus-Halving problem, a fundamental problem in fair
divisi...
In the ε-Consensus-Halving problem, a fundamental problem in fair
divisi...
We study the search problem class PPA_q defined as a modulo-q
analog of ...
We study the problem of estimating the parameters of a Gaussian distribu...
The Mallows model, introduced in the seminal paper of Mallows 1957, is o...
We provide an efficient algorithm for the classical problem, going back ...
Polynomial Pigeonhole Principle (PPP) is an important subclass of TFNP w...
A wide range of learning tasks require human input in labeling massive d...
Banach's fixed point theorem for contraction maps has been widely used t...
The Expectation-Maximization (EM) algorithm is a widely used method for
...