7k citations
- University of California, Santa BarbaraUS78 papers
- ETH ZurichCH23 papers
- University of California, BerkeleyUS23 papers
- University of Maryland, College ParkUS22 papers
- California Institute of TechnologyUS21 papers
- Microsoft Research (United Kingdom)GB16 papers
- Princeton UniversityUS16 papers
- University of California, Los AngelesUS15 papers
- Stanford UniversityUS13 papers
- Board of the Swiss Federal Institutes of TechnologyCH11 papers
- Microsoft Research New England (United States)US9 papers
- University of California, RiversideUS9 papers
84 papers · 1 filter
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
Ohad Shamir, Tong Zhang
Stochastic Gradient Descent (SGD) is one of the simplest and most popular stochastic optimization methods. While it has already been theoretically studied for decades, the classica…
Envy Freedom and Prior-free Mechanism Design
Nikhil R. Devanur, Jason D. Hartline, Qiqi Yan
We consider the provision of an abstract service to single-dimensional agents. Our model includes position auctions, single-minded combinatorial auctions, and constrained matching…
Staged Mixture Modelling and Boosting
Christopher Meek, Bo Thiesson, David Heckerman
In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic approach is to seque…
Reduction of Maximum Entropy Models to Hidden Markov Models
Joshua Goodman
We show that maximum entropy (maxent) models can be modeled with certain kinds of HMMs, allowing us to construct maxent models with hidden variables, hidden state sequences, or oth…
Factorization of Discrete Probability Distributions
Dan Geiger, Christopher Meek, Bernd Sturmfels
We formulate necessary and sufficient conditions for an arbitrary discrete probability distribution to factor according to an undirected graphical model, or a log-linear model, or…
Finding Optimal Bayesian Networks
David Maxwell Chickering, Christopher Meek
In this paper, we derive optimality results for greedy Bayesian-network search algorithms that perform single-edge modifications at each step and use asymptotically consistent scor…