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We study the problem of modeling species geographic distributions, a critical problem in conservation biology. We propose the use of maximum-entropy techniques for this problem, specifically, sequential-update algorithms that can handle a very large number of features. We describe experiments comparing maxent with a standard distribution-modeling tool,… (More)

- Alekh Agarwal, Olivier Chapelle, Miroslav Dudík, John Langford
- Journal of Machine Learning Research
- 2014

We present a system and a set of techniques for learning linear predictors with convex losses on terascale datasets, with trillions of features, billions of training examples and millions of parameters in an hour using a cluster of 1000 machines. Individually none of the component techniques is new, but the careful synthesis required to obtain an efficient… (More)

- Steven J. Phillips, Miroslav Dudík, +4 authors Simon Ferrier
- Ecological applications : a publication of the…
- 2009

Most methods for modeling species distributions from occurrence records require additional data representing the range of environmental conditions in the modeled region. These data, called background or pseudo-absence data, are usually drawn at random from the entire region, whereas occurrence collection is often spatially biased toward easily accessed… (More)

We consider the problem of estimating an unknown probability distribution from samples using the principle of maximum entropy (maxent). To alleviate overfitting with a very large number of features, we propose applying the maxent principle with relaxed constraints on the expectations of the features. By convex duality, this turns out to be equivalent to… (More)

- Miroslav Dudík, Zaïd Harchaoui, Jérôme Malick
- AISTATS
- 2012

We consider the minimization of a smooth loss with trace-norm regularization, which is a natural objective in multi-class and multitask learning. Even though the problem is convex, existing approaches rely on optimizing a non-convex variational bound, which is not guaranteed to converge, or repeatedly perform singular-value decomposition, which prevents… (More)

- Miroslav Dudík, John Langford, Lihong Li
- ICML
- 2011

We study decision making in environments where the reward is only partially observed, but can be modeled as a function of an action and an observed context. This setting, known as contextual bandits, encompasses a wide variety of applications including health-care policy and Internet advertising. A central task is evaluation of a new policy given historic… (More)

- Miroslav Dudík, Steven J. Phillips, Robert E. Schapire
- Journal of Machine Learning Research
- 2007

We present a unified and complete account of maximum entropy density estimation subject to constraints represented by convex potential functions or, alternatively, by convex regularization. We provide fully general performance guarantees and an algorithm with a complete convergence proof. As special cases, we easily derive performance guarantees for many… (More)

- Miroslav Dudík, Daniel J. Hsu, +4 authors Tong Zhang
- UAI
- 2011

We address the problem of learning in an online setting where the learner repeatedly observes features, selects among a set of actions, and receives reward for the action taken. We provide the first efficient algorithm with an optimal regret. Our algorithm uses a cost sensitive classification learner as an oracle and has a running time polylog(N), where N… (More)

Recent work has shown that probabilistic models based on pairwise interactions—in the simplest case, the Ising model—provide surprisingly accurate descriptions of experiments on real biological networks ranging from neurons to genes. Finding these models requires us to solve an inverse problem: given experimentally measured expectation values, what are the… (More)

- Zaïd Harchaoui, Matthijs Douze, Mattis Paulin, Miroslav Dudík, Jérôme Malick
- 2012 IEEE Conference on Computer Vision and…
- 2012

With the advent of larger image classification datasets such as ImageNet, designing scalable and efficient multi-class classification algorithms is now an important challenge. We introduce a new scalable learning algorithm for large-scale multi-class image classification, based on the multinomial logistic loss and the trace-norm regularization penalty.… (More)