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Energy and sustainability issues raise a large number of problems that can be tackled using approaches from data mining and machine learning, but traction of such problems has been slow due to the lack of publicly available data. In this paper we present the Reference Energy Disaggregation Data Set (REDD), a freely available data set containing detailed… (More)

- J. Zico Kolter, Marcus A. Maloof
- Journal of Machine Learning Research
- 2007

We present an ensemble method for concept drift that dynamically creates and removes weighted experts in response to changes in performance. The method, dynamic weighted majority (DWM), uses four mechanisms to cope with concept drift: It trains online learners of the ensemble, it weights those learners based on their performance, it removes them, also based… (More)

- J. Zico Kolter, Tommi S. Jaakkola
- AISTATS
- 2012

This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function of all the hidden states. Although such models are very powerful, accurate inference is unfortunately difficult: exact inference is not computationally tractable, and existing… (More)

- J. Zico Kolter, Marcus A. Maloof
- ICDM
- 2003

Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any on-line learner for concept drift. Dynamic Weighted Majority (dwm) maintains an ensemble of base learners, predicts using a weighted-majority vote of these “experts”, and dynamically creates and… (More)

- J. Zico Kolter, Marcus A. Maloof
- Journal of Machine Learning Research
- 2006

We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the most… (More)

- J. Zico Kolter, Marcus A. Maloof
- KDD
- 2004

In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and encoded each as a training example using n-grams of byte codes as features. Such processing resulted in more than 255 million distinct n-grams. After selecting the most relevant… (More)

- J. Zico Kolter, Andrew Y. Ng
- ICML
- 2009

We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (LSTD) algorithm, provide a method for learning the parameters of the value function, but when the number of features is large this algorithm can over-fit to the data and is… (More)

- J. Zico Kolter, Andrew Y. Ng
- ICML
- 2009

We consider the exploration/exploitation problem in reinforcement learning (RL). The Bayesian approach to model-based RL offers an elegant solution to this problem, by considering a distribution over possible models and acting to maximize expected reward; unfortunately, the Bayesian solution is intractable for all but very restricted cases. In this paper we… (More)

- J. Zico Kolter, Marcus A. Maloof
- ICML
- 2005

We consider online learning where the target concept can change over time. Previous work on expert prediction algorithms has bounded the worst-case performance on any subsequence of the training data relative to the performance of the best expert. However, because these "experts" may be difficult to implement, we take a more general approach and bound… (More)

- J. Zico Kolter, Siddharth Batra, Andrew Y. Ng
- NIPS
- 2010

Energy disaggregation is the task of taking a whole-home energy signal and separating it into its component appliances. Studies have shown that having devicelevel energy information can cause users to conserve significant amounts of energy, but current electricity meters only report whole-home data. Thus, developing algorithmic methods for disaggregation… (More)