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Tobias Scheffer

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33 papers
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33

ICML Conference 2024 Conference Paper

Predicting Dose-Response Curves with Deep Neural Networks

  • Pedro Alonso Campana
  • Paul Prasse
  • Tobias Scheffer

Dose-response curves characterize the relationship between the concentration of drugs and their inhibitory effect on the growth of specific types of cells. The predominant Hill-equation model of an ideal enzymatic inhibition unduly simplifies the biochemical reality of many drugs; and for these drugs the widely-used drug performance indicator of the half-inhibitory concentration $IC_{50}$ can lead to poor therapeutic recommendations and poor selections of promising drug candidates. We develop a neural model that uses an embedding of the interaction between drug molecules and the tissue transcriptome to estimate the entire dose-response curve rather than a scalar aggregate. We find that, compared to the prior state of the art, this model excels at interpolating and extrapolating the inhibitory effect of untried concentrations. Unlike prevalent parametric models, it it able to accurately predict dose-response curves of drugs on previously unseen tumor tissues as well as of previously untested drug molecules on established tumor cell lines.

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JMLR Journal 2015 Journal Article

Learning to Identify Concise Regular Expressions that Describe Email Campaigns

  • Paul Prasse
  • Christoph Sawade
  • Niels Landwehr
  • Tobias Scheffer

This paper addresses the problem of inferring a regular expression from a given set of strings that resembles, as closely as possible, the regular expression that a human expert would have written to identify the language. This is motivated by our goal of automating the task of postmasters who use regular expressions to describe and blacklist email spam campaigns. Training data contains batches of messages and corresponding regular expressions that an expert postmaster feels confident to blacklist. We model this task as a two-stage learning problem with structured output spaces and appropriate loss functions. We derive decoders and the resulting optimization problems which can be solved using standard cutting plane methods. We report on a case study conducted with an email service provider. [abs] [ pdf ][ bib ] &copy JMLR 2015. ( edit, beta )

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IJCAI Conference 2013 Conference Paper

Active Evaluation of Ranking Functions Based on Graded Relevance (Extended Abstract)

  • Christoph Sawade
  • Steffen Bickel
  • Timo von Oertzen
  • Tobias Scheffer
  • Niels Landwehr

Evaluating the quality of ranking functions is a core task in web search and other information retrieval domains. Because query distributions and item relevance change over time, ranking models often cannot be evaluated accurately on held-out training data. Instead, considerable effort is spent on manually labeling the relevance of query results for test queries in order to track ranking performance. We address the problem of estimating ranking performance as accurately as possible on a fixed labeling budget. Estimates are based on a set of most informative test queries selected by an active sampling distribution. Query labeling costs depend on the number of result items and item-specific attributes such as document length. We derive cost-optimal sampling distributions for commonly used ranking performance measures. Experiments on web search engine data illustrate significant reductions in labeling costs.

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ICML Conference 2013 Conference Paper

Bayesian Games for Adversarial Regression Problems

  • Michael Großhans
  • Christoph Sawade
  • Michael Brückner
  • Tobias Scheffer

We study regression problems in which an adversary can exercise some control over the data generation process. Learner and adversary have conflicting but not necessarily perfectly antagonistic objectives. We study the case in which the learner is not fully informed about the adversary’s objective; instead, any knowledge of the learner about parameters of the adversary’s goal may be reflected in a Bayesian prior. We model this problem as a Bayesian game, and characterize conditions under which a unique Bayesian equilibrium point exists. We experimentally compare the Bayesian equilibrium strategy to the Nash equilibrium strategy, the minimax strategy, and regular linear regression.

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NeurIPS Conference 2012 Conference Paper

Active Comparison of Prediction Models

  • Christoph Sawade
  • Niels Landwehr
  • Tobias Scheffer

We address the problem of comparing the risks of two given predictive models - for instance, a baseline model and a challenger - as confidently as possible on a fixed labeling budget. This problem occurs whenever models cannot be compared on held-out training data, possibly because the training data are unavailable or do not reflect the desired test distribution. In this case, new test instances have to be drawn and labeled at a cost. We devise an active comparison method that selects instances according to an instrumental sampling distribution. We derive the sampling distribution that maximizes the power of a statistical test applied to the observed empirical risks, and thereby minimizes the likelihood of choosing the inferior model. Empirically, we investigate model selection problems on several classification and regression tasks and study the accuracy of the resulting p-values.

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JMLR Journal 2012 Journal Article

Static Prediction Games for Adversarial Learning Problems

  • Michael Brückner
  • Christian Kanzow
  • Tobias Scheffer

The standard assumption of identically distributed training and test data is violated when the test data are generated in response to the presence of a predictive model. This becomes apparent, for example, in the context of email spam filtering. Here, email service providers employ spam filters, and spam senders engineer campaign templates to achieve a high rate of successful deliveries despite the filters. We model the interaction between the learner and the data generator as a static game in which the cost functions of the learner and the data generator are not necessarily antagonistic. We identify conditions under which this prediction game has a unique Nash equilibrium and derive algorithms that find the equilibrial prediction model. We derive two instances, the Nash logistic regression and the Nash support vector machine, and empirically explore their properties in a case study on email spam filtering. [abs] [ pdf ][ bib ] &copy JMLR 2012. ( edit, beta )

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NeurIPS Conference 2010 Conference Paper

Active Estimation of F-Measures

  • Christoph Sawade
  • Niels Landwehr
  • Tobias Scheffer

We address the problem of estimating the F-measure of a given model as accurately as possible on a fixed labeling budget. This problem occurs whenever an estimate cannot be obtained from held-out training data; for instance, when data that have been used to train the model are held back for reasons of privacy or do not reflect the test distribution. In this case, new test instances have to be drawn and labeled at a cost. An active estimation procedure selects instances according to an instrumental sampling distribution. An analysis of the sources of estimation error leads to an optimal sampling distribution that minimizes estimator variance. We explore conditions under which active estimates of F-measures are more accurate than estimates based on instances sampled from the test distribution.

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NeurIPS Conference 2010 Conference Paper

Throttling Poisson Processes

  • Uwe Dick
  • Peter Haider
  • Thomas Vanck
  • Michael Brückner
  • Tobias Scheffer

We study a setting in which Poisson processes generate sequences of decision-making events. The optimization goal is allowed to depend on the rate of decision outcomes; the rate may depend on a potentially long backlog of events and decisions. We model the problem as a Poisson process with a throttling policy that enforces a data-dependent rate limit and reduce the learning problem to a convex optimization problem that can be solved efficiently. This problem setting matches applications in which damage caused by an attacker grows as a function of the rate of unsuppressed hostile events. We report on experiments on abuse detection for an email service.

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JMLR Journal 2009 Journal Article

Discriminative Learning Under Covariate Shift

  • Steffen Bickel
  • Michael Brückner
  • Tobias Scheffer

We address classification problems for which the training instances are governed by an input distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. The problem of learning under covariate shift can be written as an integrated optimization problem. Instantiating the general optimization problem leads to a kernel logistic regression and an exponential model classifier for covariate shift. The optimization problem is convex under certain conditions; our findings also clarify the relationship to the known kernel mean matching procedure. We report on experiments on problems of spam filtering, text classification, and landmine detection. [abs] [ pdf ][ bib ] &copy JMLR 2009. ( edit, beta )

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NeurIPS Conference 2009 Conference Paper

Localizing Bugs in Program Executions with Graphical Models

  • Laura Dietz
  • Valentin Dallmeier
  • Andreas Zeller
  • Tobias Scheffer

We devise a graphical model that supports the process of debugging software by guiding developers to code that is likely to contain defects. The model is trained using execution traces of passing test runs; it reflects the distribution over transitional patterns of code positions. Given a failing test case, the model determines the least likely transitional pattern in the execution trace. The model is designed such that Bayesian inference has a closed-form solution. We evaluate the Bernoulli graph model on data of the software projects AspectJ and Rhino.

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NeurIPS Conference 2009 Conference Paper

Nash Equilibria of Static Prediction Games

  • Michael Brückner
  • Tobias Scheffer

The standard assumption of identically distributed training and test data can be violated when an adversary can exercise some control over the generation of the test data. In a prediction game, a learner produces a predictive model while an adversary may alter the distribution of input data. We study single-shot prediction games in which the cost functions of learner and adversary are not necessarily antagonistic. We identify conditions under which the prediction game has a unique Nash equilibrium, and derive algorithms that will find the equilibrial prediction models. In a case study, we explore properties of Nash-equilibrial prediction models for email spam filtering empirically.

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NeurIPS Conference 2008 Conference Paper

Transfer Learning by Distribution Matching for Targeted Advertising

  • Steffen Bickel
  • Christoph Sawade
  • Tobias Scheffer

We address the problem of learning classifiers for several related tasks that may differ in their joint distribution of input and output variables. For each task, small - possibly even empty - labeled samples and large unlabeled samples are available. While the unlabeled samples reflect the target distribution, the labeled samples may be biased. We derive a solution that produces resampling weights which match the pool of all examples to the target distribution of any given task. Our work is motivated by the problem of predicting sociodemographic features for users of web portals, based on the content which they have accessed. Here, questionnaires offered to a small portion of each portal's users produce biased samples. Transfer learning enables us to make predictions even for new portals with few or no training data and improves the overall prediction accuracy.

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ICML Conference 2007 Conference Paper

Discriminative learning for differing training and test distributions

  • Steffen Bickel
  • Michael Brückner
  • Tobias Scheffer

We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution---problems also referred to as classification under covariate shift. We derive a solution that is purely discriminative: neither training nor test distribution are modeled explicitly. We formulate the general problem of learning under covariate shift as an integrated optimization problem. We derive a kernel logistic regression classifier for differing training and test distributions.

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ICML Conference 2007 Conference Paper

Unsupervised prediction of citation influences

  • Laura Dietz
  • Steffen Bickel
  • Tobias Scheffer

Publication repositories contain an abundance of information about the evolution of scientific research areas. We address the problem of creating a visualization of a research area that describes the flow of topics between papers, quantifies the impact that papers have on each other, and helps to identify key contributions. To this end, we devise a probabilistic topic model that explains the generation of documents; the model incorporates the aspects of topical innovation and topical inheritance via citations. We evaluate the model's ability to predict the strength of influence of citations against manually rated citations.

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NeurIPS Conference 2006 Conference Paper

Dirichlet-Enhanced Spam Filtering based on Biased Samples

  • Steffen Bickel
  • Tobias Scheffer

We study a setting that is motivated by the problem of filtering spam messages for many users. Each user receives messages according to an individual, unknown distribution, reflected only in the unlabeled inbox. The spam filter for a user is required to perform well with respect to this distribution. Labeled messages from publicly available sources can be utilized, but they are governed by a distinct distribution, not adequately representing most inboxes. We devise a method that minimizes a loss function with respect to a user's personal distribution based on the available biased sample. A nonparametric hierarchical Bayesian model furthermore generalizes across users by learning a common prior which is imposed on new email accounts. Empirically, we observe that bias-corrected learning outperforms naive reliance on the assumption of independent and identically distributed data; Dirichlet-enhanced generalization across users outperforms a single ("one size fits all") filter as well as independent filters for all users.

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ICML Conference 2006 Conference Paper

Efficient co-regularised least squares regression

  • Ulf Brefeld
  • Thomas Gärtner 0001
  • Tobias Scheffer
  • Stefan Wrobel

In many applications, unlabelled examples are inexpensive and easy to obtain. Semi-supervised approaches try to utilise such examples to reduce the predictive error. In this paper, we investigate a semi-supervised least squares regression algorithm based on the co-learning approach. Similar to other semi-supervised algorithms, our base algorithm has cubic runtime complexity in the number of unlabelled examples. To be able to handle larger sets of unlabelled examples, we devise a semi-parametric variant that scales linearly in the number of unlabelled examples. Experiments show a significant error reduction by co-regularisation and a large runtime improvement for the semi-parametric approximation. Last but not least, we propose a distributed procedure that can be applied without collecting all data at a single site.

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JMLR Journal 2002 Journal Article

Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling

  • Tobias Scheffer
  • Stefan Wrobel

Many discovery problems, e. g. subgroup or association rule discovery, can naturally be cast as n -best hypotheses problems where the goal is to find the n hypotheses from a given hypothesis space that score best according to a certain utility function. We present a sampling algorithm that solves this problem by issuing a small number of database queries while guaranteeing precise bounds on the confidence and quality of solutions. Known sampling approaches have treated single hypothesis selection problems, assuming that the utility is the average (over the examples) of some function --- which is not the case for many frequently used utility functions. We show that our algorithm works for all utilities that can be estimated with bounded error. We provide these error bounds and resulting worst-case sample bounds for some of the most frequently used utilities, and prove that there is no sampling algorithm for a popular class of utility functions that cannot be estimated with bounded error. The algorithm is sequential in the sense that it starts to return (or discard) hypotheses that already seem to be particularly good (or bad) after a few examples. Thus, the algorithm is almost always faster than its worst-case bounds.

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IJCAI Conference 1997 Conference Paper

Unbiased Assessment of Learning Algorithms

  • Tobias Scheffer
  • Ralf Herbrich

In order to rank the performance of machine learning algorithms, many researchers conduct experiments on benchmark data sets. Since most learning algorithms have domain-specific parameters, it is a popular custom to adapt these parameters to obtain a minimal error rate on the test set. The same rate is then used to rank the algorithm, which causes an optimistic bias. We quantify this bias, showing, in particular, that an algorithm with more parameters will probably be ranked higher than an equally good algorithm with fewer parameters. We demonstrate this result, showing the number of parameters and trials required in order to pretend to outperform C4. 5 or FOIL, respectively, for various benchmark problems. We then describe out how unbiased ranking experiments should be conducted.

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