Search Results for author: Andreas Krause

Found 180 papers, 58 papers with code

Multi-Scale Representation Learning on Proteins

no code implementations NeurIPS 2021 Vignesh Ram Somnath, Charlotte Bunne, Andreas Krause

This paper introduces a multi-scale graph construction of a protein -- HoloProt -- connecting surface to structure and sequence.

graph construction Protein Function Prediction +2

Tuning Particle Accelerators with Safety Constraints using Bayesian Optimization

no code implementations26 Mar 2022 Johannes Kirschner, Mojmir Mutný, Andreas Krause, Jaime Coello de Portugal, Nicole Hiller, Jochem Snuverink

Tuning machine parameters of particle accelerators is a repetitive and time-consuming task, that is challenging to automate.

Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation

no code implementations14 Mar 2022 Pier Giuseppe Sessa, Maryam Kamgarpour, Andreas Krause

We consider model-based multi-agent reinforcement learning, where the environment transition model is unknown and can only be learned via expensive interactions with the environment.

Autonomous Driving Gaussian Processes +2

Recovering Stochastic Dynamics via Gaussian Schrödinger Bridges

no code implementations11 Feb 2022 Charlotte Bunne, Ya-Ping Hsieh, Marco Cuturi, Andreas Krause

Our goal is to rely on Gaussian approximations of the data to provide the reference stochastic process needed to estimate SB.

A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits

no code implementations3 Feb 2022 Ilija Bogunovic, Zihan Li, Andreas Krause, Jonathan Scarlett

We consider the sequential optimization of an unknown, continuous, and expensive to evaluate reward function, from noisy and adversarially corrupted observed rewards.

Meta-Learning Hypothesis Spaces for Sequential Decision-making

no code implementations1 Feb 2022 Parnian Kassraie, Jonas Rothfuss, Andreas Krause

We demonstrate our approach on the kernelized bandit problem (a. k. a.~Bayesian optimization), where we establish regret bounds competitive with those given the true kernel.

Decision Making Meta-Learning +1

Constrained Policy Optimization via Bayesian World Models

1 code implementation ICLR 2022 Yarden As, Ilnura Usmanova, Sebastian Curi, Andreas Krause

Improving sample-efficiency and safety are crucial challenges when deploying reinforcement learning in high-stakes real world applications.


GoSafeOpt: Scalable Safe Exploration for Global Optimization of Dynamical Systems

no code implementations24 Jan 2022 Bhavya Sukhija, Matteo Turchetta, David Lindner, Andreas Krause, Sebastian Trimpe, Dominik Baumann

Learning optimal control policies directly on physical systems is challenging since even a single failure can lead to costly hardware damage.

Safe Exploration

Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking

1 code implementation ICLR 2022 Octavian-Eugen Ganea, Xinyuan Huang, Charlotte Bunne, Yatao Bian, Regina Barzilay, Tommi Jaakkola, Andreas Krause

Protein complex formation is a central problem in biology, being involved in most of the cell's processes, and essential for applications, e. g. drug design or protein engineering.

Graph Matching Translation

Misspecified Gaussian Process Bandit Optimization

no code implementations NeurIPS 2021 Ilija Bogunovic, Andreas Krause

Instead, we introduce a \emph{misspecified} kernelized bandit setting where the unknown function can be $\epsilon$--uniformly approximated by a function with a bounded norm in some Reproducing Kernel Hilbert Space (RKHS).

Risk-averse Heteroscedastic Bayesian Optimization

1 code implementation NeurIPS 2021 Anastasiia Makarova, Ilnura Usmanova, Ilija Bogunovic, Andreas Krause

We generalize BO to trade mean and input-dependent variance of the objective, both of which we assume to be unknown a priori.

Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems

no code implementations NeurIPS 2021 Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause, Florian Dörfler

To this end, neural ODEs regularized with neural Lyapunov functions are a promising approach when states are fully observed.

Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes

no code implementations22 Oct 2021 Elvis Nava, Mojmír Mutný, Andreas Krause

In Bayesian Optimization (BO) we study black-box function optimization with noisy point evaluations and Bayesian priors.

Point Processes

Sensing Cox Processes via Posterior Sampling and Positive Bases

1 code implementation21 Oct 2021 Mojmír Mutný, Andreas Krause

We study adaptive sensing of Cox point processes, a widely used model from spatial statistics.

Experimental Design Point Processes

Hierarchical Skills for Efficient Exploration

1 code implementation NeurIPS 2021 Jonas Gehring, Gabriel Synnaeve, Andreas Krause, Nicolas Usunier

We alleviate the need for prior knowledge by proposing a hierarchical skill learning framework that acquires skills of varying complexity in an unsupervised manner.

Continuous Control Efficient Exploration +2

Invariant Causal Mechanisms through Distribution Matching

no code implementations29 Sep 2021 Mathieu Chevalley, Charlotte Bunne, Andreas Krause, Stefan Bauer

Learning representations that capture the underlying data generating process is akey problem for data efficient and robust use of neural networks.

Domain Generalization

Data Summarization via Bilevel Optimization

no code implementations26 Sep 2021 Zalán Borsos, Mojmír Mutný, Marco Tagliasacchi, Andreas Krause

We show the effectiveness of our framework for a wide range of models in various settings, including training non-convex models online and batch active learning.

Active Learning Bilevel Optimization +1

Contextual Games: Multi-Agent Learning with Side Information

no code implementations NeurIPS 2020 Pier Giuseppe Sessa, Ilija Bogunovic, Andreas Krause, Maryam Kamgarpour

We formulate the novel class of contextual games, a type of repeated games driven by contextual information at each round.

Neural Contextual Bandits without Regret

1 code implementation7 Jul 2021 Parnian Kassraie, Andreas Krause

Contextual bandits are a rich model for sequential decision making given side information, with important applications, e. g., in recommender systems.

Decision Making Multi-Armed Bandits +1

PopSkipJump: Decision-Based Attack for Probabilistic Classifiers

1 code implementation14 Jun 2021 Carl-Johann Simon-Gabriel, Noman Ahmed Sheikh, Andreas Krause

Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output.

Proximal Optimal Transport Modeling of Population Dynamics

1 code implementation11 Jun 2021 Charlotte Bunne, Laetitia Meng-Papaxanthos, Andreas Krause, Marco Cuturi

We propose to model these trajectories as collective realizations of a causal Jordan-Kinderlehrer-Otto (JKO) flow of measures: The JKO scheme posits that the new configuration taken by a population at time $t+1$ is one that trades off an improvement, in the sense that it decreases an energy, while remaining close (in Wasserstein distance) to the previous configuration observed at $t$.

Meta-Learning Reliable Priors in the Function Space

no code implementations NeurIPS 2021 Jonas Rothfuss, Dominique Heyn, Jinfan Chen, Andreas Krause

When data are scarce meta-learning can improve a learner's accuracy by harnessing previous experience from related learning tasks.

Decision Making Meta-Learning

Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning

no code implementations ICLR 2022 Yatao Bian, Yu Rong, Tingyang Xu, Jiaxiang Wu, Andreas Krause, Junzhou Huang

By running fixed point iteration for multiple steps, we achieve a trajectory of the valuations, among which we define the valuation with the best conceivable decoupling error as the Variational Index.

Variational Inference

Learning Graph Models for Template-Free Retrosynthesis

no code implementations arXiv 2021 Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.

Single-step retrosynthesis

Addressing the Long-term Impact of ML Decisions via Policy Regret

1 code implementation2 Jun 2021 David Lindner, Hoda Heidari, Andreas Krause

To capture the long-term effects of ML-based allocation decisions, we study a setting in which the reward from each arm evolves every time the decision-maker pulls that arm.

Multi-Armed Bandits

Bias-Robust Bayesian Optimization via Dueling Bandits

no code implementations25 May 2021 Johannes Kirschner, Andreas Krause

We consider Bayesian optimization in settings where observations can be adversarially biased, for example by an uncontrolled hidden confounder.

DiBS: Differentiable Bayesian Structure Learning

1 code implementation NeurIPS 2021 Lars Lorch, Jonas Rothfuss, Bernhard Schölkopf, Andreas Krause

In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.

Causal Discovery Variational Inference

A note on the CAPM with endogenously consistent market returns

no code implementations21 May 2021 Andreas Krause

I demonstrate that with the market return determined by the equilibrium returns of the CAPM, expected returns of an asset are affected by the risks of all assets jointly.

Regret Bounds for Gaussian-Process Optimization in Large Domains

1 code implementation NeurIPS 2021 Manuel Wüthrich, Bernhard Schölkopf, Andreas Krause

These regret bounds illuminate the relationship between the number of evaluations, the domain size (i. e. cardinality of finite domains / Lipschitz constant of the covariance function in continuous domains), and the optimality of the retrieved function value.

Automatic Termination for Hyperparameter Optimization

no code implementations16 Apr 2021 Anastasia Makarova, Huibin Shen, Valerio Perrone, Aaron Klein, Jean Baptiste Faddoul, Andreas Krause, Matthias Seeger, Cedric Archambeau

Bayesian optimization (BO) is a widely popular approach for the hyperparameter optimization (HPO) of machine learning algorithms.

Hyperparameter Optimization

Risk-Averse Offline Reinforcement Learning

1 code implementation ICLR 2021 Núria Armengol Urpí, Sebastian Curi, Andreas Krause

We demonstrate empirically that in the presence of natural distribution-shifts, O-RAAC learns policies with good average performance.


Efficient Pure Exploration for Combinatorial Bandits with Semi-Bandit Feedback

no code implementations21 Jan 2021 Marc Jourdan, Mojmír Mutný, Johannes Kirschner, Andreas Krause

Combinatorial bandits with semi-bandit feedback generalize multi-armed bandits, where the agent chooses sets of arms and observes a noisy reward for each arm contained in the chosen set.

Multi-Armed Bandits online learning

Safe and Efficient Model-free Adaptive Control via Bayesian Optimization

no code implementations19 Jan 2021 Christopher König, Matteo Turchetta, John Lygeros, Alisa Rupenyan, Andreas Krause

Thus, our approach builds on GoOSE, an algorithm for safe and sample-efficient Bayesian optimization.

Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory

no code implementations1 Jan 2021 Jonas Rothfuss, Martin Josifoski, Andreas Krause

Bayesian deep learning is a promising approach towards improved uncertainty quantification and sample efficiency.

Meta-Learning Variational Inference

Logistic Q-Learning

no code implementations21 Oct 2020 Joan Bas-Serrano, Sebastian Curi, Andreas Krause, Gergely Neu

We propose a new reinforcement learning algorithm derived from a regularized linear-programming formulation of optimal control in MDPs.

Q-Learning reinforcement-learning

Semi-supervised Batch Active Learning via Bilevel Optimization

1 code implementation19 Oct 2020 Zalán Borsos, Marco Tagliasacchi, Andreas Krause

Active learning is an effective technique for reducing the labeling cost by improving data efficiency.

Active Learning Bilevel Optimization +1

Online Active Model Selection for Pre-trained Classifiers

1 code implementation19 Oct 2020 Mohammad Reza Karimi, Nezihe Merve Gürel, Bojan Karlaš, Johannes Rausch, Ce Zhang, Andreas Krause

Given $k$ pre-trained classifiers and a stream of unlabeled data examples, how can we actively decide when to query a label so that we can distinguish the best model from the rest while making a small number of queries?

Model Selection

Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

5 code implementations ICLR 2021 Max B. Paulus, Chris J. Maddison, Andreas Krause

Gradient estimation in models with discrete latent variables is a challenging problem, because the simplest unbiased estimators tend to have high variance.

Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases

2 code implementations1 Oct 2020 Chris Wendler, Andisheh Amrollahi, Bastian Seifert, Andreas Krause, Markus Püschel

Many applications of machine learning on discrete domains, such as learning preference functions in recommender systems or auctions, can be reduced to estimating a set function that is sparse in the Fourier domain.

Recommendation Systems

Stochastic Linear Bandits Robust to Adversarial Attacks

no code implementations7 Jul 2020 Ilija Bogunovic, Arpan Losalka, Andreas Krause, Jonathan Scarlett

We consider a stochastic linear bandit problem in which the rewards are not only subject to random noise, but also adversarial attacks subject to a suitable budget $C$ (i. e., an upper bound on the sum of corruption magnitudes across the time horizon).

Continuous Submodular Function Maximization

no code implementations24 Jun 2020 Yatao Bian, Joachim M. Buhmann, Andreas Krause

We start by a thorough characterization of the class of continuous submodular functions, and show that continuous submodularity is equivalent to a weak version of the diminishing returns (DR) property.

Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning

1 code implementation NeurIPS 2020 Sebastian Curi, Felix Berkenkamp, Andreas Krause

Based on this theoretical foundation, we show how optimistic exploration can be easily combined with state-of-the-art reinforcement learning algorithms and different probabilistic models.

Model-based Reinforcement Learning reinforcement-learning

Learning Graph Models for Retrosynthesis Prediction

no code implementations NeurIPS 2021 Vignesh Ram Somnath, Charlotte Bunne, Connor W. Coley, Andreas Krause, Regina Barzilay

Retrosynthesis prediction is a fundamental problem in organic synthesis, where the task is to identify precursor molecules that can be used to synthesize a target molecule.

Safe non-smooth black-box optimization with application to policy search

no code implementations L4DC 2020 Ilnura Usmanova, Andreas Krause, Maryam Kamgarpour

For safety-critical black-box optimization tasks, observations of the constraints and the objective are often noisy and available only for the feasible points.

Hierarchical Image Classification using Entailment Cone Embeddings

1 code implementation2 Apr 2020 Ankit Dhall, Anastasia Makarova, Octavian Ganea, Dario Pavllo, Michael Greeff, Andreas Krause

Image classification has been studied extensively, but there has been limited work in using unconventional, external guidance other than traditional image-label pairs for training.

Classification General Classification +2

SLEIPNIR: Deterministic and Provably Accurate Feature Expansion for Gaussian Process Regression with Derivatives

1 code implementation5 Mar 2020 Emmanouil Angelis, Philippe Wenk, Bernhard Schölkopf, Stefan Bauer, Andreas Krause

Gaussian processes are an important regression tool with excellent analytic properties which allow for direct integration of derivative observations.

Gaussian Processes

Corruption-Tolerant Gaussian Process Bandit Optimization

no code implementations4 Mar 2020 Ilija Bogunovic, Andreas Krause, Jonathan Scarlett

We consider the problem of optimizing an unknown (typically non-convex) function with a bounded norm in some Reproducing Kernel Hilbert Space (RKHS), based on noisy bandit feedback.

Mixed Strategies for Robust Optimization of Unknown Objectives

no code implementations28 Feb 2020 Pier Giuseppe Sessa, Ilija Bogunovic, Maryam Kamgarpour, Andreas Krause

We consider robust optimization problems, where the goal is to optimize an unknown objective function against the worst-case realization of an uncertain parameter.

Autonomous Vehicles Gaussian Processes +2

Information Directed Sampling for Linear Partial Monitoring

no code implementations25 Feb 2020 Johannes Kirschner, Tor Lattimore, Andreas Krause

Partial monitoring is a rich framework for sequential decision making under uncertainty that generalizes many well known bandit models, including linear, combinatorial and dueling bandits.

Decision Making Decision Making Under Uncertainty

Distributionally Robust Bayesian Optimization

no code implementations20 Feb 2020 Johannes Kirschner, Ilija Bogunovic, Stefanie Jegelka, Andreas Krause

Attaining such robustness is the goal of distributionally robust optimization, which seeks a solution to an optimization problem that is worst-case robust under a specified distributional shift of an uncontrolled covariate.

Efficiently Learning Fourier Sparse Set Functions

1 code implementation NeurIPS 2019 Andisheh Amrollahi, Amir Zandieh, Michael Kapralov, Andreas Krause

In this paper we consider the problem of efficiently learning set functions that are defined over a ground set of size $n$ and that are sparse (say $k$-sparse) in the Fourier domain.

A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness

no code implementations8 Nov 2019 Mohammad Yaghini, Andreas Krause, Hoda Heidari

Our family of fairness notions corresponds to a new interpretation of economic models of Equality of Opportunity (EOP), and it includes most existing notions of fairness as special cases.

Decision Making Fairness

Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization

no code implementations29 Oct 2019 Matteo Turchetta, Andreas Krause, Sebastian Trimpe

In reinforcement learning (RL), an autonomous agent learns to perform complex tasks by maximizing an exogenous reward signal while interacting with its environment.


Adaptive Sampling for Stochastic Risk-Averse Learning

1 code implementation NeurIPS 2020 Sebastian Curi, Kfir. Y. Levy, Stefanie Jegelka, Andreas Krause

In high-stakes machine learning applications, it is crucial to not only perform well on average, but also when restricted to difficult examples.

Point Processes

Noise Regularization for Conditional Density Estimation

1 code implementation21 Jul 2019 Jonas Rothfuss, Fabio Ferreira, Simon Boehm, Simon Walther, Maxim Ulrich, Tamim Asfour, Andreas Krause

To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training.

Density Estimation

Structured Variational Inference in Unstable Gaussian Process State Space Models

1 code implementation16 Jul 2019 Silvan Melchior, Sebastian Curi, Felix Berkenkamp, Andreas Krause

Finally, we show experimentally that our learning algorithm performs well in stable and unstable real systems with hidden states.

Gaussian Processes Variational Inference

Mixed-Variable Bayesian Optimization

no code implementations2 Jul 2019 Erik Daxberger, Anastasia Makarova, Matteo Turchetta, Andreas Krause

However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications.

Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning

no code implementations28 Jun 2019 Marcello Fiducioso, Sebastian Curi, Benedikt Schumacher, Markus Gwerder, Andreas Krause

Furthermore, this successful attempt paves the way for further use at different levels of HVAC systems, with promising energy, operational, and commissioning costs savings, and it is a practical demonstration of the positive effects that Artificial Intelligence can have on environmental sustainability.

Learning-based Model Predictive Control for Safe Exploration and Reinforcement Learning

1 code implementation27 Jun 2019 Torsten Koller, Felix Berkenkamp, Matteo Turchetta, Joschka Boedecker, Andreas Krause

We evaluate the resulting algorithm to safely explore the dynamics of an inverted pendulum and to solve a reinforcement learning task on a cart-pole system with safety constraints.

reinforcement-learning Safe Exploration

Stochastic Bandits with Context Distributions

1 code implementation NeurIPS 2019 Johannes Kirschner, Andreas Krause

We introduce a stochastic contextual bandit model where at each time step the environment chooses a distribution over a context set and samples the context from this distribution.

Learning Generative Models across Incomparable Spaces

no code implementations14 May 2019 Charlotte Bunne, David Alvarez-Melis, Andreas Krause, Stefanie Jegelka

Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety.

Relational Reasoning

Evaluating GANs via Duality

no code implementations ICLR 2019 Paulina Grnarova, Kfir. Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Thomas Hofmann, Andreas Krause

Generative Adversarial Networks (GANs) have shown great results in accurately modeling complex distributions, but their training is known to be difficult due to instabilities caused by a challenging minimax optimization problem.

Online Variance Reduction with Mixtures

1 code implementation29 Mar 2019 Zalán Borsos, Sebastian Curi, Kfir. Y. Levy, Andreas Krause

Adaptive importance sampling for stochastic optimization is a promising approach that offers improved convergence through variance reduction.

Stochastic Optimization

Multi-Player Bandits: The Adversarial Case

no code implementations21 Feb 2019 Pragnya Alatur, Kfir. Y. Levy, Andreas Krause

We consider a setting where multiple players sequentially choose among a common set of actions (arms).

ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems

2 code implementations17 Feb 2019 Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer

Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting.

Gaussian Processes Model Selection

Adaptive Sequence Submodularity

1 code implementation NeurIPS 2019 Marko Mitrovic, Ehsan Kazemi, Moran Feldman, Andreas Krause, Amin Karbasi

In many machine learning applications, one needs to interactively select a sequence of items (e. g., recommending movies based on a user's feedback) or make sequential decisions in a certain order (e. g., guiding an agent through a series of states).

Decision Making Link Prediction +1

Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

2 code implementations8 Feb 2019 Johannes Kirschner, Mojmír Mutný, Nicole Hiller, Rasmus Ischebeck, Andreas Krause

In order to scale the method and keep its benefits, we propose an algorithm (LineBO) that restricts the problem to a sequence of iteratively chosen one-dimensional sub-problems that can be solved efficiently.

No-Regret Bayesian Optimization with Unknown Hyperparameters

no code implementations10 Jan 2019 Felix Berkenkamp, Angela P. Schoellig, Andreas Krause

In this paper, we present the first BO algorithm that is provably no-regret and converges to the optimum without knowledge of the hyperparameters.

Provable Variational Inference for Constrained Log-Submodular Models

no code implementations NeurIPS 2018 Josip Djolonga, Stefanie Jegelka, Andreas Krause

Submodular maximization problems appear in several areas of machine learning and data science, as many useful modelling concepts such as diversity and coverage satisfy this natural diminishing returns property.

Variational Inference

Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features

no code implementations NeurIPS 2018 Mojmir Mutny, Andreas Krause

We develop an efficient and provably no-regret Bayesian optimization (BO) algorithm for optimization of black-box functions in high dimensions.

Hyperparameter Optimization Numerical Integration

Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy

no code implementations13 Nov 2018 Robin Spiess, Felix Berkenkamp, Jan Poland, Andreas Krause

In this paper, we present a deep learning approach that uses images of the sky to compensate power fluctuations predictively and reduces battery stress.

Imitation Learning

A domain agnostic measure for monitoring and evaluating GANs

1 code implementation NeurIPS 2019 Paulina Grnarova, Kfir. Y. Levy, Aurelien Lucchi, Nathanael Perraudin, Ian Goodfellow, Thomas Hofmann, Andreas Krause

Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring the progress of a single model throughout training.

A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity

no code implementations10 Sep 2018 Hoda Heidari, Michele Loi, Krishna P. Gummadi, Andreas Krause

In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness.


The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems

1 code implementation2 Aug 2018 Spencer M. Richards, Felix Berkenkamp, Andreas Krause

We demonstrate our method by learning the safe region of attraction for a simulated inverted pendulum.

Discrete Sampling using Semigradient-based Product Mixtures

no code implementations4 Jul 2018 Alkis Gotovos, Hamed Hassani, Andreas Krause, Stefanie Jegelka

We consider the problem of inference in discrete probabilistic models, that is, distributions over subsets of a finite ground set.

Point Processes

Adaptive Input Estimation in Linear Dynamical Systems with Applications to Learning-from-Observations

no code implementations19 Jun 2018 Sebastian Curi, Kfir. Y. Levy, Andreas Krause

To this end, we introduce a novel estimation algorithm that explicitly trades off bias and variance to optimally reduce the overall estimation error.

Imitation Learning

Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making

no code implementations NeurIPS 2018 Hoda Heidari, Claudio Ferrari, Krishna P. Gummadi, Andreas Krause

We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations.

Decision Making Fairness

Teaching Multiple Concepts to a Forgetful Learner

no code implementations NeurIPS 2019 Anette Hunziker, Yuxin Chen, Oisin Mac Aodha, Manuel Gomez Rodriguez, Andreas Krause, Pietro Perona, Yisong Yue, Adish Singla

Our framework is both generic, allowing the design of teaching schedules for different memory models, and also interactive, allowing the teacher to adapt the schedule to the underlying forgetting mechanisms of the learner.


Optimal DR-Submodular Maximization and Applications to Provable Mean Field Inference

no code implementations19 May 2018 An Bian, Joachim M. Buhmann, Andreas Krause

Mean field inference in probabilistic models is generally a highly nonconvex problem.

Learning-based Model Predictive Control for Safe Exploration

1 code implementation22 Mar 2018 Torsten Koller, Felix Berkenkamp, Matteo Turchetta, Andreas Krause

However, these methods typically do not provide any safety guarantees, which prevents their use in safety-critical, real-world applications.

Safe Exploration

Online Variance Reduction for Stochastic Optimization

2 code implementations13 Feb 2018 Zalán Borsos, Andreas Krause, Kfir. Y. Levy

Modern stochastic optimization methods often rely on uniform sampling which is agnostic to the underlying characteristics of the data.

Stochastic Optimization

Information Directed Sampling and Bandits with Heteroscedastic Noise

no code implementations29 Jan 2018 Johannes Kirschner, Andreas Krause

In the stochastic bandit problem, the goal is to maximize an unknown function via a sequence of noisy evaluations.

Differentiable Learning of Submodular Models

no code implementations NeurIPS 2017 Josip Djolonga, Andreas Krause

In this paper we focus on the problem of submodular minimization, for which we show that such layers are indeed possible.

Variational Inference

Interactive Submodular Bandit

no code implementations NeurIPS 2017 Lin Chen, Andreas Krause, Amin Karbasi

We then receive a noisy feedback about the utility of the action (e. g., ratings) which we model as a submodular function over the context-action space.

Data Summarization News Recommendation

Fake News Detection in Social Networks via Crowd Signals

no code implementations24 Nov 2017 Sebastian Tschiatschek, Adish Singla, Manuel Gomez Rodriguez, Arpit Merchant, Andreas Krause

The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network.

Social and Information Networks

Learning User Preferences to Incentivize Exploration in the Sharing Economy

no code implementations17 Nov 2017 Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause

We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb.

online learning

Stochastic Submodular Maximization: The Case of Coverage Functions

no code implementations NeurIPS 2017 Mohammad Reza Karimi, Mario Lucic, Hamed Hassani, Andreas Krause

By exploiting that common extensions act linearly on the class of submodular functions, we employ projected stochastic gradient ascent and its variants in the continuous domain, and perform rounding to obtain discrete solutions.

Stochastic Optimization

Learning Implicit Generative Models Using Differentiable Graph Tests

no code implementations4 Sep 2017 Josip Djolonga, Andreas Krause

Recently, there has been a growing interest in the problem of learning rich implicit models - those from which we can sample, but can not evaluate their density.

Stochastic Optimization

Probabilistic Submodular Maximization in Sub-Linear Time

no code implementations ICML 2017 Serban Stan, Morteza Zadimoghaddam, Andreas Krause, Amin Karbasi

As a remedy, we introduce the problem of sublinear time probabilistic submodular maximization: Given training examples of functions (e. g., via user feature vectors), we seek to reduce the ground set so that optimizing new functions drawn from the same distribution will provide almost as much value when restricted to the reduced ground set as when using the full set.

Recommendation Systems

Uniform Deviation Bounds for k-Means Clustering

no code implementations ICML 2017 Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause

In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are unbounded.

Distributed and Provably Good Seedings for k-Means in Constant Rounds

no code implementations ICML 2017 Olivier Bachem, Mario Lucic, Andreas Krause

The k-Means++ algorithm is the state of the art algorithm to solve k-Means clustering problems as the computed clusterings are O(log k) competitive in expectation.

Streaming Non-monotone Submodular Maximization: Personalized Video Summarization on the Fly

1 code implementation12 Jun 2017 Baharan Mirzasoleiman, Stefanie Jegelka, Andreas Krause

The need for real time analysis of rapidly producing data streams (e. g., video and image streams) motivated the design of streaming algorithms that can efficiently extract and summarize useful information from massive data "on the fly".

Data Structures and Algorithms Information Retrieval

Training Gaussian Mixture Models at Scale via Coresets

no code implementations23 Mar 2017 Mario Lucic, Matthew Faulkner, Andreas Krause, Dan Feldman

In this work we show how to construct coresets for mixtures of Gaussians.

Practical Coreset Constructions for Machine Learning

2 code implementations19 Mar 2017 Olivier Bachem, Mario Lucic, Andreas Krause

We investigate coresets - succinct, small summaries of large data sets - so that solutions found on the summary are provably competitive with solution found on the full data set.

Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting

no code implementations16 Mar 2017 Yuxin Chen, Jean-Michel Renders, Morteza Haghir Chehreghani, Andreas Krause

We consider the optimal value of information (VoI) problem, where the goal is to sequentially select a set of tests with a minimal cost, so that one can efficiently make the best decision based on the observed outcomes.

online learning

Scalable k-Means Clustering via Lightweight Coresets

1 code implementation27 Feb 2017 Olivier Bachem, Mario Lucic, Andreas Krause

As such, they have been successfully used to scale up clustering models to massive data sets.

Data Summarization

Uniform Deviation Bounds for Unbounded Loss Functions like k-Means

no code implementations27 Feb 2017 Olivier Bachem, Mario Lucic, S. Hamed Hassani, Andreas Krause

In this paper, we provide a novel framework to obtain uniform deviation bounds for loss functions which are *unbounded*.

Learning to Use Learners' Advice

no code implementations16 Feb 2017 Adish Singla, Hamed Hassani, Andreas Krause

In our setting, the feedback at any time $t$ is limited in a sense that it is only available to the expert $i^t$ that has been selected by the central algorithm (forecaster), \emph{i. e.}, only the expert $i^t$ receives feedback from the environment and gets to learn at time $t$.

Multi-Armed Bandits online learning

Coordinated Online Learning With Applications to Learning User Preferences

no code implementations9 Feb 2017 Christoph Hirnschall, Adish Singla, Sebastian Tschiatschek, Andreas Krause

We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners.

Multi-Task Learning online learning

Fast and Provably Good Seedings for k-Means

no code implementations NeurIPS 2016 Olivier Bachem, Mario Lucic, Hamed Hassani, Andreas Krause

Seeding - the task of finding initial cluster centers - is critical in obtaining high-quality clusterings for k-Means.

Cooperative Graphical Models

no code implementations NeurIPS 2016 Josip Djolonga, Stefanie Jegelka, Sebastian Tschiatschek, Andreas Krause

We study a rich family of distributions that capture variable interactions significantly more expressive than those representable with low-treewidth or pairwise graphical models, or log-supermodular models.

Variational Inference

Variational Inference in Mixed Probabilistic Submodular Models

no code implementations NeurIPS 2016 Josip Djolonga, Sebastian Tschiatschek, Andreas Krause

We consider the problem of variational inference in probabilistic models with both log-submodular and log-supermodular higher-order potentials.

Variational Inference

Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation

no code implementations NeurIPS 2016 Ilija Bogunovic, Jonathan Scarlett, Andreas Krause, Volkan Cevher

We present a new algorithm, truncated variance reduction (TruVaR), that treats Bayesian optimization (BO) and level-set estimation (LSE) with Gaussian processes in a unified fashion.

Gaussian Processes

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

no code implementations17 Jun 2016 Andrew An Bian, Baharan Mirzasoleiman, Joachim M. Buhmann, Andreas Krause

Submodular continuous functions are a category of (generally) non-convex/non-concave functions with a wide spectrum of applications.

Data Summarization

Horizontally Scalable Submodular Maximization

no code implementations31 May 2016 Mario Lucic, Olivier Bachem, Morteza Zadimoghaddam, Andreas Krause

A variety of large-scale machine learning problems can be cast as instances of constrained submodular maximization.

Near-optimal Bayesian Active Learning with Correlated and Noisy Tests

no code implementations24 May 2016 Yuxin Chen, S. Hamed Hassani, Andreas Krause

We consider the Bayesian active learning and experimental design problem, where the goal is to learn the value of some unknown target variable through a sequence of informative, noisy tests.

Active Learning Experimental Design

Actively Learning Hemimetrics with Applications to Eliciting User Preferences

no code implementations23 May 2016 Adish Singla, Sebastian Tschiatschek, Andreas Krause

We propose an active learning algorithm that substantially reduces this sample complexity by exploiting the structural constraints on the version space of hemimetrics.

Active Learning

Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning

no code implementations2 May 2016 Mario Lucic, Mesrob I. Ohannessian, Amin Karbasi, Andreas Krause

Using k-means clustering as a prototypical unsupervised learning problem, we show how we can strategically summarize the data (control space) in order to trade off risk and time when data is generated by a probabilistic model.

Algorithms for Learning Sparse Additive Models with Interactions in High Dimensions

no code implementations2 May 2016 Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause

A function $f: \mathbb{R}^d \rightarrow \mathbb{R}$ is a Sparse Additive Model (SPAM), if it is of the form $f(\mathbf{x}) = \sum_{l \in \mathcal{S}}\phi_{l}(x_l)$ where $\mathcal{S} \subset [d]$, $|\mathcal{S}| \ll d$.

Additive models

Learning Sparse Additive Models with Interactions in High Dimensions

no code implementations18 Apr 2016 Hemant Tyagi, Anastasios Kyrillidis, Bernd Gärtner, Andreas Krause

For some $\mathcal{S}_1 \subset [d], \mathcal{S}_2 \subset {[d] \choose 2}$, the function $f$ is assumed to be of the form: $$f(\mathbf{x}) = \sum_{p \in \mathcal{S}_1}\phi_{p} (x_p) + \sum_{(l, l^{\prime}) \in \mathcal{S}_2}\phi_{(l, l^{\prime})} (x_{l}, x_{l^{\prime}}).$$ Assuming $\phi_{p},\phi_{(l, l^{\prime})}$, $\mathcal{S}_1$ and, $\mathcal{S}_2$ to be unknown, we provide a randomized algorithm that queries $f$ and exactly recovers $\mathcal{S}_1,\mathcal{S}_2$.

Additive models

Bayesian Optimization with Safety Constraints: Safe and Automatic Parameter Tuning in Robotics

3 code implementations14 Feb 2016 Felix Berkenkamp, Andreas Krause, Angela P. Schoellig

While an initial guess for the parameters may be obtained from dynamic models of the robot, parameters are usually tuned manually on the real system to achieve the best performance.

Better safe than sorry: Risky function exploitation through safe optimization

no code implementations2 Feb 2016 Eric Schulz, Quentin J. M. Huys, Dominik R. Bach, Maarten Speekenbrink, Andreas Krause

Exploration-exploitation of functions, that is learning and optimizing a mapping between inputs and expected outputs, is ubiquitous to many real world situations.

Sampling from Probabilistic Submodular Models

no code implementations NeurIPS 2015 Alkis Gotovos, Hamed Hassani, Andreas Krause

Submodular and supermodular functions have found wide applicability in machine learning, capturing notions such as diversity and regularity, respectively.

Point Processes

Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization

no code implementations23 Nov 2015 Adish Singla, Sebastian Tschiatschek, Andreas Krause

When the underlying submodular function is unknown, users' feedback can provide noisy evaluations of the function that we seek to maximize.

Safe Controller Optimization for Quadrotors with Gaussian Processes

3 code implementations3 Sep 2015 Felix Berkenkamp, Angela P. Schoellig, Andreas Krause

One of the most fundamental problems when designing controllers for dynamic systems is the tuning of the controller parameters.


Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures

no code implementations21 Aug 2015 Mario Lucic, Olivier Bachem, Andreas Krause

We propose a single, practical algorithm to construct strong coresets for a large class of hard and soft clustering problems based on Bregman divergences.

Learning to Hire Teams

no code implementations12 Aug 2015 Adish Singla, Eric Horvitz, Pushmeet Kohli, Andreas Krause

Furthermore, we consider an embedding of the tasks and workers in an underlying graph that may arise from task similarities or social ties, and that can provide additional side-observations for faster learning.

online learning

Crowd Access Path Optimization: Diversity Matters

no code implementations8 Aug 2015 Besmira Nushi, Adish Singla, Anja Gruenheid, Erfan Zamanian, Andreas Krause, Donald Kossmann

Based on this intuitive idea, we introduce the Access Path Model (APM), a novel crowd model that leverages the notion of access paths as an alternative way of retrieving information.

Discovering Valuable Items from Massive Data

no code implementations2 Jun 2015 Hastagiri P. Vanchinathan, Andreas Marfurt, Charles-Antoine Robelin, Donald Kossmann, Andreas Krause

Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value?

Recommendation Systems

Building Hierarchies of Concepts via Crowdsourcing

no code implementations27 Apr 2015 Yuyin Sun, Adish Singla, Dieter Fox, Andreas Krause

Hierarchies of concepts are useful in many applications from navigation to organization of objects.

Information Gathering in Networks via Active Exploration

no code implementations24 Apr 2015 Adish Singla, Eric Horvitz, Pushmeet Kohli, Ryen White, Andreas Krause

How should we gather information in a network, where each node's visibility is limited to its local neighborhood?

Experimental Design Informativeness +1

Efficient Sampling for Learning Sparse Additive Models in High Dimensions

no code implementations NeurIPS 2014 Hemant Tyagi, Bernd Gärtner, Andreas Krause

We consider the problem of learning sparse additive models, i. e., functions of the form: $f(\vecx) = \sum_{l \in S} \phi_{l}(x_l)$, $\vecx \in \matR^d$ from point queries of $f$.

Additive models Compressive Sensing

Efficient Partial Monitoring with Prior Information

no code implementations NeurIPS 2014 Hastagiri P. Vanchinathan, Gábor Bartók, Andreas Krause

In every round, the learner suffers some loss and receives some feedback based on the action and the outcome.

online learning

Distributed Submodular Maximization

no code implementations3 Nov 2014 Baharan Mirzasoleiman, Amin Karbasi, Rik Sarkar, Andreas Krause

Such problems can often be reduced to maximizing a submodular set function subject to various constraints.

Lazier Than Lazy Greedy

no code implementations28 Sep 2014 Baharan Mirzasoleiman, Ashwinkumar Badanidiyuru, Amin Karbasi, Jan Vondrak, Andreas Krause

Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice?

Data Summarization

Near-Optimally Teaching the Crowd to Classify

no code implementations10 Feb 2014 Adish Singla, Ilija Bogunovic, Gábor Bartók, Amin Karbasi, Andreas Krause

How should we present training examples to learners to teach them classification rules?

A Utility-Theoretic Approach to Privacy in Online Services

no code implementations16 Jan 2014 Andreas Krause, Eric Horvitz

We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service.

Optimal Value of Information in Graphical Models

no code implementations15 Jan 2014 Andreas Krause, Carlos Guestrin

In a sensor network, for example, it is important to select the subset of sensors that is expected to provide the strongest reduction in uncertainty.

Decision Making

Efficient Informative Sensing using Multiple Robots

no code implementations15 Jan 2014 Amarjeet Singh, Andreas Krause, Carlos Guestrin, William J. Kaiser

In this paper, we present an efficient approach for near-optimally solving the NP-hard optimization problem of planning such informative paths.

High-Dimensional Gaussian Process Bandits

no code implementations NeurIPS 2013 Josip Djolonga, Andreas Krause, Volkan Cevher

Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain.

Incentives for Privacy Tradeoff in Community Sensing

no code implementations19 Aug 2013 Adish Singla, Andreas Krause

Community sensing, fusing information from populations of privately-held sensors, presents a great opportunity to create efficient and cost-effective sensing applications.

Scalable Training of Mixture Models via Coresets

no code implementations NeurIPS 2011 Dan Feldman, Matthew Faulkner, Andreas Krause

In this paper, we show how to construct coresets for mixtures of Gaussians and natural generalizations.

Density Estimation

Contextual Gaussian Process Bandit Optimization

no code implementations NeurIPS 2011 Andreas Krause, Cheng S. Ong

How should we design experiments to maximize performance of a complex system, taking into account uncontrollable environmental conditions?

Efficient Minimization of Decomposable Submodular Functions

no code implementations NeurIPS 2010 Peter Stobbe, Andreas Krause

Decomposable submodular functions are those that can be represented as sums of concave functions applied to linear functions.

Near-Optimal Bayesian Active Learning with Noisy Observations

no code implementations NeurIPS 2010 Daniel Golovin, Andreas Krause, Debajyoti Ray

In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perform near-optimally.

Active Learning Experimental Design

Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

no code implementations21 Mar 2010 Daniel Golovin, Andreas Krause

Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge.

Active Learning Stochastic Optimization

Cost-effective Outbreak Detection in Networks

1 code implementation SIGKDD 2007 Jure Leskovec, Andreas Krause, Carlos Guestrin, Christos Faloutsos, Jeanne VanBriesen, Natalie Glance

We show that the approach scales, achieving speedups and savings in storage of several orders of magnitude.

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