Search Results for author: Daniel Kuhn

Found 32 papers, 15 papers with code

Reliable Frequency Regulation through Vehicle-to-Grid: Encoding Legislation with Robust Constraints

1 code implementation12 May 2020 Dirk Lauinger, François Vuille, Daniel Kuhn

We formulate a robust optimization problem that maximizes a vehicle owner's expected profit from selling primary frequency regulation to the grid and guarantees that market commitments are met at all times for all frequency deviation trajectories in a functional uncertainty set that encodes applicable legislation.

Optimization and Control

Regularization via Mass Transportation

1 code implementation27 Oct 2017 Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution.

Generalization Bounds

Context-enriched molecule representations improve few-shot drug discovery

1 code implementation24 Apr 2023 Johannes Schimunek, Philipp Seidl, Lukas Friedrich, Daniel Kuhn, Friedrich Rippmann, Sepp Hochreiter, Günter Klambauer

Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network.

Drug Discovery Few-Shot Learning

Wasserstein Distributionally Robust Kalman Filtering

1 code implementation NeurIPS 2018 Soroosh Shafieezadeh-Abadeh, Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani

Despite the non-convex nature of the ambiguity set, we prove that the estimation problem is equivalent to a tractable convex program.

Stability Verification of Neural Network Controllers using Mixed-Integer Programming

1 code implementation27 Jun 2022 Roland Schwan, Colin N. Jones, Daniel Kuhn

We provide sufficient conditions for the closed-loop stability of the candidate policy in terms of the worst-case approximation error with respect to the baseline policy, and we show that these conditions can be checked by solving a Mixed-Integer Quadratic Program (MIQP).

Model Predictive Control

New Perspectives on Regularization and Computation in Optimal Transport-Based Distributionally Robust Optimization

1 code implementation7 Mar 2023 Soroosh Shafieezadeh-Abadeh, Liviu Aolaritei, Florian Dörfler, Daniel Kuhn

We study optimal transport-based distributionally robust optimization problems where a fictitious adversary, often envisioned as nature, can choose the distribution of the uncertain problem parameters by reshaping a prescribed reference distribution at a finite transportation cost.

Semi-Discrete Optimal Transport: Hardness, Regularization and Numerical Solution

1 code implementation10 Mar 2021 Bahar Taskesen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn

Semi-discrete optimal transport problems, which evaluate the Wasserstein distance between a discrete and a generic (possibly non-discrete) probability measure, are believed to be computationally hard.

Discrete Choice Models

Sequential Domain Adaptation by Synthesizing Distributionally Robust Experts

1 code implementation1 Jun 2021 Bahar Taskesen, Man-Chung Yue, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen

Given available data, we investigate novel strategies to synthesize a family of least squares estimator experts that are robust with regard to moment conditions.

Domain Adaptation

Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization

1 code implementation NeurIPS 2019 Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann

A fundamental problem arising in many areas of machine learning is the evaluation of the likelihood of a given observation under different nominal distributions.

Distributionally Robust Optimization with Markovian Data

1 code implementation12 Jun 2021 Mengmeng Li, Tobias Sutter, Daniel Kuhn

We study a stochastic program where the probability distribution of the uncertain problem parameters is unknown and only indirectly observed via finitely many correlated samples generated by an unknown Markov chain with $d$ states.

Dimensionality Reduction

Bridging Bayesian and Minimax Mean Square Error Estimation via Wasserstein Distributionally Robust Optimization

1 code implementation8 Nov 2019 Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani

The proposed model can be viewed as a zero-sum game between a statistician choosing an estimator -- that is, a measurable function of the observation -- and a fictitious adversary choosing a prior -- that is, a pair of signal and noise distributions ranging over independent Wasserstein balls -- with the goal to minimize and maximize the expected squared estimation error, respectively.

Metrizing Fairness

1 code implementation30 May 2022 Yves Rychener, Bahar Taskesen, Daniel Kuhn

This means that the distributions of the predictions within the two groups should be close with respect to the Kolmogorov distance, and fairness is achieved by penalizing the dissimilarity of these two distributions in the objective function of the learning problem.

Fairness

Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator

no code implementations18 May 2018 Viet Anh Nguyen, Daniel Kuhn, Peyman Mohajerin Esfahani

We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a $p$-dimensional Gaussian random vector from $n$ independent samples.

Size Matters: Cardinality-Constrained Clustering and Outlier Detection via Conic Optimization

no code implementations22 May 2017 Napat Rujeerapaiboon, Kilian Schindler, Daniel Kuhn, Wolfram Wiesemann

Plain vanilla K-means clustering has proven to be successful in practice, yet it suffers from outlier sensitivity and may produce highly unbalanced clusters.

Constrained Clustering Outlier Detection

Robust Data-Driven Dynamic Programming

no code implementations NeurIPS 2013 Grani Adiwena Hanasusanto, Daniel Kuhn

In stochastic optimal control the distribution of the exogenous noise is typically unknown and must be inferred from limited data before dynamic programming (DP)-based solution schemes can be applied.

RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm for Continuous Control of Nonlinear Dynamical Systems

no code implementations7 Mar 2019 Ekaterina Abramova, Luke Dickens, Daniel Kuhn, Aldo Faisal

We show that a small number of locally optimal linear controllers are able to solve global nonlinear control problems with unknown dynamics when combined with a reinforcement learner in this hierarchical framework.

Continuous Control Hierarchical Reinforcement Learning +3

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

no code implementations23 Aug 2019 Daniel Kuhn, Peyman Mohajerin Esfahani, Viet Anh Nguyen, Soroosh Shafieezadeh-Abadeh

The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples.

BIG-bench Machine Learning Decision Making

On Linear Optimization over Wasserstein Balls

no code implementations15 Apr 2020 Man-Chung Yue, Daniel Kuhn, Wolfram Wiesemann

In this technical note we prove that the Wasserstein ball is weakly compact under mild conditions, and we offer necessary and sufficient conditions for the existence of optimal solutions.

A Distributionally Robust Approach to Fair Classification

no code implementations18 Jul 2020 Bahar Taskesen, Viet Anh Nguyen, Daniel Kuhn, Jose Blanchet

We propose a distributionally robust logistic regression model with an unfairness penalty that prevents discrimination with respect to sensitive attributes such as gender or ethnicity.

Classification Fairness +3

A Statistical Test for Probabilistic Fairness

no code implementations9 Dec 2020 Bahar Taskesen, Jose Blanchet, Daniel Kuhn, Viet Anh Nguyen

Leveraging the geometry of the feature space, the test statistic quantifies the distance of the empirical distribution supported on the test samples to the manifold of distributions that render a pre-trained classifier fair.

BIG-bench Machine Learning Fairness

Topological Linear System Identification via Moderate Deviations Theory

no code implementations5 Mar 2021 Wouter Jongeneel, Tobias Sutter, Daniel Kuhn

Two dynamical systems are topologically equivalent when their phase-portraits can be morphed into each other by a homeomorphic coordinate transformation on the state space.

Optimization and Control

Small errors in random zeroth-order optimization are imaginary

no code implementations9 Mar 2021 Wouter Jongeneel, Man-Chung Yue, Daniel Kuhn

Most zeroth-order optimization algorithms mimic a first-order algorithm but replace the gradient of the objective function with some gradient estimator that can be computed from a small number of function evaluations.

Optimization and Control 65D25, 65G50, 65K05, 65Y04, 65Y20, 90C56

Mean-Covariance Robust Risk Measurement

no code implementations18 Dec 2021 Viet Anh Nguyen, Soroosh Shafiee, Damir Filipović, Daniel Kuhn

We introduce a universal framework for mean-covariance robust risk measurement and portfolio optimization.

Portfolio Optimization

Discrete Optimal Transport with Independent Marginals is #P-Hard

no code implementations2 Mar 2022 Bahar Taşkesen, Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Karthik Natarajan

We study the computational complexity of the optimal transport problem that evaluates the Wasserstein distance between the distributions of two K-dimensional discrete random vectors.

Distributionally Robust Optimal Allocation with Costly Verification

no code implementations28 Nov 2022 Halil İbrahim Bayrak, Çağıl Koçyiğit, Daniel Kuhn, Mustafa Çelebi Pınar

However, this result relies on the unrealistic assumptions that the agents' types follow known independent probability distributions.

Policy Gradient Algorithms for Robust MDPs with Non-Rectangular Uncertainty Sets

no code implementations30 May 2023 Mengmeng Li, Daniel Kuhn, Tobias Sutter

We propose policy gradient algorithms for robust infinite-horizon Markov decision processes (MDPs) with non-rectangular uncertainty sets, thereby addressing an open challenge in the robust MDP literature.

Unifying Distributionally Robust Optimization via Optimal Transport Theory

no code implementations10 Aug 2023 Jose Blanchet, Daniel Kuhn, Jiajin Li, Bahar Taskesen

In the past few years, there has been considerable interest in two prominent approaches for Distributionally Robust Optimization (DRO): Divergence-based and Wasserstein-based methods.

A Large Deviations Perspective on Policy Gradient Algorithms

no code implementations13 Nov 2023 Wouter Jongeneel, Mengmeng Li, Daniel Kuhn

Motivated by policy gradient methods in the context of reinforcement learning, we derive the first large deviation rate function for the iterates generated by stochastic gradient descent for possibly non-convex objectives satisfying a Polyak-Lojasiewicz condition.

Policy Gradient Methods reinforcement-learning

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