Concept

Constraint logic programming

Publications associées (239)

Non-Discriminatory Machine Learning through Convex Fairness Criteria

Boi Faltings, Naman Goel, Mohammad Yaghini

Biased decision making by machine learning systems is increasingly recognized as an important issue. Recently, techniques have been proposed to learn non-discriminatory classifiers by enforcing constraints in the training phase. Such constraints are either ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Learning Monocular 3D Human Pose Estimation from Multi-view Images

Pascal Fua, Mathieu Salzmann, Frédéric Meyer, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

Accurate 3D human pose estimation from single images is possible with sophisticated deep-net architectures that have been trained on very large datasets. However, this still leaves open the problem of capturing motions for which no such database exists. Ma ...

2018

Learning Monocular 3D Human Pose Estimation from Multi-view Images

Pascal Fua, Mathieu Salzmann, Frédéric Meyer, Isinsu Katircioglu, Helge Jochen Rhodin, Victor Constantin

IEEE2018

Real-Time Optimization of Uncertain Process Systems via Modifier Adaptation and Gaussian Processes

Dominique Bonvin, Colin Neil Jones, Timm Faulwasser, Harsh Ambarishkumar Shukla

In the context of static real-time optimization, the use of measurements allows dealing with uncertainty in the form of plant-model mismatch and disturbances. Modifier adaptation (MA) is a measurement-based scheme that uses first-order corrections to the m ...

IEEE2018

Sampling at unknown locations: Uniqueness and reconstruction under constraints

Martin Vetterli, Benjamin Bejar Haro, Adam James Scholefield, Golnooshsadat Elhami, Michalina Wanda Pacholska

Traditional sampling results assume that the sample locations are known. Motivated by simultaneous localization and mapping (SLAM) and structure from motion (SfM), we investigate sampling at unknown locations. Without further constraints, the problem is of ...

2018

Conic Programming Reformulations of Two-Stage Distributionally Robust Linear Programs over Wasserstein Balls

Daniel Kuhn, Grani Adiwena Hanasusanto

Adaptive robust optimization problems are usually solved approximately by restricting the adaptive decisions to simple parametric decision rules. However, the corresponding approximation error can be substantial. In this paper we show that two-stage robust ...

2018

Stochastic Three-Composite Convex Minimization with a Linear Operator

Volkan Cevher

We develop a primal-dual convex minimization framework to solve a class of stochastic convex three-composite problem with a linear operator. We consider the cases where the problem is both convex and strongly convex and analyze the convergence of the propo ...

JMLR: W&CP2018

A Theory of Traffic Regulators for Deterministic Networks with Application to Interleaved Regulators

Jean-Yves Le Boudec

We define the minimal interleaved regulator, which generalizes the Urgency Based Shaper that was recently proposed by Specht and Samii as a simpler alternative to per-flow reshaping in deterministic networks with aggregate scheduling. With this regulator, ...

Arxiv2018

Busy Man’s Synthesis: Combinational Delay Optimization With SAT

Giovanni De Micheli, Mathias Soeken

Boolean SAT solving can be used to find a minimum- size logic network for a given small Boolean function. This paper extends the SAT formulation to find a minimum-size network under delay constraints. Delay constraints are given in terms of input arrival t ...

Ieee2017

Diffusion LMS for Multitask Problems With Local Linear Equality Constraints

Ali H. Sayed, Roula Nassif

We consider distributed multitask learning problems over a network of agents where each agent is interested in estimating its own parameter vector, also called task, and where the tasks at neighboring agents are related according to a set of linear equalit ...

2017

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