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Nearly optimal exploration-exploitation decision thresholds
Related publications (33)
When Stuck, Flip a Coin
Many modern services need to routinely perform tasks on a large scale. This prompts us to consider the following question:
How can we design efficient algorithms for large-scale computation?
In this thesis, we focus on devising a general strategy to addr ...
EPFL2018Asynchronous updates for stochastic gradient descent
Finding convergence rates for numerical optimization algorithms is an important task, because it gives a justification to their use in solving practical problems, while also providing a way to compare their efficiency. This is especially useful in an async ...
2017From Acoustic Room Reconstruction to SLAM
Martin Vetterli, Ivan Dokmanic
Recent works on reconstruction of room geometry from echoes assume that the geometry of the sensor array is known. In this paper, we show that such an assumption is not essential; echoes provide sufficient clues to reconstruct the room’s and the array’s ge ...
Ieee2016Centrality of trees for capacitated -center
Ola Nils Anders Svensson, Hyung Chan An, Aditya Bhaskara
We consider the capacitated -center problem. In this problem we are given a finite set of locations in a metric space and each location has an associated non-negative integer capacity. The goal is to choose (open) locations (called centers) and assign each ...
Springer Heidelberg2015Source Localization and Tracking in Non-Convex Rooms
Martin Vetterli, Ivan Dokmanic, Orhan Öçal
We consider the estimation of the acoustic source position in a known room from recordings by a microphone array. We propose an algorithm that does not require the room to be convex, nor a line-of-sight path between the microphone array and the source to b ...
Ieee2014Centrality of Trees for Capacitated k-Center
Ola Nils Anders Svensson, Hyung Chan An, Aditya Bhaskara
We consider the capacitated k-center problem. In this problem we are given a finite set of locations in a metric space and each location has an associated non-negative integer capacity. The goal is to choose (open) k locations (called centers) and assign e ...
Springer2014Probabilistic inverse reinforcement learning in unknown environments
We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to sol ...
2013Coordination and Sampling in Distributed Constraint Optimization
The Distributed Constraint Optimization (DCOP) framework can be used to model a wide range of optimization problems that are inherently distributed. A distributed optimization problem can be viewed as a problem distributed over a set of agents, where agent ...
EPFL2012Knowledge-based design space exploration of wireless sensor networks
David Atienza Alonso, Ivan Beretta
The complexity of Wireless Sensor Networks (WSNs) has been constantly increasing over the last decade, and the necessity of efficient CAD tools has been growing accordingly. In fact, the size of the design space of a WSN has become large, and an exploratio ...
ACM Press2012Vector Network Coding
Christina Fragouli, Javad Ebrahimi Boroojeni
We develop new algebraic algorithms for scalar and vector network coding. In vector network coding, the source multicasts information by transmitting vectors of length L, while intermediate nodes process and combine their incoming packets by multiplying th ...
2010