9 citations · 11 across the 4 of their papers we have counts for
6 papers
Density-Ratio Losses for Post-Hoc Learning to Defer
Alexander Soen, Ragnar Thobaben, Joakim Jaldén +1
We study post-hoc Learning to Defer (L2D) through the lens of ideal distributions: divergence-regularized reweightings of the data distribution under which a model attains low loss…
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation
Vidit Saxena, Hugo Tullberg, Joakim Jaldén
Wireless links adapt the data transmission parameters to the dynamic channel state -- this is called link adaptation. Classical link adaptation relies on tuning parameters that are…
Deep unfolding of the weighted MMSE beamforming algorithm
Lissy Pellaco, Mats Bengtsson, Joakim Jaldén
Downlink beamforming is a key technology for cellular networks. However, computing the transmit beamformer that maximizes the weighted sum rate subject to a power constraint is an…
Thompson Sampling for Linearly Constrained Bandits
Vidit Saxena, Joseph E. Gonzalez, Joakim Jaldén
We address multi-armed bandits (MAB) where the objective is to maximize the cumulative reward under a probabilistic linear constraint. For a few real-world instances of this proble…
Spectrum Prediction and Interference Detection for Satellite Communications
Lissy Pellaco, Nirankar Singh, Joakim Jaldén
Spectrum monitoring and interference detection are crucial for the satellite service performance and the revenue of SatCom operators. Interference is one of the major causes of ser…
A geometrically converging dual method for distributed optimization over time-varying graphs
Marie Maros, Joakim Jaldén
In this paper we consider a distributed convex optimization problem over time-varying undirected networks. We propose a dual method, primarily averaged network dual ascent (PANDA),…