75 citations · 820 across the 81 of their papers we have counts for
6 papers · 1 filter
LQG for portfolio optimization
M. Abeille, E. Serie, A. Lazaric +1
We introduce a generic solver for dynamic portfolio allocation problems when the market exhibits return predictability, price impact and partial observability. We assume that the p…
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Reinforcement learning (RL) in Markov decision processes (MDPs) with large state spaces is a challenging problem. The performance of standard RL algorithms degrades drastically wit…
Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting
Daniele Calandriello, Alessandro Lazaric, Michal Valko
We derive a new proof to show that the incremental resparsification algorithm proposed by Kelner and Levin (2013) produces a spectral sparsifier in high probability. We rigorously…
Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Planning plays an important role in the broad class of decision theory. Planning has drawn much attention in recent work in the robotics and sequential decision making areas. Recen…
Reinforcement Learning of POMDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have be…
Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning
Daniele Calandriello, Alessandro Lazaric, Michal Valko +1
While the harmonic function solution performs well in many semi-supervised learning (SSL) tasks, it is known to scale poorly with the number of samples. Recent successful and scala…