Search Results for author:
Found 23 papers, 9 papers with code
Pure Exploration with Feedback Graphs
We study the sample complexity of pure exploration in an online learning problem with a feedback graph.
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation
We study the policy evaluation problem in an online multi-reward multi-policy discounted setting, where multiple reward functions must be evaluated simultaneously for different policies.
Achieving $\widetilde{\mathcal{O}}(\sqrt{T})$ Regret in Average-Reward POMDPs with Known Observation Models
We tackle average-reward infinite-horizon POMDPs with an unknown transition model but a known observation model, a setting that has been previously addressed in two limiting ways: (i) frequentist methods relying on suboptimal stochastic policies having a minimum probability of choosing each action, and (ii) Bayesian approaches employing the optimal policy class but requiring strong assumptions about the consistency of employed estimators.
Explainable Reinforcement Learning via Temporal Policy Decomposition
We present Temporal Policy Decomposition (TPD), a novel explainability approach that explains individual RL actions in terms of their Expected Future Outcome (EFO).
Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation
Link adaptation (LA) is an essential function in modern wireless communication systems that dynamically adjusts the transmission rate of a communication link to match time- and frequency-varying radio link conditions.
Efficient Learning of POMDPs with Known Observation Model in Average-Reward Setting
For each episode, the optimal belief-based policy of the estimated POMDP interacts with the environment and collects samples that will be used in the next episode by the OAS estimation procedure to compute a new estimate of the POMDP parameters.
Fair Best Arm Identification with Fixed Confidence
For this setting, we establish an instance-specific sample complexity lower bound and analyze the \textit{price of fairness}, quantifying how fairness impacts sample complexity.
Model-Free Active Exploration in Reinforcement Learning
We study the problem of exploration in Reinforcement Learning and present a novel model-free solution.
Constrained Deep Reinforcement Learning for Fronthaul Compression Optimization
In the Centralized-Radio Access Network (C-RAN) architecture, functions can be placed in the central or distributed locations.
What influences occupants' behavior in residential buildings: An experimental study on window operation in the KTH Live-In Lab
Window-opening and window-closing behaviors play an important role in indoor environmental conditions and therefore have an impact on building energy efficiency.
Conformal Off-Policy Evaluation in Markov Decision Processes
Reinforcement Learning aims at identifying and evaluating efficient control policies from data.
On the Sample Complexity of Representation Learning in Multi-task Bandits with Global and Local structure
Arms consist of two components: one that is shared across tasks (that we call representation) and one that is task-specific (that we call predictor).
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems
In recent years, there has been a growing interest in the effects of data poisoning attacks on data-driven control methods.
Model Based Residual Policy Learning with Applications to Antenna Control
Non-differentiable controllers and rule-based policies are widely used for controlling real systems such as telecommunication networks and robots.
Self-Tuning Tube-based Model Predictive Control
We present Self-Tuning Tube-based Model Predictive Control (STT-MPC), an adaptive robust control algorithm for uncertain linear systems with additive disturbances based on the least-squares estimator and polytopic tubes.
Tube-Based Zonotopic Data-Driven Predictive Control
We present a novel tube-based data-driven predictive control method for linear systems affected by a bounded addictive disturbance.
Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning
Importance Sampling (IS) is a widely used building block for a large variety of off-policy estimation and learning algorithms.
Balancing detectability and performance of attacks on the control channel of Markov Decision Processes
In such an attack, drawing inspiration from adversarial examples used in supervised learning, the amplitude of the adversarial perturbation is limited according to some norm, with the hope that this constraint will make the attack imperceptible.
Some Ethical Issues in the Review Process of Machine Learning Conferences
Recent successes in the Machine Learning community have led to a steep increase in the number of papers submitted to conferences.
Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study
This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques.
Poisoning Attacks against Data-Driven Control Methods
This paper investigates poisoning attacks against data-driven control methods.
Minimizing Information Leakage of Abrupt Changes in Stochastic Systems
In contrast to previous work on privacy, we study the problem for an online sequence of data.
Optimal Attacks on Reinforcement Learning Policies
Finally, we show that from the main agent perspective, the system uncertainties and the attacker can be modeled as a Partially Observable Markov Decision Process.
