A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
New approach uses contrastive learning for better wireless power control.
This paper optimizes a power-to-heat system using reinforcement learning for cost minimization under uncertain conditions.
Decision-calibrated prediction sets improve power system operations by reducing unnecessary costs.
Optimizes wireless power control using graph neural networks and counterfactual optimization.
DNN policies improve stochastic AC OPF for power grid optimization.
New method controls false discoveries in real-time data streams.
Survey of RL methods for optimizing power grid topologies.
In this paper, we develop a multi-agent reinforcement learning (MARL) framework to obtain online power control policies for a large energy harvesting (EH) multiple access channel, when only causal information about the EH process and wireless channel is available. In the proposed framework, we model the online power co…
Machine learning techniques have been used in the past using Monte Carlo samples to construct predictors of the dynamic stability of power systems. In this paper we move beyond the task of prediction and propose a comprehensive approach to use predictors, such as Decision Trees (DT), within a standard optimization fram…
GAIF enhances online multiple testing with feedback, improving statistical power.
Partially observable Markov decision processes (POMDPs) with continuous state and observation spaces have powerful flexibility for representing real-world decision and control problems but are notoriously difficult to solve. Recent online sampling-based algorithms that use observation likelihood weighting have shown un…
A/B testing improves marketing decisions by selecting effective stratification variables.
dtControl uses decision trees to represent controllers efficiently and explainably.
Finite resources limit false discovery rate control in structured hypothesis spaces.
Novel method improves load estimation in power grids using anomaly and change point detection.
The framework of reinforcement learning or optimal control provides a mathematical formalization of intelligent decision making that is powerful and broadly applicable. While the general form of the reinforcement learning problem enables effective reasoning about uncertainty, the connection between reinforcement learni…
ACS is an interactive framework for model-free selection with guaranteed error control.
Paper robustifies reinforcement learning agents against action space perturbations.
Unified framework connects reinforcement learning and optimal control.
One important partition of algorithms for controlling the false discovery rate (FDR) in multiple testing is into offline and online algorithms. The first generally achieve significantly higher power of discovery, while the latter allow making decisions sequentially as well as adaptively formulating hypotheses based on …
Paper proposes a risk-aware decision-making framework for real-world sequential decisions.
In this paper, we propose a decision making algorithm intended for automated vehicles that negotiate with other possibly non-automated vehicles in intersections. The decision algorithm is separated into two parts: a high-level decision module based on reinforcement learning, and a low-level planning module based on mod…
RL applied to TCLs for power consumption control.
A new algorithm finds optimal solutions for constrained decision processes.
New algorithm learns coordinated decisions in loosely-coupled multi-agent systems.
Deep RL improves power control and scheduling for wireless multicast systems.
Most of the current game-theoretic demand-side management methods focus primarily on the scheduling of home appliances, and the related numerical experiments are analyzed under various scenarios to achieve the corresponding Nash-equilibrium (NE) and optimal results. However, not much work is conducted for academic or c…
In this paper, we propose a new perspective for quantizing a signal and more specifically the channel state information (CSI). The proposed point of view is fully relevant for a receiver which has to send a quantized version of the channel state to the transmitter. Roughly, the key idea is that the receiver sends the r…
We consider a multicast scheme recently proposed for a wireless downlink in [1]. It was shown earlier that power control can significantly improve its performance. However for this system, obtaining optimal power control is intractable because of a very large state space. Therefore in this paper we use deep reinforceme…
Partially observable Markov decision processes (POMDPs) are a powerful abstraction for tasks that require decision making under uncertainty, and capture a wide range of real world tasks. Today, effective planning approaches exist that generate effective strategies given black-box models of a POMDP task. Yet, an open qu…
Deep actor-critic learning optimizes power control in mobile networks.
New framework uses OR to ensure AI systems make safe decisions.
This paper considers a transmission control problem in network-coded two-way relay channels (NC-TWRC), where the relay buffers random symbol arrivals from two users, and the channels are assumed to be fading. The problem is modeled by a discounted infinite horizon Markov decision process (MDP). The objective is to find…
In high-dimensional classification settings, we wish to seek a balance between high power and ensuring control over a desired loss function. In many settings, the points most likely to be misclassified are those who lie near the decision boundary of the given classification method. Often, these uninformative points sho…
By leveraging the concept of mobile edge computing (MEC), massive amount of data generated by a large number of Internet of Things (IoT) devices could be offloaded to MEC server at the edge of wireless network for further computational intensive processing. However, due to the resource constraint of IoT devices and wir…
Sparse oblique decision tree improves security rules for renewable power systems.
The paper improves decision tree stability for health care applications.
Unified framework uses all data to improve multiple testing efficiency.
This work develops a novel power control framework for energy-efficient power control in wireless networks. The proposed method is a new branch-and-bound procedure based on problem-specific bounds for energy-efficiency maximization that allow for faster convergence. This enables to find the global solution for all of t…
Transforms any test into anytime-valid with sample savings.
Paper proposes a DRL-based controller for networked AP systems that reduces communication frequency.
A deep neural network (DNN) based power control method is proposed, which aims at solving the non-convex optimization problem of maximizing the sum rate of a multi-user interference channel. Towards this end, we first present PCNet, which is a multi-layer fully connected neural network that is specifically designed for…
New algorithms control FDX while achieving more power in online multiple testing.
Paper tackles risk-sensitive decision-making under uncertainty.
A predictor improves power grid frequency forecasts up to one hour.
We present a unified method, based on convex optimization, for managing the power produced and consumed by a network of devices over time. We start with the simple setting of optimizing power flows in a static network, and then proceed to the case of optimizing dynamic power flows, i.e., power flows that change with ti…
Pronounced variability due to the growth of renewable energy sources, flexible loads, and distributed generation is challenging residential distribution systems. This context, motivates well fast, efficient, and robust reactive power control. Real-time optimal reactive power control is possible in theory by solving a n…
New method controls false discoveries in online testing with deadlines.