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Found 13 papers, 1 papers with code
Elucidating Discrepancy in Explanations of Predictive Models Developed using EMR
The lack of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms.
Online Long-run Constrained Optimization
In this paper, a novel Follow-the-Perturbed-Leader type algorithm is proposed and analyzed for solving general long-term constrained optimization problems in online manner, where the objective and constraints are not necessarily convex.
Distributional Reinforcement Learning with Online Risk-awareness Adaption
The use of reinforcement learning (RL) in practical applications requires considering sub-optimal outcomes, which depend on the agent's familiarity with the uncertain environment.
Distributional Reinforcement Learning
reinforcement-learning
+1
Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength
Therefore, we firstly generate a dataset with 45, 000 numerical simulations and 900 particle types to facilitate the research progress of machine learning for particle crushing.
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?
The absence of transparency and explainability hinders the clinical adoption of Machine learning (ML) algorithms.
Message-passing selection: Towards interpretable GNNs for graph classification
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
Waveform Design for Mutual Interference Mitigation in Automotive Radar
The mutual interference between similar radar systems can result in reduced radar sensitivity and increased false alarm rates.
Preference Robust Optimization with Quasi-Concave Choice Functions for Multi-Attribute Prospects
Preference robust choice models concern decision-making problems where the decision maker's (DM) utility/risk preferences are ambiguous and the evaluation is based on the worst-case utility function/risk measure from a set of plausible utility functions/risk measures.
Online non-convex learning for river pollution source identification
In this paper, novel gradient-based online learning algorithms are developed to investigate an important environmental application: real-time river pollution source identification, which aims at estimating the released mass, location, and time of a river pollution source based on downstream sensor data monitoring the pollution concentration.
Data-driven satisficing measure and ranking
We propose an computational framework for real-time risk assessment and prioritizing for random outcomes without prior information on probability distributions.
Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions
Decision maker's preferences are often captured by some choice functions which are used to rank prospects.
Stochastic Approximation for Risk-aware Markov Decision Processes
The inner loop computes the risk by solving a stochastic saddle-point problem.
Randomized Smoothing SVRG for Large-scale Nonsmooth Convex Optimization
In this paper, we consider the problem of minimizing the average of a large number of nonsmooth and convex functions.
