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Found 19 papers, 8 papers with code
Leveraging AI Planning For Detecting Cloud Security Vulnerabilities
A planner can then generate attacks to identify such vulnerabilities in the cloud.
FlowPG: Action-constrained Policy Gradient with Normalizing Flows
To address this, first we use a normalizing flow model to learn an invertible, differentiable mapping between the feasible action space and the support of a simple distribution on a latent variable, such as Gaussian.
Unified Training of Universal Time Series Forecasting Transformers
Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models.
Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain
Time series has been left behind in the era of pre-training and transfer learning.
Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects
In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects.
Shining light on data: Geometric data analysis through quantum dynamics
Experimental sciences have come to depend heavily on our ability to organize and interpret high-dimensional datasets.
Learning Deep Time-index Models for Time Series Forecasting
Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models.
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies
Modern software systems rely on mining insights from business sensitive data stored in public clouds.
Sample-efficient Iterative Lower Bound Optimization of Deep Reactive Policies for Planning in Continuous MDPs
This novel formulation of DRP learning as iterative lower bound optimization (ILBO) is particularly appealing because (i) each step is structurally easier to optimize than the overall objective, (ii) it guarantees a monotonically improving objective under certain theoretical conditions, and (iii) it reuses samples between iterations thus lowering sample complexity.
InfraredTags: Embedding Invisible AR Markers and Barcodes Using Low-Cost, Infrared-Based 3D Printing and Imaging Tools
We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects, and detected rapidly by low-cost near-infrared cameras.
CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting
Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step -- we justify such a paradigm from a causal perspective.
ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
Transformers have been actively studied for time-series forecasting in recent years.
Manifold learning via quantum dynamics
We introduce an algorithm for computing geodesics on sampled manifolds that relies on simulation of quantum dynamics on a graph embedding of the sampled data.
Combining Propositional Logic Based Decision Diagrams with Decision Making in Urban Systems
Solving multiagent problems can be an uphill task due to uncertainty in the environment, partial observability, and scalability of the problem at hand.
Resource Constrained Deep Reinforcement Learning
However, existing Deep RL methods are unable to handle combinatorial action spaces and constraints on allocation of resources.
Credit Assignment For Collective Multiagent RL With Global Rewards
Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment.
Policy Gradient With Value Function Approximation For Collective Multiagent Planning
Decentralized (PO)MDPs provide an expressive framework for sequential decision making in a multiagent system.
Robust Optimization for Tree-Structured Stochastic Network Design
We therefore address the robust river network design problem where the goal is to optimize river connectivity for fish movement by removing barriers.
MAP Estimation for Graphical Models by Likelihood Maximization
We experiment on the real-world protein design dataset and show that EM's convergence rate is significantly higher than the previous LP relaxation based approach MPLP.
