activity
20042024
most citedETSformer: Exponential Smoothing Transformers for Time-series Forecasting

111 citations · 279 across the 20 of their papers we have counts for

collaborators

20 papers

cs.CR2024

CloudLens: Modeling and Detecting Cloud Security Vulnerabilities

Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. With their growing popularity, concerns about security vulne…

cs.GT2023

Mean-field games among teams

Jayakumar Subramanian, Akshat Kumar, Aditya Mahajan

In this paper, we present a model of a game among teams. Each team consists of a homogeneous population of agents. Agents within a team are cooperative while the teams compete with…

cs.LG2023

Safe MDP Planning by Learning Temporal Patterns of Undesirable Trajectories and Averting Negative Side Effects

Siow Meng Low, Akshat Kumar, Scott Sanner

In safe MDP planning, a cost function based on the current state and action is often used to specify safety aspects. In the real world, often the state representation used may lack…

quant-ph2022

Shining light on data: Geometric data analysis through quantum dynamics

Akshat Kumar, Mohan Sarovar

Experimental sciences have come to depend heavily on our ability to organize and interpret high-dimensional datasets. Natural laws, conservation principles, and inter-dependencies…

cs.LG2022★ 14 cited

Learning Deep Time-index Models for Time Series Forecasting

Gerald Woo, Chenghao Liu, Doyen Sahoo +2

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. Yet, despite the attracti…

cs.CR2022

Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security Policies

Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Modern software systems rely on mining insights from business sensitive data stored in public clouds. A data breach usually incurs significant (monetary) loss for a commercial orga…