3 citations · 6 across the 8 of their papers we have counts for
10 papers
Prediction-Guided Control in Data Center Networks
Kevin Zhao, Chenning Li, Anton A. Zabreyko +7
In this paper, we design, implement, and evaluate Polyphony, a system to give network operators a new way to control and reduce the frequency of poor tail latency events in multi-c…
Glia: A Human-Inspired AI for Automated Systems Design and Optimization
Pouya Hamadanian, Pantea Karimi, Arash Nasr-Esfahany +5
Can AI autonomously design mechanisms for computer systems on par with the creativity and reasoning of human experts? We present Glia, an AI architecture for networked systems desi…
Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion
Arash Nasr-Esfahany, Mohammad Alizadeh, Victor Lee +9
Cycle-level simulators such as gem5 are widely used in microarchitecture design, but they are prohibitively slow for large-scale design space explorations. We present Concorde, a n…
m4: A Learned Flow-level Network Simulator
Chenning Li, Anton A. Zabreyko, Arash Nasr-Esfahany +4
Flow-level simulation is widely used to model large-scale data center networks due to its scalability. Unlike packet-level simulators that model individual packets, flow-level simu…
Direct Alignment with Heterogeneous Preferences
Ali Shirali, Arash Nasr-Esfahany, Abdullah Alomar +3
Alignment with human preferences is commonly framed using a universal reward function, even though human preferences are inherently heterogeneous. We formalize this heterogeneity b…
Counterfactual Identifiability of Bijective Causal Models
Arash Nasr-Esfahany, Mohammad Alizadeh, Devavrat Shah
We study counterfactual identifiability in causal models with bijective generation mechanisms (BGM), a class that generalizes several widely-used causal models in the literature. W…
Online Reinforcement Learning in Non-Stationary Context-Driven Environments
Pouya Hamadanian, Arash Nasr-Esfahany, Malte Schwarzkopf +2
We study online reinforcement learning (RL) in non-stationary environments, where a time-varying exogenous context process affects the environment dynamics. Online RL is challengin…
Counterfactual (Non-)identifiability of Learned Structural Causal Models
Arash Nasr-Esfahany, Emre Kiciman
Recent advances in probabilistic generative modeling have motivated learning Structural Causal Models (SCM) from observational datasets using deep conditional generative models, al…
CausalSim: A Causal Framework for Unbiased Trace-Driven Simulation
Abdullah Alomar, Pouya Hamadanian, Arash Nasr-Esfahany +3
We present CausalSim, a causal framework for unbiased trace-driven simulation. Current trace-driven simulators assume that the interventions being simulated (e.g., a new algorithm)…
Real-Time Video Inference on Edge Devices via Adaptive Model Streaming
Mehrdad Khani, Pouya Hamadanian, Arash Nasr-Esfahany +1
Real-time video inference on edge devices like mobile phones and drones is challenging due to the high computation cost of Deep Neural Networks. We present Adaptive Model Streaming…