6 citations · 32 across the 16 of their papers we have counts for
18 papers
Efficient Exploration at Scale
Seyed Mohammad Asghari, Chris Chute, Vikranth Dwaracherla +5
We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates rew…
Efficient Exploration for LLMs
Vikranth Dwaracherla, Seyed Mohammad Asghari, Botao Hao +1
We present evidence of substantial benefit from efficient exploration in gathering human feedback to improve large language models. In our experiments, an agent sequentially genera…
Approximate Thompson Sampling via Epistemic Neural Networks
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +4
Thompson sampling (TS) is a popular heuristic for action selection, but it requires sampling from a posterior distribution. Unfortunately, this can become computationally intractab…
Fine-Tuning Language Models via Epistemic Neural Networks
Ian Osband, Seyed Mohammad Asghari, Benjamin Van Roy +3
Language models often pre-train on large unsupervised text corpora, then fine-tune on additional task-specific data. However, typical fine-tuning schemes do not prioritize the exam…
Robustness of Epinets against Distributional Shifts
Xiuyuan Lu, Ian Osband, Seyed Mohammad Asghari +4
Recent work introduced the epinet as a new approach to uncertainty modeling in deep learning. An epinet is a small neural network added to traditional neural networks, which, toget…
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
Vikranth Dwaracherla, Zheng Wen, Ian Osband +3
In machine learning, an agent needs to estimate uncertainty to efficiently explore and adapt and to make effective decisions. A common approach to uncertainty estimation maintains…
Evaluating High-Order Predictive Distributions in Deep Learning
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +3
Most work on supervised learning research has focused on marginal predictions. In decision problems, joint predictive distributions are essential for good performance. Previous wor…
The Neural Testbed: Evaluating Joint Predictions
Ian Osband, Zheng Wen, Seyed Mohammad Asghari +7
Predictive distributions quantify uncertainties ignored by point estimates. This paper introduces The Neural Testbed: an open-source benchmark for controlled and principled evaluat…
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems
Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar
Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt t…
Learning to Code: Coded Caching via Deep Reinforcement Learning
Navid Naderializadeh, Seyed Mohammad Asghari
We consider a system comprising a file library and a network with a server and multiple users equipped with cache memories. The system operates in two phases: a prefetching phase,…
Optimal Infinite Horizon Decentralized Networked Controllers with Unreliable Communication
Yi Ouyang, Seyed Mohammad Asghari, Ashutosh Nayyar
We consider a decentralized networked control system (DNCS) consisting of a remote controller and a collection of linear plants, each associated with a local controller. Each local…
Decentralized Control of Stochastically Switched Linear System with Unreliable Communication
Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar
We consider a networked control system (NCS) consisting of two plants, a global plant and a local plant, and two controllers, a global controller and a local controller. The global…
An Approximation Algorithm for Optimal Clique Cover Delivery in Coded Caching
Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar +1
Coded caching can significantly reduce the communication bandwidth requirement for satisfying users' demands by utilizing the multicasting gain among multiple users. Most existing…
Dynamic Teams and Decentralized Control Problems with Substitutable Actions
Seyed Mohammad Asghari, Ashutosh Nayyar
This paper considers two problems -- a dynamic team problem and a decentralized control problem. The problems we consider do not belong to the known classes of "simpler" dynamic te…
Optimal Local and Remote Controllers with Unreliable Uplink Channels
Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar
We consider a networked control system consisting of a remote controller and a collection of linear plants, each associated with a local controller. Each local controller directly…
Optimal Local and Remote Controllers with Unreliable Communication
Yi Ouyang, Seyed Mohammad Asghari, Ashutosh Nayyar
We consider a decentralized optimal control problem for a linear plant controlled by two controllers, a local controller and a remote controller. The local controller directly obse…
Decentralized Control Problems with Substitutable Actions
Seyed Mohammad Asghari, Ashutosh Nayyar
We consider a decentralized system with multiple controllers and define substitutability of one controller by another in open-loop strategies. We explore the implications of this p…
LORD: Leader-based framework for Resource Discovery in Mobile Device Clouds
Seyed Mohammad Asghari, Yi-Hsuan Kao, Mohammad Hassan Lotfi +4
We provide a novel solution for Resource Discovery (RD) in mobile device clouds consisting of selfish nodes. Mobile device clouds (MDCs) refer to cooperative arrangement of communi…