23 citations · 36 across the 16 of their papers we have counts for
19 papers
A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection
Wuxia Chen, Arman Adibi, Taposh Banerjee +1
A Mixture-of-Experts (MoE) framework is proposed for quickest change detection (QCD) where there are multiple experts, and each expert produces a sequence of statistics for change…
Certified Robustness via Sparse Second-Order Cone Relaxations
Mohammadreza Maleki, Daniel Sadig, Rushendra Sidibomma +3
Certified robustness is critical for deploying neural networks in safety-sensitive settings, but computing exact certificates typically requires solving nonconvex optimization prob…
Nash Equilibrium Text: A Game-Theoretic Decoding Framework for Text Generation
Alireza Jafari, Arman Adibi, Mohammad Ghavamzadeh +1
Text revision has become an integral component of large language models. This paper formulates revision such that it admits a Nash equilibrium: Token positions are players, vocabul…
Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
Arman Adibi, Alireza Jafari, Mohammad Ghavamzadeh +1
A growing body of work establishes that large language models are not mere statistical memorizers, but are capable of in-context learning: performing inference at test time using o…
Tight Sample Bounds for Renyi and Min-Entropy Estimation
Arman Adibi, Piotr Krysta
Estimating entropy from samples is fundamental in information theory and property testing. Shannon entropy measures average uncertainty and can be estimated to constant additive ac…
Convergence of Byzantine-Resilient Gradient Tracking via Probabilistic Edge Dropout
Amirhossein Dezhboro, Fateme Maleki, Arman Adibi +2
We study distributed optimization over networks with Byzantine agents that may send arbitrary adversarial messages. We propose \emph{Gradient Tracking with Probabilistic Edge Dropo…
Cascading Robustness Verification: Toward Efficient Model-Agnostic Certification
Mohammadreza Maleki, Rushendra Sidibomma, Arman Adibi +1
Certifying neural network robustness against adversarial examples is challenging, as formal guarantees often require solving non-convex problems. Hence, incomplete verifiers are wi…
Asymptotically Optimal Change Detection for Unnormalized Pre- and Post-Change Distributions
Arman Adibi, Sanjeev Kulkarni, H. Vincent Poor +2
This paper addresses the problem of detecting changes when only unnormalized pre- and post-change distributions are accessible. This situation happens in many scenarios in physics…
Finite-Time Analysis of Asynchronous Multi-Agent TD Learning
Nicolò Dal Fabbro, Arman Adibi, Aritra Mitra +1
Recent research endeavours have theoretically shown the beneficial effect of cooperation in multi-agent reinforcement learning (MARL). In a setting involving agents, this benef…
Stochastic Approximation with Delayed Updates: Finite-Time Rates under Markovian Sampling
Arman Adibi, Nicolo Dal Fabbro, Luca Schenato +5
Motivated by applications in large-scale and multi-agent reinforcement learning, we study the non-asymptotic performance of stochastic approximation (SA) schemes with delayed updat…
DASA: Delay-Adaptive Multi-Agent Stochastic Approximation
Nicolò Dal Fabbro, Arman Adibi, H. Vincent Poor +3
We consider a setting in which agents aim to speedup a common Stochastic Approximation (SA) problem by acting in parallel and communicating with a central server. We assume tha…
Score-Based Methods for Discrete Optimization in Deep Learning
Eric Lei, Arman Adibi, Hamed Hassani
Discrete optimization problems often arise in deep learning tasks, despite the fact that neural networks typically operate on continuous data. One class of these problems involve o…
Min-Max Optimization under Delays
Arman Adibi, Aritra Mitra, Hamed Hassani
Delays and asynchrony are inevitable in large-scale machine-learning problems where communication plays a key role. As such, several works have extensively analyzed stochastic opti…
Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds
Aritra Mitra, Arman Adibi, George J. Pappas +1
We consider a linear stochastic bandit problem involving agents that can collaborate via a central server to minimize regret. A fraction of these agents are adversarial and…
Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents
Arman Adibi, Aritra Mitra, George J. Pappas +1
Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control…
Minimax Optimization: The Case of Convex-Submodular
Arman Adibi, Aryan Mokhtari, Hamed Hassani
Minimax optimization has been central in addressing various applications in machine learning, game theory, and control theory. Prior literature has thus far mainly focused on study…
Submodular Meta-Learning
Arman Adibi, Aryan Mokhtari, Hamed Hassani
In this paper, we introduce a discrete variant of the meta-learning framework. Meta-learning aims at exploiting prior experience and data to improve performance on future tasks. By…
Optimal Algorithms for Submodular Maximization with Distributed Constraints
Alexander Robey, Arman Adibi, Brent Schlotfeldt +2
We consider a class of discrete optimization problems that aim to maximize a submodular objective function subject to a distributed partition matroid constraint. More precisely, we…
Max-Min Fairness Design for MIMO Interference Channels: a Minorization-Maximization Approach
Mohammad Mahdi Naghsh, Maryam Masjedi, Arman Adibi +1
We address the problem of linear precoder (beamformer) design in a multiple-input multiple-output interference channel (MIMO-IC). The aim is to design the transmit covariance matri…