1 citations · 1 across the 7 of their papers we have counts for
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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…
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…
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…
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…
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…