activity
20202026
most citedFlexible Multiple-Objective Reinforcement Learning for Chip Placement

3 citations · 5 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL2026

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

Avyav Kumar Singh, Yen-Chen Wu, Alexandru Cioba +2

Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains a largely unsolved problem.…

cs.LG2025

Reinforcement Learning Using known Invariances

Alexandru Cioba, Aya Kayal, Laura Toni +2

In many real-world reinforcement learning (RL) problems, the environment exhibits inherent symmetries that can be exploited to improve learning efficiency. This paper develops a th…

cs.LG2024

Exact, Tractable Gauss-Newton Optimization in Deep Reversible Architectures Reveal Poor Generalization

Davide Buffelli, Jamie McGowan, Wangkun Xu +4

Second-order optimization has been shown to accelerate the training of deep neural networks in many applications, often yielding faster progress per iteration on the training loss…

cs.LG2024

Sample-efficient Bayesian Optimisation Using Known Invariances

Theodore Brown, Alexandru Cioba, Ilija Bogunovic

Bayesian optimisation (BO) is a powerful framework for global optimisation of costly functions, using predictions from Gaussian process models (GPs). In this work, we apply BO to f…

cs.LG20223 cited

Flexible Multiple-Objective Reinforcement Learning for Chip Placement

Fu-Chieh Chang, Yu-Wei Tseng, Ya-Wen Yu +11

Recently, successful applications of reinforcement learning to chip placement have emerged. Pretrained models are necessary to improve efficiency and effectiveness. Currently, the…

eess.SP2020

Efficient attention guided 5G power amplifier digital predistortion

Alexandru Cioba, Alvin Chua, Da-shan Shiu +2

We investigate neural network (NN) assisted techniques for compensating the non-linear behaviour and the memory effect of a 5G PA through digital predistortion (DPD). Traditionally…