IRS-assisted system model. by Sarmad Sohaib (21273402)

“…In the PSM-based algorithm, the IRS phase shift vector is selected from a predefined PSM to maximize the output signal-to-noise ratio (SNR) at the receiver. …”

Generalized architecture of an IRS system. by Sarmad Sohaib (21273402)

“…In the PSM-based algorithm, the IRS phase shift vector is selected from a predefined PSM to maximize the output signal-to-noise ratio (SNR) at the receiver. …”

Two simulated instances with <i>M</i> = 16. by Sarmad Sohaib (21273402)

“…In the PSM-based algorithm, the IRS phase shift vector is selected from a predefined PSM to maximize the output signal-to-noise ratio (SNR) at the receiver. …”

On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization by Jiancong Xiao (22265935)

“…In extreme cases, this bias could lead to a phenomenon we term preference collapse, where minority preferences are virtually disregarded. To mitigate this algorithmic bias, we introduce preference matching (PM) RLHF, a novel approach that <i>provably</i> aligns LLMs with the preference distribution of the reward model under the Bradley–Terry–Luce/Plackett–Luce model. …”

Integrating AI and OR for investment decision-making in emerging digital lending businesses: a risk-return multi-objective optimization approach by Vajiheh Torkian (21244732)

“…The study proposes a multi-objective decision-making model that leverages data from the Lending Club, the largest P2P marketplace in the United States, to minimize risk and maximize returns. To address the data imbalance, the model uses classification techniques including logistic regression, decision trees, random forests, and light gradient boosting machines (LGBM), which are supported by the synthetic minority oversampling technique (SMOTE). …”

Data Sheet 1_A novel method for power transformer fault diagnosis considering imbalanced data samples.docx by Jun Chen (4238)

“…Hyperparameter tuning is achieved through the Bayesian optimization algorithm to identify the model parameter set that maximizes test set accuracy.…”

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…<p dir="ltr">The Synthetic Minority Over-sampling Technique (SMOTE) is a machine learning approach to address class imbalance in datasets. …”