Avirup Das

Amartya Roy, Danush Khanna, Devanshu Mahapatra et al.

This paper tackles the challenge of building robust and generalizable bias mitigation models for language. Recognizing the limitations of existing datasets, we introduce ANUBIS, a novel dataset with 1507 carefully curated sentence pairs encompassing nine social bias categories. We evaluate state-of-the-art models like T5, utilizing Supervised Fine-Tuning (SFT), Reinforcement Learning (PPO, DPO), and In-Context Learning (ICL) for effective bias mitigation. Our analysis focuses on multi-class social bias reduction, cross-dataset generalizability, and environmental impact of the trained models. ANUBIS and our findings offer valuable resources for building more equitable AI systems and contribute to the development of responsible and unbiased technologies with broad societal impact.

Souvik Deb, Shankar K. Ghosh, Avirup Das et al.

Device-to-device (D2D) communication was proposed to enhance the coverage of cellular base stations. In a D2D enabled non-standalone fifth generation cellular network (NSA), service demand of a user equipment (UE) may be served in four \emph{modes}: through LTE only, through NR only, through LTE via D2D and through NR via D2D. Such mode selection should consider the service requirements of the UEs (e.g., high data rate, low latency, ultra-reliability, etc.) and the overhead incurred by handovers. In existing mode selection approaches for D2D enabled NSA, the service requirements of the UEs have been largely ignored. To address this, in this paper, we propose a mode selection algorithm for D2D enabled NSA based on a two-level Analytic Hierarchy Process (AHP). The proposed AHP-based mechanism considers the service requirements of the UEs in level 1; and mode selection options (i.e., LTE only, NR only, LTE via D2D and NR via D2D) in level 2. Thereafter, a novel mode selection algorithm is proposed by combining the static ranking computed by the proposed two-level AHP and the variation of Reference Signal Received Power (RSRP) in different modes, thus capturing the impact of UE mobility and reducing unnecessary handovers. Simulation results show that our proposed algorithm outperforms the best performing related work in terms of the major Key performance indicators (KPIs) for all three slices, i.e., enhanced mobile broadband (eMBB), ultra reliable low latency (uRLLc) and massive machine type communications (mMTc).

Hao Zeng, Jiaqi Wang, Avirup Das et al.

Feasible solutions are crucial for Integer Programming (IP) since they can substantially speed up the solving process. In many applications, similar IP instances often exhibit similar structures and shared solution distributions, which can be potentially modeled by deep learning methods. Unfortunately, existing deep-learning-based algorithms, such as Neural Diving and Predict-and-search framework, are limited to generating only partial feasible solutions, and they must rely on solvers like SCIP and Gurobi to complete the solutions for a given IP problem. In this paper, we propose a novel framework that generates complete feasible solutions end-to-end. Our framework leverages contrastive learning to characterize the relationship between IP instances and solutions, and learns latent embeddings for both IP instances and their solutions. Further, the framework employs diffusion models to learn the distribution of solution embeddings conditioned on IP representations, with a dedicated guided sampling strategy that accounts for both constraints and objectives. We empirically evaluate our framework on four typical datasets of IP problems, and show that it effectively generates complete feasible solutions with a high probability (> 89.7 \%) without the reliance of Solvers and the quality of solutions is comparable to the best heuristic solutions from Gurobi. Furthermore, by integrating our method's sampled partial solutions with the CompleteSol heuristic from SCIP, the resulting feasible solutions outperform those from state-of-the-art methods across all datasets, exhibiting a 3.7 to 33.7\% improvement in the gap to optimal values, and maintaining a feasible ratio of over 99.7\% for all datasets.

Soham Dan, Anirbit Mukherjee, Avirup Das et al.

In the recent past, a property of neural training trajectories in weight-space had been isolated, that of "local elasticity" (denoted as $S_{\rm rel}$). Local elasticity attempts to quantify the propagation of the influence of a sampled data point on the prediction at another data. In this work, we embark on a comprehensive study of the existing notion of $S_{\rm rel}$ and also propose a new definition that addresses the limitations that we point out for the original definition in the classification setting. On various state-of-the-art neural network training on SVHN, CIFAR-10 and CIFAR-100 we demonstrate how our new proposal of $S_{\rm rel}$, as opposed to the original definition, much more sharply detects the property of the weight updates preferring to make prediction changes within the same class as the sampled data. In neural regression experiments we demonstrate that the original $S_{\rm rel}$ reveals a $2-$phase behavior -- that the training proceeds via an initial elastic phase when $S_{\rm rel}$ changes rapidly and an eventual inelastic phase when $S_{\rm rel}$ remains large. We show that some of these properties can be analytically reproduced in various instances of doing regression via gradient flows on model predictor classes.