Classification with imbalance: A similarity-based method for predicting respiratory failure. - Harsh Shrivastava 001; Vijay Huddar; Sakyajit Bhattacharya; Vaibhav Rajan (BIBM, 2015)

ICU Mortality Prediction: A Classification Algorithm for Imbalanced Datasets. - Sakyajit Bhattacharya; Vaibhav Rajan; Harsh Shrivastava 001 (AAAI, 2017)

Cooperative neural networks (CoNN): Exploiting prior independence structure for improved classification. - Harsh Shrivastava 001; Eugene Bart; Bob Price; Hanjun Dai; Bo Dai 001; Srinivas Aluru (NeurIPS, 2018)

GLAD: Learning Sparse Graph Recovery. - Harsh Shrivastava 001; Xinshi Chen; Binghong Chen; Guanghui Lan; Srinivas Aluru; Le Song (CoRR, 2019)

AntMan: Sparse Low-Rank Compression to Accelerate RNN inference. - Samyam Rajbhandari; Harsh Shrivastava 001; Yuxiong He (CoRR, 2019)

GLAD: Learning Sparse Graph Recovery. - Harsh Shrivastava 001; Xinshi Chen; Binghong Chen; Guanghui Lan; Srinivas Aluru; Han Liu 001; Le Song (ICLR, 2020)

Echo State Speech Recognition. - Harsh Shrivastava 001; Ankush Garg; Yuan Cao 007; Yu Zhang 033; Tara N. Sainath (ICASSP, 2021)

On Using Inductive Biases for Designing Deep Learning Architectures. - Harsh Shrivastava 001 (2021)

EnGRaiN: a supervised ensemble learning method for recovery of large-scale gene regulatory networks. - Maneesha Aluru; Harsh Shrivastava 001; Sriram P. Chockalingam; Shruti Shivakumar; Srinivas Aluru (Bioinform., 2022)

uGLAD: Sparse graph recovery by optimizing deep unrolled networks. - Harsh Shrivastava 001; Urszula Chajewska; Robin Abraham; Xinshi Chen (CoRR, 2022)

GRNUlar: A Deep Learning Framework for Recovering Single-Cell Gene Regulatory Networks. - Harsh Shrivastava 001; Xiuwei Zhang 002; Le Song; Srinivas Aluru (J. Comput. Biol., 2022)

tGLAD: A Sparse Graph Recovery Based Approach for Multivariate Time Series Segmentation. - Shima Imani; Harsh Shrivastava 001 (AALTD@ECML/PKDD, 2023)

MathPrompter: Mathematical Reasoning using Large Language Models. - Shima Imani; Liang Du; Harsh Shrivastava 001 (ACL (industry), 2023)

Neural Graphical Models. - Harsh Shrivastava 001; Urszula Chajewska (ECSQARU, 2023)

Neural Graph Revealers. - Harsh Shrivastava 001; Urszula Chajewska (ML4MHD, 2023)

Federated Learning with Neural Graphical Models. - Urszula Chajewska; Harsh Shrivastava 001 (PKDD/ECML Workshops (5), 2023)

Are uGLAD? Time will tell! - Shima Imani; Harsh Shrivastava 001 (CoRR, 2023)

DiversiGATE: A Comprehensive Framework for Reliable Large Language Models. - Shima Imani; Ali Beyram; Harsh Shrivastava 001 (CoRR, 2023)

Knowledge Propagation over Conditional Independence Graphs. - Urszula Chajewska; Harsh Shrivastava 001 (CoRR, 2023)

Methods for recovering conditional independence graphs (Abstract Reprint). - Harsh Shrivastava 001; Urszula Chajewska (IJCAI, 2024)

Generative Kaleidoscopic Networks. - Harsh Shrivastava 001 (CoRR, 2024)

Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation. - Chenyang An; Shima Imani; Feng Yao; Chengyu Dong; Ali Abbasi 008; Harsh Shrivastava 001; Samuel Buss; Jingbo Shang; Gayathri Mahalingam; Pramod Sharma; Maurice Diesendruck (CoRR, 2024)

Diffusion-Augmented Coreset Expansion for Scalable Dataset Distillation. - Ali Abbasi 008; Shima Imani; Chenyang An; Gayathri Mahalingam; Harsh Shrivastava 001; Maurice Diesendruck; Hamed Pirsiavash; Pramod Sharma; Soheil Kolouri (CoRR, 2024)

Methods for Recovering Conditional Independence Graphs: A Survey. - Harsh Shrivastava 001; Urszula Chajewska (J. Artif. Intell. Res., 2024)