Bayesian Convolutional Neural Networks. - Felix Laumann; Kumar Shridhar; Adrian Llopart Maurin (CoRR, 2018)

Subword Semantic Hashing for Intent Classification on Small Datasets. - Kumar Shridhar; Amit Sahu; Ayushman Dash; Pedro Alonso; Gustav Pihlgren; Vinay Pondeknath; Fotini Simistira; Marcus Liwicki (CoRR, 2018)

Author Profiling using Semantic and Syntactic Features. - György Kovács 001; Vanda Balogh; Purvanshi Mehta; Kumar Shridhar; Pedro Alonso; Marcus Liwicki (CLEF (Working Notes), 2019)

Subword Semantic Hashing for Intent Classification on Small Datasets. - Kumar Shridhar; Ayushman Dash; Amit Sahu; Gustav Grund Pihlgren; Pedro Alonso; Vinaychandran Pondenkandath; György Kovács 001; Foteini Simistira; Marcus Liwicki (IJCNN, 2019)

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference. - Kumar Shridhar; Felix Laumann; Marcus Liwicki (CoRR, 2019)

ProbAct: A Probabilistic Activation Function for Deep Neural Networks. - Joonho Lee; Kumar Shridhar; Hideaki Hayashi; Brian Kenji Iwana; Seokjun Kang; Seiichi Uchida (CoRR, 2019)

End to End Binarized Neural Networks for Text Classification. - Kumar Shridhar; Harshil Jain; Akshat Agarwal; Denis Kleyko (SustaiNLP@EMNLP, 2020)

Indic-Transformers: An Analysis of Transformer Language Models for Indian Languages. - Kushal Jain; Adwait P. Deshpande; Kumar Shridhar; Felix Laumann; Ayushman Dash (CoRR, 2020)

Scaling Within Document Coreference to Long Texts. - Raghuveer Thirukovalluru; Nicholas Monath; Kumar Shridhar; Manzil Zaheer; Mrinmaya Sachan; Andrew McCallum (ACL/IJCNLP (Findings), 2021)

One to Rule Them All: Towards Joint Indic Language Hate Speech Detection. - Mehar Bhatia; Tenzin Singhay Bhotia; Akshat Agarwal; Prakash Ramesh; Shubham Gupta; Kumar Shridhar; Felix Laumann; Ayushman Dash (FIRE (Working Notes), 2021)

HyperEmbed: Tradeoffs Between Resources and Performance in NLP Tasks with Hyperdimensional Computing Enabled Embedding of n-gram Statistics. - Pedro Alonso; Kumar Shridhar; Denis Kleyko; Evgeny Osipov; Marcus Liwicki (IJCNN, 2021)

Translational Equivariance in Kernelizable Attention. - Max Horn; Kumar Shridhar; Elrich Groenewald; Philipp F. M. Baumann (CoRR, 2021)

Automatic Generation of Socratic Subquestions for Teaching Math Word Problems. - Kumar Shridhar; Jakub Macina; Mennatallah El-Assady; Tanmay Sinha; Manu Kapur; Mrinmaya Sachan (EMNLP, 2022)

Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs. - Djordje Miladinovic; Kumar Shridhar; Kushal Jain; Max B. Paulus; Joachim M. Buhmann; Carl Allen (NeurIPS, 2022)

Distilling Multi-Step Reasoning Capabilities of Large Language Models into Smaller Models via Semantic Decompositions. - Kumar Shridhar; Alessandro Stolfo; Mrinmaya Sachan (CoRR, 2022)

Distilling Reasoning Capabilities into Smaller Language Models. - Kumar Shridhar; Alessandro Stolfo; Mrinmaya Sachan (ACL (Findings), 2023)

A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models. - Alessandro Stolfo; Zhijing Jin 001; Kumar Shridhar; Bernhard Schölkopf; Mrinmaya Sachan (ACL (1), 2023)

Automatic Educational Question Generation with Difficulty Level Controls. - Ying Jiao; Kumar Shridhar; Peng Cui 006; Wangchunshu Zhou; Mrinmaya Sachan (AIED, 2023)

Longtonotes: OntoNotes with Longer Coreference Chains. - Kumar Shridhar; Nicholas Monath; Raghuveer Thirukovalluru; Alessandro Stolfo; Manzil Zaheer; Andrew McCallum; Mrinmaya Sachan (EACL (Findings), 2023)

SCREWS: A Modular Framework for Reasoning with Revisions. - Kumar Shridhar; Harsh Jhamtani; Hao Fang 002; Benjamin Van Durme; Jason Eisner; Patrick Xia 002 (CoRR, 2023)

First Step Advantage: Importance of Starting Right in Multi-Step Reasoning. - Kushal Jain; Kumar Shridhar (CoRR, 2023)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

The ART of LLM Refinement: Ask, Refine, and Trust. - Kumar Shridhar; Koustuv Sinha; Andrew Cohen; Tianlu Wang; Ping Yu; Ramakanth Pasunuru; Mrinmaya Sachan; Jason Weston; Asli Celikyilmaz (NAACL-HLT, 2024)

Distilling LLMs' Decomposition Abilities into Compact Language Models. - Denis Tarasov; Kumar Shridhar (CoRR, 2024)

SMART: Self-learning Meta-strategy Agent for Reasoning Tasks. - Rongxing Liu; Kumar Shridhar; Manish Prajapat; Patrick Xia 002; Mrinmaya Sachan (CoRR, 2024)

Calibrating Large Language Models with Sample Consistency. - Qing Lyu 001; Kumar Shridhar; Chaitanya Malaviya; Li Zhang 039; Yanai Elazar; Niket Tandon; Marianna Apidianaki; Mrinmaya Sachan; Chris Callison-Burch (AAAI, 2025)

SIKeD: Self-guided Iterative Knowledge Distillation for Mathematical Reasoning. - Shivam Adarsh; Kumar Shridhar; Caglar Gulcehre; Nicholas Monath; Mrinmaya Sachan (ACL (Findings), 2025)

First-Step Advantage: Importance of Starting Right in Multi-Step Math Reasoning. - Kushal Jain; Moritz Miller; Niket Tandon; Kumar Shridhar (ACL (Findings), 2025)

Beyond Pattern Recognition: Probing Mental Representations of LMs. - Moritz Miller; Kumar Shridhar (CoRR, 2025)

UNDO: Understanding Distillation as Optimization. - Kushal Jain; Piyushi Goyal; Kumar Shridhar (CoRR, 2025)

EMAFusion: A Self-Optimizing System for Seamless LLM Selection and Integration. - Soham Shah; Kumar Shridhar; Surojit Chatterjee; Souvik Sen (CoRR, 2025)