Compression of Optimal Value Functions for Markov Decision Processes. - Mykel J. Kochenderfer; Nicholas Monath (DCC, 2013)

Building Knowledge Bases with Universal Schema: Cold Start and Slot-Filling Approaches. - Benjamin Roth 001; Nicholas Monath; David Belanger 002; Emma Strubell; Patrick Verga; Andrew McCallum (TAC, 2015)

Extracting Multilingual Relations under Limited Resources: TAC 2016 Cold-Start KB construction and Slot-Filling using Compositional Universal Schema. - Haw-Shiuan Chang; Abdurrahman Munir; Ao Liu 003; Johnny Tian-Zheng Wei; Aaron Traylor; Ajay Nagesh; Nicholas Monath; Patrick Verga; Emma Strubell; Andrew McCallum (TAC, 2016)

Entity-centric Attribute Feedback for Interactive Knowledge Bases. - Ari Kobren; Nicholas Monath; Andrew McCallum (AKBC@NIPS, 2017)

Learning String Alignments for Entity Aliases. - Aaron Traylor; Nicholas Monath; Rajarshi Das; Andrew McCallum (AKBC@NIPS, 2017)

A Hierarchical Algorithm for Extreme Clustering. - Ari Kobren; Nicholas Monath; Akshay Krishnamurthy; Andrew McCallum (KDD, 2017)

An Online Hierarchical Algorithm for Extreme Clustering. - Ari Kobren; Nicholas Monath; Akshay Krishnamurthy; Andrew McCallum (CoRR, 2017)

Play Duration Based User-Entity Affinity Modeling in Spoken Dialog System. - Bo Xiao; Nicholas Monath; Shankar Ananthakrishnan; Abishek Ravi (INTERSPEECH, 2018)

Compact Representation of Uncertainty in Clustering. - Craig S. Greenberg; Nicholas Monath; Ari Kobren; Patrick Flaherty; Andrew McGregor 001; Andrew McCallum (NeurIPS, 2018)

Optimal Transport-based Alignment of Learned Character Representations for String Similarity. - Derek Tam; Nicholas Monath; Ari Kobren; Aaron Traylor; Rajarshi Das; Andrew McCallum (ACL (1), 2019)

Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction. - Ari Kobren; Nicholas Monath; Andrew McCallum (AKBC, 2019)

Supervised Hierarchical Clustering with Exponential Linkage. - Nishant Yadav; Ari Kobren; Nicholas Monath; Andrew McCallum (ICML, 2019)

Scalable Hierarchical Clustering with Tree Grafting. - Nicholas Monath; Ari Kobren; Akshay Krishnamurthy; Michael R. Glass; Andrew McCallum (KDD, 2019)

Gradient-based Hierarchical Clustering using Continuous Representations of Trees in Hyperbolic Space. - Nicholas Monath; Manzil Zaheer; Daniel Silva 005; Andrew McCallum; Amr Ahmed 001 (KDD, 2019)

Predicting Institution Hierarchies with Set-based Models. - Derek Tam; Nicholas Monath; Ari Kobren; Andrew McCallum (AKBC, 2020)

Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction. - Dung Thai; Zhiyang Xu; Nicholas Monath; Boris Veytsman; Andrew McCallum (AKBC, 2020)

Probabilistic Case-based Reasoning in Knowledge Bases. - Rajarshi Das; Ameya Godbole; Nicholas Monath; Manzil Zaheer; Andrew McCallum (EMNLP (Findings), 2020)

Compact Representation of Uncertainty in Hierarchical Clustering. - Craig S. Greenberg; Sebastian Macaluso; Nicholas Monath; Ji Ah Lee; Patrick Flaherty; Kyle Cranmer; Andrew McGregor 001; Andrew McCallum (CoRR, 2020)

Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion. - Rajarshi Das; Ameya Godbole; Nicholas Monath; Manzil Zaheer; Andrew McCallum (CoRR, 2020)

Clustering-based Inference for Zero-Shot Biomedical Entity Linking. - Rico Angell; Nicholas Monath; Sunil Mohan; Nishant Yadav; Andrew McCallum (CoRR, 2020)

Scalable Bottom-Up Hierarchical Clustering. - Nicholas Monath; Avinava Dubey; Guru Guruganesh; Manzil Zaheer; Amr Ahmed 001; Andrew McCallum; Gökhan Mergen; Marc Najork; Mert Terzihan; Bryon Tjanaka; Yuan Wang; Yuchen Wu (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)

Cluster Trellis: Data Structures & Algorithms for Exact Inference in Hierarchical Clustering. - Sebastian Macaluso; Craig S. Greenberg; Nicholas Monath; Ji Ah Lee; Patrick Flaherty; Kyle Cranmer; Andrew McGregor 001; Andrew McCallum (AISTATS, 2021)

DAG-Structured Clustering by Nearest Neighbors. - Nicholas Monath; Manzil Zaheer; Kumar Avinava Dubey; Amr Ahmed 001; Andrew McCallum (AISTATS, 2021)

Low resource recognition and linking of biomedical concepts from a large ontology. - Sunil Mohan; Rico Angell; Nicholas Monath; Andrew McCallum (BCB, 2021)

Scalable Hierarchical Agglomerative Clustering. - Nicholas Monath; Kumar Avinava Dubey; Guru Guruganesh; Manzil Zaheer; Amr Ahmed 001; Andrew McCallum; Gökhan Mergen; Marc Najork; Mert Terzihan; Bryon Tjanaka; Yuan Wang; Yuchen Wu (KDD, 2021)

Clustering-based Inference for Biomedical Entity Linking. - Rico Angell; Nicholas Monath; Sunil Mohan; Nishant Yadav; Andrew McCallum (NAACL-HLT, 2021)

Capacity and Bias of Learned Geometric Embeddings for Directed Graphs. - Michael Boratko; Dongxu Zhang; Nicholas Monath; Luke Vilnis; Kenneth L. Clarkson; Andrew McCallum (NeurIPS, 2021)

Knowledge Informed Semantic Parsing for Conversational Question Answering. - Raghuveer Thirukovalluru; Mukund Sridhar; Dung Thai; Shruti Chanumolu; Nicholas Monath; Sankaranarayanan Ananthakrishnan; Andrew McCallum (RepL4NLP@ACL-IJCNLP, 2021)

Exact and approximate hierarchical clustering using A. - Craig S. Greenberg; Sebastian Macaluso; Nicholas Monath; Avinava Dubey; Patrick Flaherty; Manzil Zaheer; Amr Ahmed 001; Kyle Cranmer; Andrew McCallum (UAI, 2021)

Model-Agnostic Graph Regularization for Few-Shot Learning. - Ethan Shen; Maria Brbic; Nicholas Monath; Jiaqi Zhai; Manzil Zaheer; Jure Leskovec (CoRR, 2021)

Entity Linking and Discovery via Arborescence-based Supervised Clustering. - Dhruv Agarwal 003; Rico Angell; Nicholas Monath; Andrew McCallum (CoRR, 2021)

An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity. - Siddhartha Mishra; Nicholas Monath; Michael Boratko; Ariel Kobren; Andrew McCallum (AAAI, 2022)

Sublinear Time Approximation of Text Similarity Matrices. - Archan Ray; Nicholas Monath; Andrew McCallum; Cameron Musco (AAAI, 2022)

Autoregressive Structured Prediction with Language Models. - Tianyu Liu 004; Yuchen Eleanor Jiang; Nicholas Monath; Ryan Cotterell; Mrinmaya Sachan (EMNLP (Findings), 2022)

Efficient Nearest Neighbor Search for Cross-Encoder Models using Matrix Factorization. - Nishant Yadav; Nicholas Monath; Rico Angell; Manzil Zaheer; Andrew McCallum (EMNLP, 2022)

Interactive Correlation Clustering with Existential Cluster Constraints. - Rico Angell; Nicholas Monath; Nishant Yadav; Andrew McCallum (ICML, 2022)

Entity Linking via Explicit Mention-Mention Coreference Modeling. - Dhruv Agarwal 003; Rico Angell; Nicholas Monath; Andrew McCallum (NAACL-HLT, 2022)

Autoregressive Structured Prediction with Language Models. - Tianyu Liu 004; Yuchen Jiang; Nicholas Monath; Ryan Cotterell; Mrinmaya Sachan (CoRR, 2022)

Improving Dual-Encoder Training through Dynamic Indexes for Negative Mining. - Nicholas Monath; Manzil Zaheer; Kelsey Allen; Andrew McCallum (AISTATS, 2023)

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

Unsupervised Opinion Summarization Using Approximate Geodesics. - Somnath Basu Roy Chowdhury; Nicholas Monath; Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (EMNLP (Findings), 2023)

Efficient k-NN Search with Cross-Encoders using Adaptive Multi-Round CUR Decomposition. - Nishant Yadav; Nicholas Monath; Manzil Zaheer; Andrew McCallum (EMNLP (Findings), 2023)

Online Level-wise Hierarchical Clustering. - Nicholas Monath; Manzil Zaheer; Andrew McCallum (KDD, 2023)

Robust Concept Erasure via Kernelized Rate-Distortion Maximization. - Somnath Basu Roy Chowdhury; Nicholas Monath; Kumar Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (NeurIPS, 2023)

Adaptive Selection of Anchor Items for CUR-based k-NN search with Cross-Encoders. - Nishant Yadav; Nicholas Monath; Manzil Zaheer; Andrew McCallum (CoRR, 2023)

Sequence Reducible Holdout Loss for Language Model Pretraining. - Raghuveer Thirukovalluru; Nicholas Monath; Bhuwan Dhingra; Sam Wiseman (LREC/COLING, 2024)

Analysis of Plan-based Retrieval for Grounded Text Generation. - Ameya Godbole; Nicholas Monath; Seungyeon Kim 001; Ankit Singh Rawat; Andrew McCallum; Manzil Zaheer (EMNLP, 2024)

Enhancing Group Fairness in Online Settings Using Oblique Decision Forests. - Somnath Basu Roy Chowdhury; Nicholas Monath; Ahmad Beirami; Rahul Kidambi; Kumar Avinava Dubey; Amr Ahmed 001; Snigdha Chaturvedi (ICLR, 2024)

Adaptive Retrieval and Scalable Indexing for k-NN Search with Cross-Encoders. - Nishant Yadav; Nicholas Monath; Manzil Zaheer; Rob Fergus; Andrew McCallum (ICLR, 2024)

A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks. - Nicholas Monath; Will Sussman Grathwohl; Michael Boratko; Rob Fergus; Andrew McCallum; Manzil Zaheer (ICML, 2024)

Long-Range Tasks Using Short-Context LLMs: Incremental Reasoning With Structured Memories. - Dulhan Jayalath; James Bradley Wendt; Nicholas Monath; Sandeep Tata; Beliz Gunel (CoRR, 2024)

Incremental Extractive Opinion Summarization Using Cover Trees. - Somnath Basu Roy Chowdhury; Nicholas Monath; Kumar Avinava Dubey; Manzil Zaheer; Andrew McCallum; Amr Ahmed 001; Snigdha Chaturvedi (Trans. Mach. Learn. Res., 2024)

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

Fundamental Limits of Perfect Concept Erasure. - Somnath Basu Roy Chowdhury; Kumar Avinava Dubey; Ahmad Beirami; Rahul Kidambi; Nicholas Monath; Amr Ahmed 001; Snigdha Chaturvedi (AISTATS, 2025)