McCallum, Andrew

95 publications

ICML 2025 A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings Shib Sankar Dasgupta, Michael Boratko, Andrew Mccallum

NeurIPS 2025 AutoDiscovery: Open-Ended Scientific Discovery via Bayesian Surprise Dhruv Agarwal, Bodhisattwa Prasad Majumder, Reece Adamson, Megha Chakravorty, Satvika Reddy Gavireddy, Aditya Parashar, Harshit Surana, Bhavana Dalvi Mishra, Andrew McCallum, Ashish Sabharwal, Peter Clark

NeurIPS 2025 OpenUnlearning: Accelerating LLM Unlearning via Unified Benchmarking of Methods and Metrics Vineeth Dorna, Anmol Reddy Mekala, Wenlong Zhao, Andrew McCallum, J Zico Kolter, Zachary Chase Lipton, Pratyush Maini

ICML 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

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

ICML 2024 Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching Rico Angell, Andrew Mccallum

TMLR 2024 Incremental Extractive Opinion Summarization Using Cover Trees Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Manzil Zaheer, Andrew McCallum, Amr Ahmed, Snigdha Chaturvedi

NeurIPS 2024 Learning Representations for Hierarchies with Minimal Support Benjamin Rozonoyer, Michael Boratko, Dhruvesh Patel, Wenlong Zhao, Shib Dasgupta, Hung Le, Andrew McCallum

AISTATS 2023 Improving Dual-Encoder Training Through Dynamic Indexes for Negative Mining Nicholas Monath, Manzil Zaheer, Kelsey Allen, Andrew Mccallum

ICLR 2023 KwikBucks: Correlation Clustering with Cheap-Weak and Expensive-Strong Signals Sandeep Silwal, Sara Ahmadian, Andrew Nystrom, Andrew McCallum, Deepak Ramachandran, Seyed Mehran Kazemi

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

ICML 2022 Interactive Correlation Clustering with Existential Cluster Constraints Rico Angell, Nicholas Monath, Nishant Yadav, Andrew Mccallum

ICML 2022 Knowledge Base Question Answering by Case-Based Reasoning over Subgraphs Rajarshi Das, Ameya Godbole, Ankita Naik, Elliot Tower, Manzil Zaheer, Hannaneh Hajishirzi, Robin Jia, Andrew Mccallum

AutoML 2022 Meta-Adapters: Parameter Efficient Few-Shot Fine-Tuning Through Meta-Learning Trapit Bansal, Salaheddin Alzubi, Tong Wang, Jay-Yoon Lee, Andrew McCallum

ICLR 2022 Modeling Label Space Interactions in Multi-Label Classification Using Box Embeddings Dhruvesh Patel, Pavitra Dangati, Jay-Yoon Lee, Michael Boratko, Andrew McCallum

NeurIPS 2022 Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings Dongxu Zhang, Michael Boratko, Cameron Musco, Andrew McCallum

NeurIPS 2022 Structured Energy Network as a Loss Jay Yoon Lee, Dhruvesh Patel, Purujit Goyal, Wenlong Zhao, Zhiyang Xu, Andrew McCallum

AAAI 2022 Sublinear Time Approximation of Text Similarity Matrices Archan Ray, Nicholas Monath, Andrew McCallum, Cameron Musco

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

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

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

UAI 2021 Exact and Approximate Hierarchical Clustering Using A* Craig S. Greenberg, Sebastian Macaluso, Nicholas Monath, Avinava Dubey, Patrick Flaherty, Manzil Zaheer, Amr Ahmed, Kyle Cranmer, Andrew McCallum

AAAI 2021 Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications Haw-Shiuan Chang, Amol Agrawal, Andrew McCallum

UAI 2021 Min/max Stability and Box Distributions Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum

AAAI 2020 Energy and Policy Considerations for Modern Deep Learning Research Emma Strubell, Ananya Ganesh, Andrew McCallum

NeurIPS 2020 Improving Local Identifiability in Probabilistic Box Embeddings Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum

AAAI 2020 Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-Level Supervision Trapit Bansal, Patrick Verga, Neha Choudhary, Andrew McCallum

MLJ 2020 Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition Haw-Shiuan Chang, Shankar Vembu, Sunil Mohan, Rheeya Uppaal, Andrew McCallum

ICLR 2019 Building Dynamic Knowledge Graphs from Text Using Machine Reading Comprehension Rajarshi Das, Tsendsuren Munkhdalai, Xingdi Yuan, Adam Trischler, Andrew McCallum

ICLR 2019 Multi-Step Retriever-Reader Interaction for Scalable Open-Domain Question Answering Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Andrew McCallum

NeurIPS 2019 Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum

ICLRW 2019 Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks Amirmohammad Rooshenas, Dongxu Zhang, Gopal Sharma, Andrew McCallum

ICLR 2019 Smoothing the Geometry of Probabilistic Box Embeddings Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum

ICML 2019 Supervised Hierarchical Clustering with Exponential Linkage Nishant Yadav, Ari Kobren, Nicholas Monath, Andrew Mccallum

NeurIPS 2018 Compact Representation of Uncertainty in Clustering Craig Greenberg, Nicholas Monath, Ari Kobren, Patrick Flaherty, Andrew McGregor, Andrew McCallum

ICLR 2018 Go for a Walk and Arrive at the Answer: Reasoning over Paths in Knowledge Bases Using Reinforcement Learning Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum

NeurIPS 2017 Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples Haw-Shiuan Chang, Erik Learned-Miller, Andrew McCallum

ICML 2017 End-to-End Learning for Structured Prediction Energy Networks David Belanger, Bishan Yang, Andrew McCallum

ICLR 2017 Learning a Natural Language Interface with Neural Programmer Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei

ICML 2016 Structured Prediction Energy Networks David Belanger, Andrew McCallum

UAI 2015 Bethe Projections for Non-Local Inference Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum

ICLR 2015 Word Representations via Gaussian Embedding Luke Vilnis, Andrew McCallum

UAI 2014 Message Passing for Soft Constraint Dual Decomposition David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum

ICLR 2013 Latent Relation Representations for Universal Schemas Sebastian Riedel, Limin Yao, Andrew McCallum

FnTML 2012 An Introduction to Conditional Random Fields Charles Sutton, Andrew McCallum

NeurIPS 2012 MAP Inference in Chains Using Column Generation David Belanger, Alexandre Passos, Sebastian Riedel, Andrew McCallum

NeurIPS 2011 Query-Aware MCMC Michael L. Wick, Andrew McCallum

ICML 2011 SampleRank: Training Factor Graphs with Atomic Gradients Michael L. Wick, Khashayar Rohanimanesh, Kedar Bellare, Aron Culotta, Andrew McCallum

JMLR 2010 Generalized Expectation Criteria for Semi-Supervised Learning with Weakly Labeled Data Gideon S. Mann, Andrew McCallum

ICML 2010 High-Performance Semi-Supervised Learning Using Discriminatively Constrained Generative Models Gregory Druck, Andrew McCallum

UAI 2010 Inference by Minimizing Size, Divergence, or Their Sum Sebastian Riedel, David A. Smith, Andrew McCallum

ECML-PKDD 2010 Modeling Relations and Their Mentions Without Labeled Text Sebastian Riedel, Limin Yao, Andrew McCallum

UAI 2009 Alternating Projections for Learning with Expectation Constraints Kedar Bellare, Gregory Druck, Andrew McCallum

ECML-PKDD 2009 Bi-Directional Joint Inference for Entity Resolution and Segmentation Using Imperatively-Defined Factor Graphs Sameer Singh, Karl Schultz, Andrew McCallum

NeurIPS 2009 FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs Andrew McCallum, Karl Schultz, Sameer Singh

MLJ 2009 Piecewise Training for Structured Prediction Charles Sutton, Andrew McCallum

NeurIPS 2009 Rethinking LDA: Why Priors Matter Hanna M. Wallach, David M. Mimno, Andrew McCallum

NeurIPS 2009 Training Factor Graphs with Reinforcement Learning for Efficient MAP Inference Khashayar Rohanimanesh, Sameer Singh, Andrew McCallum, Michael J. Black

ICML 2008 Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008 William W. Cohen, Andrew McCallum, Sam T. Roweis

UAI 2008 Topic Models Conditioned on Arbitrary Features with Dirichlet-Multinomial Regression David M. Mimno, Andrew McCallum

JMLR 2007 Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data Charles Sutton, Andrew McCallum, Khashayar Rohanimanesh

UAI 2007 Improved Dynamic Schedules for Belief Propagation Charles Sutton, Andrew McCallum

IJCAI 2007 Improving Author Coreference by Resource-Bounded Information Gathering from the Web Pallika H. Kanani, Andrew McCallum, Chris Pal

ICML 2007 Mixtures of Hierarchical Topics with Pachinko Allocation David M. Mimno, Wei Li, Andrew McCallum

UAI 2007 Nonparametric Bayes Pachinko Allocation Wei Li, David M. Blei, Andrew McCallum

ICCV 2007 People-LDA: Anchoring Topics to People Using Face Recognition Vidit Jain, Erik G. Learned-Miller, Andrew McCallum

ICML 2007 Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields Charles Sutton, Andrew McCallum

COLT 2007 Resource-Bounded Information Gathering for Correlation Clustering Pallika H. Kanani, Andrew McCallum

ICML 2007 Simple, Robust, Scalable Semi-Supervised Learning via Expectation Regularization Gideon S. Mann, Andrew McCallum

JAIR 2007 Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email Andrew McCallum, Xuerui Wang, Andrés Corrada-Emmanuel

ICML 2006 Joint Group and Topic Discovery from Relations and Text Andrew McCallum, Xuerui Wang, Natasha Mohanty

AAAI 2006 Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification Andrew McCallum, Chris Pal, Gregory Druck, Xuerui Wang

ICML 2006 Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations Wei Li, Andrew McCallum

UAI 2005 A Conditional Random Field for Discriminatively-Trained Finite-State String Edit Distance Andrew McCallum, Kedar Bellare, Fernando C. N. Pereira

NeurIPS 2005 Group and Topic Discovery from Relations and Their Attributes Xuerui Wang, Natasha Mohanty, Andrew McCallum

ICML 2005 Multi-Way Distributional Clustering via Pairwise Interactions Ron Bekkerman, Ran El-Yaniv, Andrew McCallum

UAI 2005 Piecewise Training for Undirected Models Charles Sutton, Andrew McCallum

AAAI 2005 Reducing Labeling Effort for Structured Prediction Tasks Aron Culotta, Andrew McCallum

AAAI 2005 Semi-Supervised Sequence Modeling with Syntactic Topic Models Wei Li, Andrew McCallum

IJCAI 2005 Topic and Role Discovery in Social Networks Andrew McCallum, Andrés Corrada-Emmanuel, Xuerui Wang

UAI 2004 An Integrated, Conditional Model of Information Extraction and Coreference with Appli Ben Wellner, Andrew McCallum, Fuchun Peng, Michael Hay

NeurIPS 2004 Conditional Models of Identity Uncertainty with Application to Noun Coreference Andrew McCallum, Ben Wellner

ICML 2004 Dynamic Conditional Random Fields: Factorized Probabilistic Models for Labeling and Segmenting Sequence Data Charles Sutton, Khashayar Rohanimanesh, Andrew McCallum

AAAI 2004 Interactive Information Extraction with Constrained Conditional Random Fields Trausti T. Kristjansson, Aron Culotta, Paul A. Viola, Andrew McCallum

NeurIPS 2003 Classification with Hybrid Generative/Discriminative Models Rajat Raina, Yirong Shen, Andrew McCallum, Andrew Y. Ng

UAI 2003 Efficiently Inducing Features of Conditional Random Fields Andrew McCallum

ICML 2001 Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data John D. Lafferty, Andrew McCallum, Fernando C. N. Pereira

ICML 2001 Toward Optimal Active Learning Through Sampling Estimation of Error Reduction Nicholas Roy, Andrew McCallum

AAAI 2000 Information Extraction with HMM Structures Learned by Stochastic Optimization Dayne Freitag, Andrew McCallum

ICML 2000 Learning to Create Customized Authority Lists Huan Chang, David Cohn, Andrew McCallum

ICML 2000 Maximum Entropy Markov Models for Information Extraction and Segmentation Andrew McCallum, Dayne Freitag, Fernando C. N. Pereira

IJCAI 1999 A Machine Learning Approach to Building Domain-Specific Search Engines Andrew McCallum, Kamal Nigam, Jason Rennie, Kristie Seymore

ICML 1998 Improving Text Classification by Shrinkage in a Hierarchy of Classes Andrew McCallum, Ronald Rosenfeld, Tom M. Mitchell, Andrew Y. Ng

AAAI 1998 Learning to Classify Text from Labeled and Unlabeled Documents Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom M. Mitchell

AAAI 1998 Learning to Extract Symbolic Knowledge from the World Wide Web Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom M. Mitchell, Kamal Nigam, Seán Slattery