Aditya Krishna Menon

Lexing Xie

CoRR, 2020

Self-supervised Learning for Deep Models in Recommendations.

[BibT_eX]

[DOI]

CoRR, 2020

Why distillation helps: a statistical perspective.

[BibT_eX]

[DOI]

CoRR, 2020

Doubly-stochastic mining for heterogeneous retrieval.

[BibT_eX]

[DOI]

CoRR, 2020

Robust large-margin learning in hyperbolic space.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Federated Learning with Only Positive Labels.

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Supervised learning: no loss no cry.

[BibT_eX]

[DOI]

Richard Nock

Alexander Patrick Mathews

Proceedings of the 37th International Conference on Machine Learning, 2020

Does label smoothing mitigate label noise?

[BibT_eX]

[DOI]

Proceedings of the 37th International Conference on Machine Learning, 2020

Can gradient clipping mitigate label noise?

[BibT_eX]

[DOI]

Proceedings of the 8th International Conference on Learning Representations, 2020

Semantic Label Smoothing for Sequence to Sequence Problems.

[BibT_eX]

[DOI]

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, 2020

SupMMD: A Sentence Importance Model for Extractive Summarisation using Maximum Mean Discrepancy.

[BibT_eX]

[DOI]

Umanga Bista

Lexing Xie

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

2019

The risk of trivial solutions in bipartite top ranking.

[BibT_eX]

[DOI]

Mach. Learn., 2019

Online Hierarchical Clustering Approximations.

[BibT_eX]

[DOI]

CoRR, 2019

Noise-tolerant fair classification.

[BibT_eX]

[DOI]

CoRR, 2019

Fairness risk measures.

[BibT_eX]

[DOI]

Alexander Patrick Mathews

Proceedings of the 36th International Conference on Machine Learning, 2019

Complementary-Label Learning for Arbitrary Losses and Models.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

Monge blunts Bayes: Hardness Results for Adversarial Training.

[BibT_eX]

[DOI]

Proceedings of the 36th International Conference on Machine Learning, 2019

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data.

[BibT_eX]

[DOI]

Proceedings of the 7th International Conference on Learning Representations, 2019

Comparative Document Summarisation via Classification.

[BibT_eX]

[DOI]

Umanga Bista

Minjeong Shin

Lexing Xie

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Cold-start playlist recommendation with multitask learning.

[BibT_eX]

[DOI]

Dawei Chen

Cheng Soon Ong

PeerJ Prepr., 2018

Learning from binary labels with instance-dependent noise.

[BibT_eX]

[DOI]

Nagarajan Natarajan

Mach. Learn., 2018

Monge beats Bayes: Hardness Results for Adversarial Training.

[BibT_eX]

[DOI]

CoRR, 2018

Anomaly Detection using One-Class Neural Networks.

[BibT_eX]

[DOI]

Raghavendra Chalapathy

CoRR, 2018

The cost of fairness in binary classification.

[BibT_eX]

[DOI]

Proceedings of the Conference on Fairness, Accountability and Transparency, 2018

Proper Loss Functions for Nonlinear Hawkes Processes.

[BibT_eX]

[DOI]

Young Lee

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

The cost of fairness in classification.

[BibT_eX]

[DOI]

CoRR, 2017

Structured Recommendation.

[BibT_eX]

[DOI]

CoRR, 2017

Revisiting revisits in trajectory recommendation.

[BibT_eX]

[DOI]

Proceedings of International Workshop on Citizens for Recommender Systems, 2017

PathRec: Visual Analysis of Travel Route Recommendations.

[BibT_eX]

[DOI]

Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017

Robust, Deep and Inductive Anomaly Detection.

[BibT_eX]

[DOI]

Raghavendra Chalapathy

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

f-GANs in an Information Geometric Nutshell.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes.

[BibT_eX]

[DOI]

Young Lee

Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Low-Rank Linear Cold-Start Recommendation from Social Data.

[BibT_eX]

[DOI]

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Bipartite Ranking: a Risk-Theoretic Perspective.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2016

Making Neural Networks Robust to Label Noise: a Loss Correction Approach.

[BibT_eX]

[DOI]

CoRR, 2016

Learning from Binary Labels with Instance-Dependent Corruption.

[BibT_eX]

[DOI]

Nagarajan Natarajan

CoRR, 2016

A scaled Bregman theorem with applications.

[BibT_eX]

[DOI]

Richard Nock

Cheng Soon Ong

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Practical Linear Models for Large-Scale One-Class Collaborative Filtering.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Linking losses for density ratio and class-probability estimation.

[BibT_eX]

[DOI]

Cheng Soon Ong

Proceedings of the 33nd International Conference on Machine Learning, 2016

On the Effectiveness of Linear Models for One-Class Collaborative Filtering.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

An Average Classification Algorithm.

[BibT_eX]

[DOI]

CoRR, 2015

AutoRec: Autoencoders Meet Collaborative Filtering.

[BibT_eX]

[DOI]

Proceedings of the 24th International Conference on World Wide Web Companion, 2015

Cross-Modal Retrieval: A Pairwise Classification Approach.

[BibT_eX]

[DOI]

Didi Surian

Proceedings of the 2015 SIAM International Conference on Data Mining, Vancouver, BC, Canada, April 30, 2015

Learning with Symmetric Label Noise: The Importance of Being Unhinged.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Learning from Corrupted Binary Labels via Class-Probability Estimation.

[BibT_eX]

[DOI]

Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Detecting inappropriate access to electronic health records using collaborative filtering.

[BibT_eX]

[DOI]

Mach. Learn., 2014

Bayes-Optimal Scorers for Bipartite Ranking.

[BibT_eX]

[DOI]

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Latent feature models for dyadic prediction /.

[BibT_eX]

[DOI]

PhD thesis, 2013

Beam search algorithms for multilabel learning.

[BibT_eX]

[DOI]

Mach. Learn., 2013

A colorful approach to text processing by example.

[BibT_eX]

[DOI]

Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, 2013

A Machine Learning Framework for Programming by Example.

[BibT_eX]

[DOI]

Proceedings of the 30th International Conference on Machine Learning, 2013

On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance.

[BibT_eX]

[DOI]

Harikrishna Narasimhan

Shivani Agarwal

Proceedings of the 30th International Conference on Machine Learning, 2013

2012

Textual Features for Programming by Example

[BibT_eX]

[DOI]

CoRR, 2012

Learning and Inference in Probabilistic Classifier Chains with Beam Search.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Predicting accurate probabilities with a ranking loss.

[BibT_eX]

[DOI]

Proceedings of the 29th International Conference on Machine Learning, 2012

2011

Fast Algorithms for Approximating the Singular Value Decomposition.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2011

Link Prediction via Matrix Factorization.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Response prediction using collaborative filtering with hierarchies and side-information.

[BibT_eX]

[DOI]

Krishna Prasad Chitrapura

Sachin Garg

Deepak Agarwal

Nagaraj Kota

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2010

Predicting labels for dyadic data.

[BibT_eX]

[DOI]

Data Min. Knowl. Discov., 2010

Dyadic Prediction Using a Latent Feature Log-Linear Model

[BibT_eX]

[DOI]

CoRR, 2010

A Log-Linear Model with Latent Features for Dyadic Prediction.

[BibT_eX]

[DOI]