Aditya Krishna Menon

According to our database1, Aditya Krishna Menon authored at least 59 papers between 2007 and 2020.

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Bibliography

2020
Why distillation helps: a statistical perspective.
CoRR, 2020

Doubly-stochastic mining for heterogeneous retrieval.
CoRR, 2020

Federated Learning with Only Positive Labels.
CoRR, 2020

Robust Large-Margin Learning in Hyperbolic Space.
CoRR, 2020

Does label smoothing mitigate label noise?
CoRR, 2020

Supervised Learning: No Loss No Cry.
CoRR, 2020

Can gradient clipping mitigate label noise?
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
The risk of trivial solutions in bipartite top ranking.
Mach. Learn., 2019

Online Hierarchical Clustering Approximations.
CoRR, 2019

Noise-tolerant fair classification.
CoRR, 2019

Fairness risk measures.
Proceedings of the 36th International Conference on Machine Learning, 2019

Complementary-Label Learning for Arbitrary Losses and Models.
Proceedings of the 36th International Conference on Machine Learning, 2019

Monge blunts Bayes: Hardness Results for Adversarial Training.
Proceedings of the 36th International Conference on Machine Learning, 2019

On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data.
Proceedings of the 7th International Conference on Learning Representations, 2019

Comparative Document Summarisation via Classification.
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018
Cold-start playlist recommendation with multitask learning.
PeerJ Prepr., 2018

Learning from binary labels with instance-dependent noise.
Mach. Learn., 2018

Monge beats Bayes: Hardness Results for Adversarial Training.
CoRR, 2018

Anomaly Detection using One-Class Neural Networks.
CoRR, 2018

The cost of fairness in binary classification.
Proceedings of the Conference on Fairness, Accountability and Transparency, 2018

Proper Loss Functions for Nonlinear Hawkes Processes.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
The cost of fairness in classification.
CoRR, 2017

Structured Recommendation.
CoRR, 2017

Revisiting revisits in trajectory recommendation.
Proceedings of International Workshop on Citizens for Recommender Systems, 2017

PathRec: Visual Analysis of Travel Route Recommendations.
Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017

Robust, Deep and Inductive Anomaly Detection.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

f-GANs in an Information Geometric Nutshell.
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.
Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes.
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2017

Low-Rank Linear Cold-Start Recommendation from Social Data.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Bipartite Ranking: a Risk-Theoretic Perspective.
J. Mach. Learn. Res., 2016

Making Neural Networks Robust to Label Noise: a Loss Correction Approach.
CoRR, 2016

Learning from Binary Labels with Instance-Dependent Corruption.
CoRR, 2016

A scaled Bregman theorem with applications.
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.
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Linking losses for density ratio and class-probability estimation.
Proceedings of the 33nd International Conference on Machine Learning, 2016

On the Effectiveness of Linear Models for One-Class Collaborative Filtering.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
An Average Classification Algorithm.
CoRR, 2015

AutoRec: Autoencoders Meet Collaborative Filtering.
Proceedings of the 24th International Conference on World Wide Web Companion, 2015

Cross-Modal Retrieval: A Pairwise Classification Approach.
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.
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.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Detecting inappropriate access to electronic health records using collaborative filtering.
Mach. Learn., 2014

Bayes-Optimal Scorers for Bipartite Ranking.
Proceedings of The 27th Conference on Learning Theory, 2014

2013
Latent feature models for dyadic prediction /.
PhD thesis, 2013

Beam search algorithms for multilabel learning.
Mach. Learn., 2013

A colorful approach to text processing by example.
Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, 2013

A Machine Learning Framework for Programming by Example.
Proceedings of the 30th International Conference on Machine Learning, 2013

On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
Textual Features for Programming by Example
CoRR, 2012

Learning and Inference in Probabilistic Classifier Chains with Beam Search.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Predicting accurate probabilities with a ranking loss.
Proceedings of the 29th International Conference on Machine Learning, 2012

2011
Fast Algorithms for Approximating the Singular Value Decomposition.
ACM Trans. Knowl. Discov. Data, 2011

Link Prediction via Matrix Factorization.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Response prediction using collaborative filtering with hierarchies and side-information.
Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

2010
Predicting labels for dyadic data.
Data Min. Knowl. Discov., 2010

Dyadic Prediction Using a Latent Feature Log-Linear Model
CoRR, 2010

A Log-Linear Model with Latent Features for Dyadic Prediction.
Proceedings of the ICDM 2010, 2010

2007
An incremental data-stream sketch using sparse random projections.
Proceedings of the Seventh SIAM International Conference on Data Mining, 2007


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