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ORCIDAditya Krishna Menon
Person information
- affiliation: NICTA, Canberra, Australia
- affiliation: Australian National University, College of Engineering & Computer Science
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Books and Theses
- 2013
[b1]Aditya Krishna Menon:
Latent feature models for dyadic prediction /. University of California, San Diego, USA, 2013
Journal Articles
- 2022
- [j9]Marian-Andrei Rizoiu, Alexander Soen, Shidi Li, Pio Calderon, Leanne Dong, Aditya Krishna Menon, Lexing Xie:
Interval-censored Hawkes processes. J. Mach. Learn. Res. 23: 338:1-338:84 (2022) - [j8]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. Trans. Mach. Learn. Res. 2022 (2022) - 2019
- [j7]Aditya Krishna Menon:
The risk of trivial solutions in bipartite top ranking. Mach. Learn. 108(4): 627-658 (2019) - 2018
- [j6]Aditya Krishna Menon, Brendan van Rooyen, Nagarajan Natarajan:
Learning from binary labels with instance-dependent noise. Mach. Learn. 107(8-10): 1561-1595 (2018) - 2016
- [j5]Aditya Krishna Menon, Robert C. Williamson:
Bipartite Ranking: a Risk-Theoretic Perspective. J. Mach. Learn. Res. 17: 195:1-195:102 (2016) - 2014
- [j4]Aditya Krishna Menon, Xiaoqian Jiang, Jihoon Kim, Jaideep Vaidya, Lucila Ohno-Machado
:
Detecting inappropriate access to electronic health records using collaborative filtering. Mach. Learn. 95(1): 87-101 (2014) - 2013
- [j3]Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan:
Beam search algorithms for multilabel learning. Mach. Learn. 92(1): 65-89 (2013) - 2011
- [j2]Aditya Krishna Menon, Charles Elkan:
Fast Algorithms for Approximating the Singular Value Decomposition. ACM Trans. Knowl. Discov. Data 5(2): 13:1-13:36 (2011) - 2010
- [j1]Aditya Krishna Menon, Charles Elkan:
Predicting labels for dyadic data. Data Min. Knowl. Discov. 21(2): 327-343 (2010)
Conference and Workshop Papers
- 2023
- [c54]Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar:
Supervision Complexity and its Role in Knowledge Distillation. ICLR 2023 - [c53]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? NeurIPS 2023 - [c52]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? NeurIPS 2023 - [c51]Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar:
ResMem: Learn what you can and memorize the rest. NeurIPS 2023 - [c50]Serena Lutong Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon:
Robust distillation for worst-class performance: on the interplay between teacher and student objectives. UAI 2023: 2237-2247 - 2022
- [c49]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Seungyeon Kim, Sashank J. Reddi, Sanjiv Kumar:
In defense of dual-encoders for neural ranking. ICML 2022: 15376-15400 - [c48]Harikrishna Narasimhan, Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Post-hoc estimators for learning to defer to an expert. NeurIPS 2022 - 2021
- [c47]Sashank J. Reddi, Rama Kumar Pasumarthi, Aditya Krishna Menon, Ankit Singh Rawat, Felix X. Yu, Seungyeon Kim, Andreas Veit, Sanjiv Kumar:
RankDistil: Knowledge Distillation for Ranking. AISTATS 2021: 2368-2376 - [c46]Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix X. Yu, Ting Chen, Aditya Krishna Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi (Jay) Kang, Evan Ettinger:
Self-supervised Learning for Large-scale Item Recommendations. CIKM 2021: 4321-4330 - [c45]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. ICLR 2021 - [c44]Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
Overparameterisation and worst-case generalisation: friend or foe? ICLR 2021 - [c43]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. ICLR 2021 - [c42]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
A statistical perspective on distillation. ICML 2021: 7632-7642 - [c41]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-output Spaces. ICML 2021: 8890-8901 - [c40]Harikrishna Narasimhan, Aditya Krishna Menon:
Training Over-parameterized Models with Non-decomposable Objectives. NeurIPS 2021: 18165-18181 - 2020
- [c39]Umanga Bista, Alexander Patrick Mathews, Aditya Krishna Menon, Lexing Xie:
SupMMD: A Sentence Importance Model for Extractive Summarisation using Maximum Mean Discrepancy. EMNLP (Findings) 2020: 4108-4122 - [c38]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. EMNLP (1) 2020: 4992-4998 - [c37]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Sanjiv Kumar:
Can gradient clipping mitigate label noise? ICLR 2020 - [c36]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? ICML 2020: 6448-6458 - [c35]Richard Nock, Aditya Krishna Menon:
Supervised learning: no loss no cry. ICML 2020: 7370-7380 - [c34]Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. ICML 2020: 10946-10956 - [c33]Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust large-margin learning in hyperbolic space. NeurIPS 2020 - 2019
- [c32]Umanga Bista, Alexander Patrick Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie:
Comparative Document Summarisation via Classification. AAAI 2019: 20-28 - [c31]Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. ICLR (Poster) 2019 - [c30]Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge blunts Bayes: Hardness Results for Adversarial Training. ICML 2019: 1406-1415 - [c29]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. ICML 2019: 2971-2980 - [c28]Robert C. Williamson, Aditya Krishna Menon:
Fairness risk measures. ICML 2019: 6786-6797 - 2018
- [c27]Aditya Krishna Menon, Young Lee:
Proper Loss Functions for Nonlinear Hawkes Processes. AAAI 2018: 3804-3811 - [c26]Aditya Krishna Menon, Robert C. Williamson:
The cost of fairness in binary classification. FAT 2018: 107-118 - 2017
- [c25]Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie, Darius Braziunas:
Low-Rank Linear Cold-Start Recommendation from Social Data. AAAI 2017: 1502-1508 - [c24]Aditya Krishna Menon, Young Lee:
Predicting Short-Term Public Transport Demand via Inhomogeneous Poisson Processes. CIKM 2017: 2207-2210 - [c23]Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu:
Making Deep Neural Networks Robust to Label Noise: A Loss Correction Approach. CVPR 2017: 2233-2241 - [c22]Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. NIPS 2017: 456-464 - [c21]Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla:
Robust, Deep and Inductive Anomaly Detection. ECML/PKDD (1) 2017: 36-51 - [c20]Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong:
Revisiting revisits in trajectory recommendation. CitRec@RecSys 2017: 2:1-2:6 - [c19]Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy:
PathRec: Visual Analysis of Travel Route Recommendations. RecSys 2017: 364-365 - 2016
- [c18]Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Darius Braziunas:
On the Effectiveness of Linear Models for One-Class Collaborative Filtering. AAAI 2016: 229-235 - [c17]Aditya Krishna Menon, Cheng Soon Ong:
Linking losses for density ratio and class-probability estimation. ICML 2016: 304-313 - [c16]Suvash Sedhain, Hung Bui, Jaya Kawale, Nikos Vlassis, Branislav Kveton, Aditya Krishna Menon, Trung Bui, Scott Sanner:
Practical Linear Models for Large-Scale One-Class Collaborative Filtering. IJCAI 2016: 3854-3860 - [c15]Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. NIPS 2016: 19-27 - 2015
- [c14]Aditya Krishna Menon, Brendan van Rooyen, Cheng Soon Ong, Bob Williamson:
Learning from Corrupted Binary Labels via Class-Probability Estimation. ICML 2015: 125-134 - [c13]Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson:
Learning with Symmetric Label Noise: The Importance of Being Unhinged. NIPS 2015: 10-18 - [c12]Aditya Krishna Menon, Didi Surian, Sanjay Chawla:
Cross-Modal Retrieval: A Pairwise Classification Approach. SDM 2015: 199-207 - [c11]Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, Lexing Xie:
AutoRec: Autoencoders Meet Collaborative Filtering. WWW (Companion Volume) 2015: 111-112 - 2014
- [c10]Aditya Krishna Menon, Robert C. Williamson:
Bayes-Optimal Scorers for Bipartite Ranking. COLT 2014: 68-106 - 2013
- [c9]Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Kalai:
A Machine Learning Framework for Programming by Example. ICML (1) 2013: 187-195 - [c8]Aditya Krishna Menon, Harikrishna Narasimhan, Shivani Agarwal, Sanjay Chawla:
On the Statistical Consistency of Algorithms for Binary Classification under Class Imbalance. ICML (3) 2013: 603-611 - [c7]Kuat Yessenov, Shubham Tulsiani, Aditya Krishna Menon, Robert C. Miller, Sumit Gulwani, Butler W. Lampson, Adam Kalai:
A colorful approach to text processing by example. UIST 2013: 495-504 - 2012
- [c6]Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado:
Predicting accurate probabilities with a ranking loss. ICML 2012 - [c5]Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan:
Learning and Inference in Probabilistic Classifier Chains with Beam Search. ECML/PKDD (1) 2012: 665-680 - 2011
- [c4]Aditya Krishna Menon, Krishna Prasad Chitrapura, Sachin Garg, Deepak Agarwal, Nagaraj Kota:
Response prediction using collaborative filtering with hierarchies and side-information. KDD 2011: 141-149 - [c3]Aditya Krishna Menon, Charles Elkan:
Link Prediction via Matrix Factorization. ECML/PKDD (2) 2011: 437-452 - 2010
- [c2]Aditya Krishna Menon, Charles Elkan:
A Log-Linear Model with Latent Features for Dyadic Prediction. ICDM 2010: 364-373 - 2007
- [c1]Aditya Krishna Menon, Gia Vinh Anh Pham, Sanjay Chawla, Anastasios Viglas:
An incremental data-stream sketch using sparse random projections. SDM 2007: 563-568
Informal and Other Publications
- 2024
- [i57]Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix X. Yu, Sanjiv Kumar:
Metric-aware LLM inference. CoRR abs/2403.04182 (2024) - [i56]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-level uncertainty and beyond. CoRR abs/2404.10136 (2024) - 2023
- [i55]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Sadeep Jayasumana, Veeranjaneyulu Sadhanala, Wittawat Jitkrittum, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
EmbedDistill: A Geometric Knowledge Distillation for Information Retrieval. CoRR abs/2301.12005 (2023) - [i54]Hrayr Harutyunyan, Ankit Singh Rawat, Aditya Krishna Menon, Seungyeon Kim, Sanjiv Kumar:
Supervision Complexity and its Role in Knowledge Distillation. CoRR abs/2301.12245 (2023) - [i53]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Learning to reject meets OOD detection: Are all abstentions created equal? CoRR abs/2301.12386 (2023) - [i52]Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar:
On student-teacher deviations in distillation: does it pay to disobey? CoRR abs/2301.12923 (2023) - [i51]Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar:
ResMem: Learn what you can and memorize the rest. CoRR abs/2302.01576 (2023) - [i50]Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar:
When Does Confidence-Based Cascade Deferral Suffice? CoRR abs/2307.02764 (2023) - [i49]Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon:
The importance of feature preprocessing for differentially private linear optimization. CoRR abs/2307.11106 (2023) - [i48]Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan:
Think before you speak: Training Language Models With Pause Tokens. CoRR abs/2310.02226 (2023) - [i47]Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? CoRR abs/2310.05337 (2023) - [i46]Yongchao Zhou, Kaifeng Lyu, Ankit Singh Rawat, Aditya Krishna Menon, Afshin Rostamizadeh, Sanjiv Kumar, Jean-François Kagy, Rishabh Agarwal:
DistillSpec: Improving Speculative Decoding via Knowledge Distillation. CoRR abs/2310.08461 (2023) - 2022
- [i45]Wittawat Jitkrittum, Aditya Krishna Menon, Ankit Singh Rawat, Sanjiv Kumar:
ELM: Embedding and Logit Margins for Long-Tail Learning. CoRR abs/2204.13208 (2022) - [i44]Serena Lutong Wang, Harikrishna Narasimhan, Yichen Zhou, Sara Hooker, Michal Lukasik, Aditya Krishna Menon:
Robust Distillation for Worst-class Performance. CoRR abs/2206.06479 (2022) - [i43]Arslan Chaudhry, Aditya Krishna Menon, Andreas Veit, Sadeep Jayasumana, Srikumar Ramalingam, Sanjiv Kumar:
When does mixup promote local linearity in learned representations? CoRR abs/2210.16413 (2022) - 2021
- [i42]Srinadh Bhojanapalli, Kimberly Wilber, Andreas Veit, Ankit Singh Rawat, Seungyeon Kim, Aditya Krishna Menon, Sanjiv Kumar:
On the Reproducibility of Neural Network Predictions. CoRR abs/2102.03349 (2021) - [i41]Andrew Cotter, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sashank J. Reddi, Yichen Zhou:
Distilling Double Descent. CoRR abs/2102.06849 (2021) - [i40]Marian-Andrei Rizoiu, Alexander Soen, Shidi Li, Leanne Dong, Aditya Krishna Menon, Lexing Xie:
Interval-censored Hawkes processes. CoRR abs/2104.07932 (2021) - [i39]Ankit Singh Rawat, Aditya Krishna Menon, Wittawat Jitkrittum, Sadeep Jayasumana, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces. CoRR abs/2105.05736 (2021) - [i38]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Teacher's pet: understanding and mitigating biases in distillation. CoRR abs/2106.10494 (2021) - [i37]Harikrishna Narasimhan, Aditya Krishna Menon:
Training Over-parameterized Models with Non-decomposable Objectives. CoRR abs/2107.04641 (2021) - [i36]Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Amr Ahmed, Sanjiv Kumar:
When in Doubt, Summon the Titans: Efficient Inference with Large Models. CoRR abs/2110.10305 (2021) - 2020
- [i35]Richard Nock, Aditya Krishna Menon:
Supervised Learning: No Loss No Cry. CoRR abs/2002.03555 (2020) - [i34]Michal Lukasik, Srinadh Bhojanapalli, Aditya Krishna Menon, Sanjiv Kumar:
Does label smoothing mitigate label noise? CoRR abs/2003.02819 (2020) - [i33]Melanie Weber, Manzil Zaheer, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Robust Large-Margin Learning in Hyperbolic Space. CoRR abs/2004.05465 (2020) - [i32]Felix X. Yu, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Federated Learning with Only Positive Labels. CoRR abs/2004.10342 (2020) - [i31]Ankit Singh Rawat, Aditya Krishna Menon, Andreas Veit, Felix X. Yu, Sashank J. Reddi, Sanjiv Kumar:
Doubly-stochastic mining for heterogeneous retrieval. CoRR abs/2004.10915 (2020) - [i30]Aditya Krishna Menon, Ankit Singh Rawat, Sashank J. Reddi, Seungyeon Kim, Sanjiv Kumar:
Why distillation helps: a statistical perspective. CoRR abs/2005.10419 (2020) - [i29]Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, Sanjiv Kumar:
Long-tail learning via logit adjustment. CoRR abs/2007.07314 (2020) - [i28]Tiansheng Yao, Xinyang Yi, Derek Zhiyuan Cheng, Felix X. Yu, Aditya Krishna Menon, Lichan Hong, Ed H. Chi, Steve Tjoa, Jieqi Kang, Evan Ettinger:
Self-supervised Learning for Deep Models in Recommendations. CoRR abs/2007.12865 (2020) - [i27]Umanga Bista, Alexander Patrick Mathews, Aditya Krishna Menon, Lexing Xie:
SupMMD: A Sentence Importance Model for Extractive Summarization using Maximum Mean Discrepancy. CoRR abs/2010.02568 (2020) - [i26]Michal Lukasik, Himanshu Jain, Aditya Krishna Menon, Seungyeon Kim, Srinadh Bhojanapalli, Felix X. Yu, Sanjiv Kumar:
Semantic Label Smoothing for Sequence to Sequence Problems. CoRR abs/2010.07447 (2020) - [i25]Jingzhao Zhang, Aditya Krishna Menon, Andreas Veit, Srinadh Bhojanapalli, Sanjiv Kumar, Suvrit Sra:
Coping with Label Shift via Distributionally Robust Optimisation. CoRR abs/2010.12230 (2020) - 2019
- [i24]Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon:
Cold-start Playlist Recommendation with Multitask Learning. CoRR abs/1901.06125 (2019) - [i23]Robert C. Williamson, Aditya Krishna Menon:
Fairness risk measures. CoRR abs/1901.08665 (2019) - [i22]Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon, Nakul Verma:
Noise-tolerant fair classification. CoRR abs/1901.10837 (2019) - [i21]Aditya Krishna Menon, Anand Rajagopalan, Baris Sumengen, Gui Citovsky, Qin Cao, Sanjiv Kumar:
Online Hierarchical Clustering Approximations. CoRR abs/1909.09667 (2019) - 2018
- [i20]Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla:
Anomaly Detection using One-Class Neural Networks. CoRR abs/1802.06360 (2018) - [i19]Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian J. Walder:
Monge beats Bayes: Hardness Results for Adversarial Training. CoRR abs/1806.02977 (2018) - [i18]Nan Lu, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
On the Minimal Supervision for Training Any Binary Classifier from Only Unlabeled Data. CoRR abs/1808.10585 (2018) - [i17]Takashi Ishida, Gang Niu, Aditya Krishna Menon, Masashi Sugiyama:
Complementary-Label Learning for Arbitrary Losses and Models. CoRR abs/1810.04327 (2018) - [i16]Umanga Bista, Alexander Patrick Mathews, Minjeong Shin, Aditya Krishna Menon, Lexing Xie:
Comparative Document Summarisation via Classification. CoRR abs/1812.02171 (2018) - [i15]Dawei Chen, Cheng Soon Ong, Aditya Krishna Menon:
Cold-start playlist recommendation with multitask learning. PeerJ Prepr. 6: e27383 (2018) - 2017
- [i14]Raghavendra Chalapathy, Aditya Krishna Menon, Sanjay Chawla:
Robust, Deep and Inductive Anomaly Detection. CoRR abs/1704.06743 (2017) - [i13]Aditya Krishna Menon, Robert C. Williamson:
The cost of fairness in classification. CoRR abs/1705.09055 (2017) - [i12]Dawei Chen, Lexing Xie, Aditya Krishna Menon, Cheng Soon Ong:
Structured Recommendation. CoRR abs/1706.09067 (2017) - [i11]Dawei Chen, Dongwoo Kim, Lexing Xie, Minjeong Shin, Aditya Krishna Menon, Cheng Soon Ong, Iman Avazpour, John Grundy:
PathRec: Visual Analysis of Travel Route Recommendations. CoRR abs/1707.01627 (2017) - [i10]Richard Nock, Zac Cranko, Aditya Krishna Menon, Lizhen Qu, Robert C. Williamson:
f-GANs in an Information Geometric Nutshell. CoRR abs/1707.04385 (2017) - [i9]Aditya Krishna Menon, Dawei Chen, Lexing Xie, Cheng Soon Ong:
Revisiting revisits in trajectory recommendation. CoRR abs/1708.05165 (2017) - 2016
- [i8]Aditya Krishna Menon, Brendan van Rooyen, Nagarajan Natarajan:
Learning from Binary Labels with Instance-Dependent Corruption. CoRR abs/1605.00751 (2016) - [i7]Richard Nock, Aditya Krishna Menon, Cheng Soon Ong:
A scaled Bregman theorem with applications. CoRR abs/1607.00360 (2016) - [i6]Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, Lizhen Qu:
Making Neural Networks Robust to Label Noise: a Loss Correction Approach. CoRR abs/1609.03683 (2016) - 2015
- [i5]Brendan van Rooyen, Aditya Krishna Menon, Robert C. Williamson:
Learning with Symmetric Label Noise: The Importance of Being Unhinged. CoRR abs/1505.07634 (2015) - [i4]Brendan van Rooyen, Aditya Krishna Menon:
An Average Classification Algorithm. CoRR abs/1506.01520 (2015) - 2012
- [i3]Aditya Krishna Menon, Xiaoqian Jiang, Shankar Vembu, Charles Elkan, Lucila Ohno-Machado:
Predicting accurate probabilities with a ranking loss. CoRR abs/1206.4661 (2012) - [i2]Aditya Krishna Menon, Omer Tamuz, Sumit Gulwani, Butler W. Lampson, Adam Tauman Kalai:
Textual Features for Programming by Example. CoRR abs/1209.3811 (2012) - 2010
- [i1]Aditya Krishna Menon, Charles Elkan:
Dyadic Prediction Using a Latent Feature Log-Linear Model. CoRR abs/1006.2156 (2010)
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the dblp computer science bibliography is funded and supported by:
