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ORCIDAditya Krishna Menon
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- affiliation: NICTA, Canberra, Australia
- affiliation: Australian National University, College of Engineering & Computer Science
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2020 – today
- 2024
[j10]Michal Lukasik, Vaishnavh Nagarajan, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
What do larger image classifiers memorise? Trans. Mach. Learn. Res. 2024 (2024)- [c62]Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar:
Regression Aware Inference with LLMs. EMNLP (Findings) 2024: 13667-13678 - [c61]Sachin Goyal, Ziwei Ji, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar, Vaishnavh Nagarajan:
Think before you speak: Training Language Models With Pause Tokens. ICLR 2024 - [c60]Neha Gupta, Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Aditya Krishna Menon, Sanjiv Kumar:
Language Model Cascades: Token-Level Uncertainty And Beyond. ICLR 2024 - [c59]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Neha Gupta, Sanjiv Kumar:
Learning to Reject Meets Long-tail Learning. ICLR 2024 - [c58]Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Sanjiv Kumar:
Plugin estimators for selective classification with out-of-distribution detection. ICLR 2024 - [c57]Ziteng Sun, Ananda Theertha Suresh, Aditya Krishna Menon:
The importance of feature preprocessing for differentially private linear optimization. ICLR 2024 - [c56]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. ICLR 2024 - [c55]Seungyeon Kim, Ankit Singh Rawat, Manzil Zaheer, Wittawat Jitkrittum, Veeranjaneyulu Sadhanala, Sadeep Jayasumana, Aditya Krishna Menon, Rob Fergus, Sanjiv Kumar:
USTAD: Unified Single-model Training Achieving Diverse Scores for Information Retrieval. ICML 2024 - [i61]Michal Lukasik, Harikrishna Narasimhan, Aditya Krishna Menon, Felix X. Yu, Sanjiv Kumar:
Metric-aware LLM inference. CoRR abs/2403.04182 (2024) - [i60]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) - [i59]Harikrishna Narasimhan, Wittawat Jitkrittum, Ankit Singh Rawat, Seungyeon Kim, Neha Gupta, Aditya Krishna Menon, Sanjiv Kumar:
Faster Cascades via Speculative Decoding. CoRR abs/2405.19261 (2024) - [i58]Congchao Wang, Sean Augenstein, Keith Rush, Wittawat Jitkrittum, Harikrishna Narasimhan, Ankit Singh Rawat, Aditya Krishna Menon, Alec Go:
Cascade-Aware Training of Language Models. CoRR abs/2406.00060 (2024) - [i57]Ziwei Ji, Himanshu Jain, Andreas Veit, Sashank J. Reddi, Sadeep Jayasumana, Ankit Singh Rawat, Aditya Krishna Menon, Felix Yu, Sanjiv Kumar:
Efficient Document Ranking with Learnable Late Interactions. CoRR abs/2406.17968 (2024) - [i56]Ankit Singh Rawat, Veeranjaneyulu Sadhanala, Afshin Rostamizadeh, Ayan Chakrabarti, Wittawat Jitkrittum, Vladimir Feinberg, Seungyeon Kim, Hrayr Harutyunyan, Nikunj Saunshi, Zachary Nado, Rakesh Shivanna, Sashank J. Reddi, Aditya Krishna Menon, Rohan Anil, Sanjiv Kumar:
A Little Help Goes a Long Way: Efficient LLM Training by Leveraging Small LMs. CoRR abs/2410.18779 (2024) - 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 - [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
- [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) - [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 - [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
- [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 - [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
- [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 - [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)
2010 – 2019
- 2019
- [j7]Aditya Krishna Menon:
The risk of trivial solutions in bipartite top ranking. Mach. Learn. 108(4): 627-658 (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 - [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
- [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) - [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 - [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
- [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 - [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
- [j5]Aditya Krishna Menon, Robert C. Williamson:
Bipartite Ranking: a Risk-Theoretic Perspective. J. Mach. Learn. Res. 17: 195:1-195:102 (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 - [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
- [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 - [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) - 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) - [c10]Aditya Krishna Menon, Robert C. Williamson:
Bayes-Optimal Scorers for Bipartite Ranking. COLT 2014: 68-106 - 2013
- [b1]Aditya Krishna Menon:
Latent feature models for dyadic prediction /. University of California, San Diego, USA, 2013 - [j3]Abhishek Kumar, Shankar Vembu, Aditya Krishna Menon, Charles Elkan:
Beam search algorithms for multilabel learning. Mach. Learn. 92(1): 65-89 (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 - [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) - 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) - [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
- [j1]Aditya Krishna Menon, Charles Elkan:
Predicting labels for dyadic data. Data Min. Knowl. Discov. 21(2): 327-343 (2010) - [c2]Aditya Krishna Menon, Charles Elkan:
A Log-Linear Model with Latent Features for Dyadic Prediction. ICDM 2010: 364-373 - [i1]Aditya Krishna Menon, Charles Elkan:
Dyadic Prediction Using a Latent Feature Log-Linear Model. CoRR abs/1006.2156 (2010)
2000 – 2009
- 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
Coauthor Index
[j10] [c62] [c61] [c60] [c59] [c58] [c56] [c55] [i61] [i60] [i59] [i57] [i56] [c54] [c53] [c52] [c51] [i55] [i54] [i53] [i52] [i51] [i50] [i48] [i47] [i46] [j8] [c49] [c48] [i45] [i43] [c47] [c45] [c44] [c43] [c42] [c41] [i42] [i39] [i38] [i36] [c38] [c37] [c36] [c34] [c33] [i34] [i33] [i32] [i31] [i30] [i29] [i26] [i25] [i21]
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