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ORCIDAlexander J. Smola
Alexander Johannes Smola – Alex J. Smola – Alex Smola
Person information
- affiliation: Carnegie Mellon University
- affiliation: Google Research
- affiliation: Yahoo! Research, Santa Clara
- affiliation: NICTA, Canberra Research Laboratory
- affiliation: Australian National University, Machine Learning Group
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2020 – today
- 2024
[j45]Yi Zhu
, Zhongyue Zhang, Chongruo Wu, Zhi Zhang, Tong He, Hang Zhang, R. Manmatha, Mu Li, Alexander J. Smola:
Improving Semantic Segmentation via Efficient Self-Training. IEEE Trans. Pattern Anal. Mach. Intell. 46(3): 1589-1602 (2024)- [j44]Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola:
Multimodal Chain-of-Thought Reasoning in Language Models. Trans. Mach. Learn. Res. 2024 (2024) - [c223]Rasool Fakoor, Jonas Mueller, Zachary Chase Lipton, Pratik Chaudhari, Alex Smola:
Time-Varying Propensity Score to Bridge the Gap between the Past and Present. ICLR 2024 - [i84]Guneet S. Dhillon, Xingjian Shi, Yee Whye Teh, Alex Smola:
L3Ms - Lagrange Large Language Models. CoRR abs/2410.21533 (2024) - 2023
- [j43]Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani:
Flexible Model Aggregation for Quantile Regression. J. Mach. Learn. Res. 24: 162:1-162:45 (2023) - [c222]Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang:
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. EMNLP 2023: 4765-4775 - [c221]Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola:
Automatic Chain of Thought Prompting in Large Language Models. ICLR 2023 - [c220]Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang:
Parameter-Efficient Fine-Tuning Design Spaces. ICLR 2023 - [c219]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary Chase Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. ICML 2023: 10879-10928 - [c218]Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alexander J. Smola, Xu Sun:
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. NeurIPS 2023 - [i83]Jiaao Chen, Aston Zhang, Xingjian Shi, Mu Li, Alex Smola, Diyi Yang:
Parameter-Efficient Fine-Tuning Design Spaces. CoRR abs/2301.01821 (2023) - [i82]Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola:
Multimodal Chain-of-Thought Reasoning in Language Models. CoRR abs/2302.00923 (2023) - [i81]Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
RLSbench: Domain Adaptation Under Relaxed Label Shift. CoRR abs/2302.03020 (2023) - [i80]Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alex Smola, Xu Sun:
Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. CoRR abs/2304.04704 (2023) - [i79]Jiaao Chen, Aston Zhang, Mu Li, Alex Smola, Diyi Yang:
A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. CoRR abs/2304.04746 (2023) - 2022
- [j42]Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin, Yuchen Fang, Yong Yu, Zheng Zhang, Alexander J. Smola:
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network. ACM Trans. Inf. Syst. 40(3): 45:1-45:35 (2022) - [c217]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander J. Smola:
ResNeSt: Split-Attention Networks. CVPR Workshops 2022: 2735-2745 - [c216]Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex J. Smola, Zhangyang Wang:
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. ICML 2022: 23446-23458 - [c215]Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola:
BLISS: A Billion scale Index using Iterative Re-partitioning. KDD 2022: 486-495 - [c214]Nick Erickson, Xingjian Shi, James Sharpnack, Alexander J. Smola:
Multimodal AutoML for Image, Text and Tabular Data. KDD 2022: 4786-4787 - [c213]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J. Smola:
Faster Deep Reinforcement Learning with Slower Online Network. NeurIPS 2022 - [c212]Benjamin Coleman, Santiago Segarra, Alexander J. Smola, Anshumali Shrivastava:
Graph Reordering for Cache-Efficient Near Neighbor Search. NeurIPS 2022 - [c211]Martin Klissarov, Rasool Fakoor, Jonas W. Mueller, Kavosh Asadi, Taesup Kim, Alexander J. Smola:
Adaptive Interest for Emphatic Reinforcement Learning. NeurIPS 2022 - [i78]Haotao Wang, Aston Zhang, Yi Zhu, Shuai Zheng, Mu Li, Alex Smola, Zhangyang Wang:
Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. CoRR abs/2207.01160 (2022) - [i77]Rasool Fakoor, Jonas Mueller, Zachary C. Lipton, Pratik Chaudhari, Alexander J. Smola:
Data drift correction via time-varying importance weight estimator. CoRR abs/2210.01422 (2022) - [i76]Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola:
Automatic Chain of Thought Prompting in Large Language Models. CoRR abs/2210.03493 (2022) - 2021
- [c210]Aashiq Muhamed, Liang Li, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alexander J. Smola:
Symbolic Music Generation with Transformer-GANs. AAAI 2021: 408-417 - [c209]Saurabh Garg, Yifan Wu, Alexander J. Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. NeurIPS 2021: 8532-8544 - [c208]Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J. Smola:
Continuous Doubly Constrained Batch Reinforcement Learning. NeurIPS 2021: 11260-11273 - [c207]Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Bernie Wang, Tim Januschowski:
Deep Explicit Duration Switching Models for Time Series. NeurIPS 2021: 29949-29961 - [c206]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. NeurIPS Datasets and Benchmarks 2021 - [e3]Alex Smola, Alex Dimakis, Ion Stoica:
Proceedings of the Fourth Conference on Machine Learning and Systems, MLSys 2021, virtual, April 5-9, 2021. mlsys.org 2021 [contents] - [i75]Rasool Fakoor, Jonas Mueller, Pratik Chaudhari, Alexander J. Smola:
Continuous Doubly Constrained Batch Reinforcement Learning. CoRR abs/2102.09225 (2021) - [i74]Taesup Kim, Rasool Fakoor, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani:
Deep Quantile Aggregation. CoRR abs/2103.00083 (2021) - [i73]Gaurav Gupta, Tharun Medini, Anshumali Shrivastava, Alexander J. Smola:
IRLI: Iterative Re-partitioning for Learning to Index. CoRR abs/2103.09944 (2021) - [i72]Benjamin Coleman, Santiago Segarra, Anshumali Shrivastava, Alex Smola:
Graph Reordering for Cache-Efficient Near Neighbor Search. CoRR abs/2104.03221 (2021) - [i71]Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola:
Dive into Deep Learning. CoRR abs/2106.11342 (2021) - [i70]Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski:
Deep Explicit Duration Switching Models for Time Series. CoRR abs/2110.13878 (2021) - [i69]Saurabh Garg, Yifan Wu, Alex Smola, Sivaraman Balakrishnan, Zachary C. Lipton:
Mixture Proportion Estimation and PU Learning: A Modern Approach. CoRR abs/2111.00980 (2021) - [i68]Xingjian Shi, Jonas Mueller, Nick Erickson, Mu Li, Alexander J. Smola:
Benchmarking Multimodal AutoML for Tabular Data with Text Fields. CoRR abs/2111.02705 (2021) - [i67]Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Michael L. Littman, Alexander J. Smola:
Deep Q-Network with Proximal Iteration. CoRR abs/2112.05848 (2021) - 2020
- [c205]Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola:
Meta-Q-Learning. ICLR 2020 - [c204]Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola:
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph. KDD 2020: 75-84 - [c203]Jonas Mueller, Xingjian Shi, Alexander J. Smola:
Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data. KDD 2020: 3509-3510 - [c202]Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. NeurIPS 2020 - [c201]Edo Liberty, Zohar S. Karnin, Bing Xiang, Laurence Rouesnel, Baris Coskun, Ramesh Nallapati, Julio Delgado, Amir Sadoughi, Yury Astashonok, Piali Das, Can Balioglu, Saswata Chakravarty, Madhav Jha, Philip Gautier, David Arpin, Tim Januschowski, Valentin Flunkert, Yuyang Wang, Jan Gasthaus, Lorenzo Stella, Syama Sundar Rangapuram, David Salinas, Sebastian Schelter, Alex Smola:
Elastic Machine Learning Algorithms in Amazon SageMaker. SIGMOD Conference 2020: 731-737 - [i66]Chenguang Wang, Zihao Ye, Aston Zhang, Zheng Zhang, Alexander J. Smola:
Transformer on a Diet. CoRR abs/2002.06170 (2020) - [i65]Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander J. Smola:
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. CoRR abs/2003.06505 (2020) - [i64]Rasool Fakoor, Pratik Chaudhari, Jonas Mueller, Alexander J. Smola:
TraDE: Transformers for Density Estimation. CoRR abs/2004.02441 (2020) - [i63]Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander J. Smola:
ResNeSt: Split-Attention Networks. CoRR abs/2004.08955 (2020) - [i62]Yi Zhu, Zhongyue Zhang, Chongruo Wu, Zhi Zhang, Tong He, Hang Zhang, R. Manmatha, Mu Li, Alexander J. Smola:
Improving Semantic Segmentation via Self-Training. CoRR abs/2004.14960 (2020) - [i61]Hyokun Yun, Michael Froh, Roshan Makhijani, Brian Luc, Alex Smola, Trishul Chilimbi:
Tiering as a Stochastic Submodular Optimization Problem. CoRR abs/2005.07893 (2020) - [i60]Rasool Fakoor, Jonas Mueller, Nick Erickson, Pratik Chaudhari, Alexander J. Smola:
Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. CoRR abs/2006.14284 (2020) - [i59]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning. CoRR abs/2006.15199 (2020) - [i58]Jiarui Jin, Jiarui Qin, Yuchen Fang, Kounianhua Du, Weinan Zhang, Yong Yu, Zheng Zhang, Alexander J. Smola:
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph. CoRR abs/2007.00216 (2020) - [i57]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (2020) - [i56]Jiarui Jin, Kounianhua Du, Weinan Zhang, Jiarui Qin, Yuchen Fang, Yong Yu, Zheng Zhang, Alexander J. Smola:
Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network. CoRR abs/2011.12683 (2020)
2010 – 2019
- 2019
- [c200]Haibin Lin, Xingjian Shi, Leonard Lausen, Aston Zhang, He He, Sheng Zha, Alexander J. Smola:
Dive into Deep Learning for Natural Language Processing. EMNLP/IJCNLP (2) 2019 - [c199]Jonas Mueller, Alex Smola:
Recognizing Variables from Their Data via Deep Embeddings of Distributions. ICDM 2019: 1264-1269 - [c198]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. ICML 2019: 6607-6617 - [c197]Ali Caner Türkmen, Yuyang Wang, Alexander J. Smola:
FastPoint: Scalable Deep Point Processes. ECML/PKDD (2) 2019: 465-480 - [c196]Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon:
Efficient Multitask Feature and Relationship Learning. UAI 2019: 777-787 - [c195]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
P3O: Policy-on Policy-off Policy Optimization. UAI 2019: 1017-1027 - [i55]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric S. Chung, Bill Dally, Jeff Dean, Inderjit S. Dhillon, Alexandros G. Dimakis, Pradeep Dubey, Charles Elkan, Grigori Fursin, Gregory R. Ganger, Lise Getoor, Phillip B. Gibbons, Garth A. Gibson, Joseph E. Gonzalez, Justin Gottschlich, Song Han, Kim M. Hazelwood, Furong Huang, Martin Jaggi, Kevin G. Jamieson, Michael I. Jordan, Gauri Joshi, Rania Khalaf, Jason Knight, Jakub Konecný, Tim Kraska, Arun Kumar, Anastasios Kyrillidis, Jing Li, Samuel Madden, H. Brendan McMahan, Erik Meijer, Ioannis Mitliagkas, Rajat Monga, Derek Gordon Murray, Dimitris S. Papailiopoulos, Gennady Pekhimenko, Theodoros Rekatsinas, Afshin Rostamizadeh, Christopher Ré, Christopher De Sa, Hanie Sedghi, Siddhartha Sen, Virginia Smith, Alex Smola, Dawn Song, Evan Randall Sparks, Ion Stoica, Vivienne Sze, Madeleine Udell, Joaquin Vanschoren, Shivaram Venkataraman, Rashmi Vinayak, Markus Weimer, Andrew Gordon Wilson, Eric P. Xing, Matei Zaharia, Ce Zhang, Ameet Talwalkar:
SysML: The New Frontier of Machine Learning Systems. CoRR abs/1904.03257 (2019) - [i54]Chenguang Wang, Mu Li, Alexander J. Smola:
Language Models with Transformers. CoRR abs/1904.09408 (2019) - [i53]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
P3O: Policy-on Policy-off Policy Optimization. CoRR abs/1905.01756 (2019) - [i52]Yuyang Wang, Alex Smola, Danielle C. Maddix, Jan Gasthaus, Dean P. Foster, Tim Januschowski:
Deep Factors for Forecasting. CoRR abs/1905.12417 (2019) - [i51]Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J. Smola, Zheng Zhang:
Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. CoRR abs/1909.01315 (2019) - [i50]Jonas Mueller, Alex Smola:
Recognizing Variables from their Data via Deep Embeddings of Distributions. CoRR abs/1909.04844 (2019) - [i49]Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola:
Meta-Q-Learning. CoRR abs/1910.00125 (2019) - 2018
- [c194]Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song:
Variational Reasoning for Question Answering With Knowledge Graph. AAAI 2018: 6069-6076 - [c193]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. AISTATS 2018: 1233-1242 - [c192]Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl:
Compressed Video Action Recognition. CVPR 2018: 6026-6035 - [c191]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. ICLR (Poster) 2018 - [c190]Hanjun Dai, Zornitsa Kozareva, Bo Dai, Alexander J. Smola, Le Song:
Learning Steady-States of Iterative Algorithms over Graphs. ICML 2018: 1114-1122 - [c189]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. ICML 2018: 3128-3136 - [c188]Alex Smola:
Algorithms, Data, Hardware and Tools: A Perfect Storm. KDD 2018: 2878 - [i48]Zachary C. Lipton, Yu-Xiang Wang, Alexander J. Smola:
Detecting and Correcting for Label Shift with Black Box Predictors. CoRR abs/1802.03916 (2018) - [i47]Emmanouil Antonios Platanios, Alex Smola:
Deep Graphs. CoRR abs/1806.01235 (2018) - [i46]Danielle C. Maddix, Yuyang Wang, Alex Smola:
Deep Factors with Gaussian Processes for Forecasting. CoRR abs/1812.00098 (2018) - 2017
- [c187]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AAAI Workshops 2017 - [c186]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. AISTATS 2017: 662-671 - [c185]Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. AISTATS 2017: 794-802 - [c184]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. AKBC@NIPS 2017 - [c183]Zichao Yang, Zhiting Hu, Yuntian Deng, Chris Dyer, Alexander J. Smola:
Neural Machine Translation with Recurrent Attention Modeling. EACL (2) 2017: 383-387 - [c182]R. Manmatha, Chao-Yuan Wu, Alexander J. Smola, Philipp Krähenbühl:
Sampling Matters in Deep Embedding Learning. ICCV 2017: 2859-2867 - [c181]Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) 2017 - [c180]Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola:
Joint Training of Ratings and Reviews with Recurrent Recommender Networks. ICLR (Workshop) 2017 - [c179]Manzil Zaheer, Amr Ahmed, Alexander J. Smola:
Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data. ICML 2017: 3967-3976 - [c178]Manzil Zaheer, Satwik Kottur, Amr Ahmed, José M. F. Moura, Alexander J. Smola:
Canopy Fast Sampling with Cover Trees. ICML 2017: 3977-3986 - [c177]Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola:
Deep Sets. NIPS 2017: 3391-3401 - [c176]Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola, How Jing:
Recurrent Recommender Networks. WSDM 2017: 495-503 - [c175]How Jing, Alexander J. Smola:
Neural Survival Recommender. WSDM 2017: 515-524 - [i45]Han Zhao, Otilia Stretcu, Renato Negrinho, Alexander J. Smola, Geoffrey J. Gordon:
Efficient Multi-task Feature and Relationship Learning. CoRR abs/1702.04423 (2017) - [i44]Joachim de Curtò, Irene Zarza, Feng Yang, Alexander J. Smola, Luc Van Gool:
F2F: A Library For Fast Kernel Expansions. CoRR abs/1702.08159 (2017) - [i43]Joachim de Curtò, Irene Zarza, Alexander J. Smola, Luc Van Gool:
HashBox: Hash Hierarchical Segmentation exploiting Bounding Box Object Detection. CoRR abs/1702.08160 (2017) - [i42]Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabás Póczos, Ruslan Salakhutdinov, Alexander J. Smola:
Deep Sets. CoRR abs/1703.06114 (2017) - [i41]Hsiao-Yu Fish Tung, Chao-Yuan Wu, Manzil Zaheer, Alexander J. Smola:
Spectral Methods for Nonparametric Models. CoRR abs/1704.00003 (2017) - [i40]Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl:
Sampling Matters in Deep Embedding Learning. CoRR abs/1706.07567 (2017) - [i39]Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola:
A Generic Approach for Escaping Saddle points. CoRR abs/1709.01434 (2017) - [i38]Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song:
Variational Reasoning for Question Answering with Knowledge Graph. CoRR abs/1709.04071 (2017) - [i37]Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alexander J. Smola, Andrew McCallum:
Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. CoRR abs/1711.05851 (2017) - [i36]Xun Zheng, Manzil Zaheer, Amr Ahmed, Yuan Wang, Eric P. Xing, Alexander J. Smola:
State Space LSTM Models with Particle MCMC Inference. CoRR abs/1711.11179 (2017) - [i35]Chao-Yuan Wu, Manzil Zaheer, Hexiang Hu, R. Manmatha, Alexander J. Smola, Philipp Krähenbühl:
Compressed Video Action Recognition. CoRR abs/1712.00636 (2017) - 2016
- [j41]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. J. Mach. Learn. Res. 17: 105:1-105:41 (2016) - [j40]Seth R. Flaxman, Daniel B. Neill, Alexander J. Smola:
Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference. ACM Trans. Intell. Syst. Technol. 7(2): 22:1-22:23 (2016) - [c174]Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Optimization. AISTATS 2016: 957-965 - [c173]Manzil Zaheer, Michael L. Wick, Jean-Baptiste Tristan, Alexander J. Smola, Guy L. Steele Jr.:
Exponential Stochastic Cellular Automata for Massively Parallel Inference. AISTATS 2016: 966-975 - [c172]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe methods for nonconvex optimization. Allerton 2016: 1244-1251 - [c171]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast incremental method for smooth nonconvex optimization. CDC 2016: 1971-1977 - [c170]Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alexander J. Smola:
Stacked Attention Networks for Image Question Answering. CVPR 2016: 21-29 - [c169]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. ICML 2016: 314-323 - [c168]Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alexander J. Smola, Eduard H. Hovy:
Hierarchical Attention Networks for Document Classification. HLT-NAACL 2016: 1480-1489 - [c167]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. NIPS 2016: 1145-1153 - [c166]Kumar Avinava Dubey, Sashank J. Reddi, Sinead A. Williamson, Barnabás Póczos, Alexander J. Smola, Eric P. Xing:
Variance Reduction in Stochastic Gradient Langevin Dynamics. NIPS 2016: 1154-1162 - [c165]Chao-Yuan Wu, Christopher V. Alvino, Alexander J. Smola, Justin Basilico:
Using Navigation to Improve Recommendations in Real-Time. RecSys 2016: 341-348 - [c164]Mu Li, Ziqi Liu, Alexander J. Smola, Yu-Xiang Wang:
DiFacto: Distributed Factorization Machines. WSDM 2016: 377-386 - [c163]Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola:
Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering. WWW (Companion Volume) 2016: 127-128 - [e2]Balaji Krishnapuram, Mohak Shah, Alexander J. Smola, Charu C. Aggarwal, Dou Shen, Rajeev Rastogi:
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13-17, 2016. ACM 2016, ISBN 978-1-4503-4232-2 [contents] - [i34]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Incremental Method for Nonconvex Optimization. CoRR abs/1603.06159 (2016) - [i33]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Variance Reduction for Nonconvex Optimization. CoRR abs/1603.06160 (2016) - [i32]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. CoRR abs/1605.06900 (2016) - [i31]Zichao Yang, Zhiting Hu, Yuntian Deng, Chris Dyer, Alexander J. Smola:
Neural Machine Translation with Recurrent Attention Modeling. CoRR abs/1607.05108 (2016) - [i30]Sashank J. Reddi, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
Stochastic Frank-Wolfe Methods for Nonconvex Optimization. CoRR abs/1607.08254 (2016) - [i29]Sashank J. Reddi, Jakub Konecný, Peter Richtárik, Barnabás Póczos, Alexander J. Smola:
AIDE: Fast and Communication Efficient Distributed Optimization. CoRR abs/1608.06879 (2016) - [i28]Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng:
Attributing Hacks. CoRR abs/1611.03021 (2016) - [i27]Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR abs/1611.04488 (2016) - 2015
- [c162]Sashank Jakkam Reddi, Barnabás Póczos, Alexander J. Smola:
Doubly Robust Covariate Shift Correction. AAAI 2015: 2949-2955 - [c161]Jay Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola:
Preferential Attachment in Graphs with Affinities. AISTATS 2015 - [c160]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. AISTATS 2015 - [c159]Zichao Yang, Andrew Gordon Wilson, Alexander J. Smola, Le Song:
A la Carte - Learning Fast Kernels. AISTATS 2015 - [c158]Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alexander J. Smola, Le Song, Ziyu Wang:
Deep Fried Convnets. ICCV 2015: 1476-1483 - [c157]Seth R. Flaxman, Andrew Gordon Wilson, Daniel B. Neill, Hannes Nickisch, Alexander J. Smola:
Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. ICML 2015: 607-616 - [c156]Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. ICML 2015: 2493-2502 - [c155]Nan Du, Mehrdad Farajtabar, Amr Ahmed, Alexander J. Smola, Le Song:
Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams. KDD 2015: 219-228 - [c154]Seth R. Flaxman, Yu-Xiang Wang, Alexander J. Smola:
Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. KDD 2015: 289-298 - [c153]Li Zhou, David G. Andersen, Mu Li, Alexander J. Smola:
Cuckoo Linear Algebra. KDD 2015: 1553-1562 - [c152]Weinan Zhang, Amr Ahmed, Jie Yang, Vanja Josifovski, Alexander J. Smola:
Annotating Needles in the Haystack without Looking: Product Information Extraction from Emails. KDD 2015: 2257-2266 - [c151]Yining Wang, Hsiao-Yu Fish Tung, Alexander J. Smola, Anima Anandkumar:
Fast and Guaranteed Tensor Decomposition via Sketching. NIPS 2015: 991-999 - [c150]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. NIPS 2015: 2647-2655 - [c149]Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola:
Fast Differentially Private Matrix Factorization. RecSys 2015: 171-178 - [c148]Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola:
Communication Efficient Coresets for Empirical Loss Minimization. UAI 2015: 752-761 - [c147]Mu Li, Amr Ahmed, Alexander J. Smola:
Inferring Movement Trajectories from GPS Snippets. WSDM 2015: 325-334 - [c146]Alex Beutel, Amr Ahmed, Alexander J. Smola:
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. WWW 2015: 119-129 - [i26]Alex Beutel, Amr Ahmed, Alexander J. Smola:
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. CoRR abs/1501.00199 (2015) - [i25]Yu-Xiang Wang, Stephen E. Fienberg, Alexander J. Smola:
Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. CoRR abs/1502.07645 (2015) - [i24]Ziqi Liu, Yu-Xiang Wang, Alexander J. Smola:
Fast Differentially Private Matrix Factorization. CoRR abs/1505.01419 (2015) - [i23]Mu Li, Dave G. Andersen, Alexander J. Smola:
Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning. CoRR abs/1505.04636 (2015) - [i22]Yining Wang, Hsiao-Yu Fish Tung, Alexander J. Smola, Animashree Anandkumar:
Fast and Guaranteed Tensor Decomposition via Sketching. CoRR abs/1506.04448 (2015) - [i21]Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabás Póczos, Alexander J. Smola:
On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. CoRR abs/1506.06840 (2015) - [i20]Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola:
AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. CoRR abs/1508.05003 (2015) - [i19]Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, Alexander J. Smola:
Stacked Attention Networks for Image Question Answering. CoRR abs/1511.02274 (2015) - [i18]Chao-Yuan Wu, Alex Beutel, Amr Ahmed, Alexander J. Smola:
Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering. CoRR abs/1512.01845 (2015) - [i17]Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Maria-Florina Balcan, Alexander J. Smola:
Data Driven Resource Allocation for Distributed Learning. CoRR abs/1512.04848 (2015) - 2014
- [c145]Yu-Xiang Wang, Alexander J. Smola, Ryan J. Tibshirani:
The Falling Factorial Basis and Its Statistical Applications. ICML 2014: 730-738 - [c144]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. ICML 2014: 1359-1367 - [c143]Qiming Diao, Minghui Qiu, Chao-Yuan Wu, Alexander J. Smola, Jing Jiang, Chong Wang:
Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). KDD 2014: 193-202 - [c142]Mu Li, Tong Zhang, Yuqiang Chen, Alexander J. Smola:
Efficient mini-batch training for stochastic optimization. KDD 2014: 661-670 - [c141]Aaron Q. Li, Amr Ahmed, Sujith Ravi, Alexander J. Smola:
Reducing the sampling complexity of topic models. KDD 2014: 891-900 - [c140]Mu Li, David G. Andersen, Alexander J. Smola, Kai Yu:
Communication Efficient Distributed Machine Learning with the Parameter Server. NIPS 2014: 19-27 - [c139]Hsiao-Yu Fish Tung, Alexander J. Smola:
Spectral Methods for Indian Buffet Process Inference. NIPS 2014: 1484-1492 - [c138]Mu Li, David G. Andersen, Jun Woo Park, Alexander J. Smola, Amr Ahmed, Vanja Josifovski, James Long, Eugene J. Shekita, Bor-Yiing Su:
Scaling Distributed Machine Learning with the Parameter Server. OSDI 2014: 583-598 - [c137]Amr Ahmed, Abhimanyu Das, Alexander J. Smola:
Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising. WSDM 2014: 153-162 - [c136]Yuchen Zhang, Amr Ahmed, Vanja Josifovski, Alexander J. Smola:
Taxonomy discovery for personalized recommendation. WSDM 2014: 243-252 - [c135]Alex Beutel, Kenton Murray, Christos Faloutsos, Alexander J. Smola:
CoBaFi: collaborative bayesian filtering. WWW 2014: 97-108 - [i16]David Lopez-Paz, Suvrit Sra, Alexander J. Smola, Zoubin Ghahramani, Bernhard Schölkopf:
Randomized Nonlinear Component Analysis. CoRR abs/1402.0119 (2014) - [i15]Quoc Viet Le, Tamás Sarlós, Alexander Johannes Smola:
Fastfood: Approximate Kernel Expansions in Loglinear Time. CoRR abs/1408.3060 (2014) - [i14]Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani:
Trend Filtering on Graphs. CoRR abs/1410.7690 (2014) - [i13]Zichao Yang, Alexander J. Smola, Le Song, Andrew Gordon Wilson:
A la Carte - Learning Fast Kernels. CoRR abs/1412.6493 (2014) - [i12]Zichao Yang, Marcin Moczulski, Misha Denil, Nando de Freitas, Alexander J. Smola, Le Song, Ziyu Wang:
Deep Fried Convnets. CoRR abs/1412.7149 (2014) - 2013
- [c134]Chenhao Tan, Ed H. Chi, David A. Huffaker, Gueorgi Kossinets, Alexander J. Smola:
Instant foodie: predicting expert ratings from grassroots. CIKM 2013: 1127-1136 - [c133]Quoc V. Le, Tamás Sarlós, Alexander J. Smola:
Fastfood - Computing Hilbert Space Expansions in loglinear time. ICML (3) 2013: 244-252 - [c132]Amr Ahmed, Liangjie Hong, Alexander J. Smola:
Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling. ICML (3) 2013: 1426-1434 - [c131]Amr Ahmed, Alexander J. Smola:
The dataminer's guide to scalable mixed-membership and nonparametric bayesian models. KDD 2013: 1529 - [c130]Chong Wang, Xi Chen, Alexander J. Smola, Eric P. Xing:
Variance Reduction for Stochastic Gradient Optimization. NIPS 2013: 181-189 - [c129]Amr Ahmed, Liangjie Hong, Alexander J. Smola:
Hierarchical geographical modeling of user locations from social media posts. WWW 2013: 25-36 - [c128]Amr Ahmed, Nino Shervashidze, Shravan M. Narayanamurthy, Vanja Josifovski, Alexander J. Smola:
Distributed large-scale natural graph factorization. WWW 2013: 37-48 - [c127]Vidhya Navalpakkam, LaDawn Jentzsch, Rory Sayres, Sujith Ravi, Amr Ahmed, Alexander J. Smola:
Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts. WWW 2013: 953-964 - 2012
- [j39]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Two-Sample Test. J. Mach. Learn. Res. 13: 723-773 (2012) - [j38]Le Song, Alexander J. Smola, Arthur Gretton, Justin Bedo, Karsten M. Borgwardt:
Feature Selection via Dependence Maximization. J. Mach. Learn. Res. 13: 1393-1434 (2012) - [c126]Amr Ahmed, Mohamed Aly, Abhimanyu Das, Alexander J. Smola, Tasos Anastasakos:
Web-scale multi-task feature selection for behavioral targeting. CIKM 2012: 1737-1741 - [c125]Nando de Freitas, Alexander J. Smola, Masrour Zoghi:
Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations. ICML 2012 - [c124]Shin Matsushima, S. V. N. Vishwanathan, Alexander J. Smola:
Linear support vector machines via dual cached loops. KDD 2012: 177-185 - [c123]Nan Du, Le Song, Alexander J. Smola, Ming Yuan:
Learning Networks of Heterogeneous Influence. NIPS 2012: 2789-2797 - [c122]Amr Ahmed, Sujith Ravi, Shravan M. Narayanamurthy, Alexander J. Smola:
FastEx: Hash Clustering with Exponential Families. NIPS 2012: 2807-2815 - [c121]Shuang-Hong Yang, Alexander J. Smola, Bo Long, Hongyuan Zha, Yi Chang:
Friend or frenemy?: predicting signed ties in social networks. SIGIR 2012: 555-564 - [c120]Sergiy Matusevych, Alexander J. Smola, Amr Ahmed:
Hokusai - Sketching Streams in Real Time. UAI 2012: 594-603 - [c119]Amr Ahmed, Mohamed Aly, Joseph Gonzalez, Shravan M. Narayanamurthy, Alexander J. Smola:
Scalable inference in latent variable models. WSDM 2012: 123-132 - [c118]Amr Ahmed, Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola:
Fair and balanced: learning to present news stories. WSDM 2012: 333-342 - [c117]Liangjie Hong, Amr Ahmed, Siva Gurumurthy, Alexander J. Smola, Kostas Tsioutsiouliklis:
Discovering geographical topics in the twitter stream. WWW 2012: 769-778 - [i11]Nando de Freitas, Alexander J. Smola, Masrour Zoghi:
Regret Bounds for Deterministic Gaussian Process Bandits. CoRR abs/1203.2177 (2012) - [i10]Yutian Chen, Max Welling, Alexander J. Smola:
Super-Samples from Kernel Herding. CoRR abs/1203.3472 (2012) - [i9]Yasemin Altun, Alexander J. Smola, Thomas Hofmann:
Exponential Families for Conditional Random Fields. CoRR abs/1207.4131 (2012) - [i8]Sergiy Matusevych, Alexander J. Smola, Amr Ahmed:
Hokusai - Sketching Streams in Real Time. CoRR abs/1210.4891 (2012) - 2011
- [j37]Qinfeng Shi, Li Cheng, Li Wang, Alexander J. Smola:
Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models. Int. J. Comput. Vis. 93(1): 22-32 (2011) - [j36]Süreyya Özögür-Akyüz, Devrim Ünay, Alexander J. Smola:
Guest editorial: model selection and optimization in machine learning. Mach. Learn. 85(1-2): 1-2 (2011) - [c116]Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, Alexander J. Smola:
Scalable distributed inference of dynamic user interests for behavioral targeting. KDD 2011: 114-122 - [c115]Yucheng Low, Deepak Agarwal, Alexander J. Smola:
Multiple domain user personalization. KDD 2011: 123-131 - [c114]Shuang-Hong Yang, Bo Long, Alexander J. Smola, Hongyuan Zha, Zhaohui Zheng:
Collaborative competitive filtering: learning recommender using context of user choice. SIGIR 2011: 295-304 - [c113]Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, Alexander J. Smola:
Bid generation for advanced match in sponsored search. WSDM 2011: 515-524 - [c112]Srinivas Vadrevu, Choon Hui Teo, Suju Rajan, Kunal Punera, Byron Dom, Alexander J. Smola, Yi Chang, Zhaohui Zheng:
Scalable clustering of news search results. WSDM 2011: 675-684 - [c111]Amr Ahmed, Qirong Ho, Jacob Eisenstein, Eric P. Xing, Alexander J. Smola, Choon Hui Teo:
Unified analysis of streaming news. WWW 2011: 267-276 - [c110]Amr Ahmed, Alexander J. Smola:
WWW 2011 invited tutorial overview: latent variable models on the internet. WWW (Companion Volume) 2011: 281-282 - [c109]Shuang-Hong Yang, Bo Long, Alexander J. Smola, Narayanan Sadagopan, Zhaohui Zheng, Hongyuan Zha:
Like like alike: joint friendship and interest propagation in social networks. WWW 2011: 537-546 - [c108]Deepak Agarwal, Lihong Li, Alexander J. Smola:
Linear-Time Estimators for Propensity Scores. AISTATS 2011: 93-100 - [c107]Amr Ahmed, Qirong Ho, Choon Hui Teo, Jacob Eisenstein, Alexander J. Smola, Eric P. Xing:
Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. AISTATS 2011: 101-109 - [i7]Daniel J. Hsu, Nikos Karampatziakis, John Langford, Alexander J. Smola:
Parallel Online Learning. CoRR abs/1103.4204 (2011) - 2010
- [j35]Choon Hui Teo, S. V. N. Vishwanathan, Alexander J. Smola, Quoc V. Le:
Bundle Methods for Regularized Risk Minimization. J. Mach. Learn. Res. 11: 311-365 (2010) - [j34]Novi Quadrianto, Alexander J. Smola, Le Song, Tinne Tuytelaars:
Kernelized Sorting. IEEE Trans. Pattern Anal. Mach. Intell. 32(10): 1809-1821 (2010) - [j33]Owen Thomas, Peter Sunehag, Gideon Dror, Sungrack Yun, Sungwoong Kim, Matthew W. Robards, Alexander J. Smola, Daniel Green, Philo Saunders:
Wearable sensor activity analysis using semi-Markov models with a grammar. Pervasive Mob. Comput. 6(3): 342-350 (2010) - [j32]Alexander J. Smola, Shravan M. Narayanamurthy:
An Architecture for Parallel Topic Models. Proc. VLDB Endow. 3(1): 703-710 (2010) - [j31]Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt:
Discriminative frequent subgraph mining with optimality guarantees. Stat. Anal. Data Min. 3(5): 302-318 (2010) - [c106]Novi Quadrianto, Dale Schuurmans, Alexander J. Smola:
Distributed Flow Algorithms for Scalable Similarity Visualization. ICDM Workshops 2010: 1220-1227 - [c105]Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, Alexander J. Smola:
Hilbert Space Embeddings of Hidden Markov Models. ICML 2010: 991-998 - [c104]Gilbert Leung, Novi Quadrianto, Alexander J. Smola, Kostas Tsioutsiouliklis:
Optimal Web-Scale Tiering as a Flow Problem. NIPS 2010: 1333-1341 - [c103]James Petterson, Alexander J. Smola, Tibério S. Caetano, Wray L. Buntine, Shravan M. Narayanamurthy:
Word Features for Latent Dirichlet Allocation. NIPS 2010: 1921-1929 - [c102]Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, S. V. N. Vishwanathan, James Petterson:
Multitask Learning without Label Correspondences. NIPS 2010: 1957-1965 - [c101]Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li:
Parallelized Stochastic Gradient Descent. NIPS 2010: 2595-2603 - [c100]Yutian Chen, Max Welling, Alexander J. Smola:
Super-Samples from Kernel Herding. UAI 2010: 109-116 - [c99]Taesup Moon, Alexander J. Smola, Yi Chang, Zhaohui Zheng:
IntervalRank: isotonic regression with listwise and pairwise constraints. WSDM 2010: 151-160 - [c98]Alexandros Karatzoglou, Alexander J. Smola, Markus Weimer:
Collaborative Filtering on a Budget. AISTATS 2010: 389-396
2000 – 2009
- 2009
- [j30]Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, Quoc V. Le:
Estimating Labels from Label Proportions. J. Mach. Learn. Res. 10: 2349-2374 (2009) - [j29]Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, S. V. N. Vishwanathan:
Hash Kernels for Structured Data. J. Mach. Learn. Res. 10: 2615-2637 (2009) - [j28]Tibério S. Caetano, Julian J. McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola:
Learning Graph Matching. IEEE Trans. Pattern Anal. Mach. Intell. 31(6): 1048-1058 (2009) - [c97]Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu:
Hilbert space embeddings of conditional distributions with applications to dynamical systems. ICML 2009: 961-968 - [c96]Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg:
Feature hashing for large scale multitask learning. ICML 2009: 1113-1120 - [c95]Novi Quadrianto, James Petterson, Alexander J. Smola:
Distribution Matching for Transduction. NIPS 2009: 1500-1508 - [c94]Martin Zinkevich, Alexander J. Smola, John Langford:
Slow Learners are Fast. NIPS 2009: 2331-2339 - [c93]Marisa Thoma, Hong Cheng, Arthur Gretton, Jiawei Han, Hans-Peter Kriegel, Alexander J. Smola, Le Song, Philip S. Yu, Xifeng Yan, Karsten M. Borgwardt:
Near-optimal Supervised Feature Selection among Frequent Subgraphs. SDM 2009: 1076-1087 - [c92]Qinfeng Shi, James Petterson, Gideon Dror, John Langford, Alexander J. Smola, Alexander L. Strehl, Vishy Vishwanathan:
Hash Kernels. AISTATS 2009: 496-503 - [c91]Alexander J. Smola, Le Song, Choon Hui Teo:
Relative Novelty Detection. AISTATS 2009: 536-543 - [i6]Kilian Q. Weinberger, Anirban Dasgupta, Josh Attenberg, John Langford, Alexander J. Smola:
Feature Hashing for Large Scale Multitask Learning. CoRR abs/0902.2206 (2009) - 2008
- [j27]Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola:
Improving maximum margin matrix factorization. Mach. Learn. 72(3): 263-276 (2008) - [c90]Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola:
Discriminative human action segmentation and recognition using semi-Markov model. CVPR 2008 - [c89]Ahmed H. El Zein, Eric McCreath, Alistair P. Rendell, Alexander J. Smola:
Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning. ICCS (1) 2008: 466-475 - [c88]Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, Quoc V. Le:
Estimating labels from label proportions. ICML 2008: 776-783 - [c87]Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf:
Tailoring density estimation via reproducing kernel moment matching. ICML 2008: 992-999 - [c86]Olivier Chapelle, Chuong B. Do, Quoc V. Le, Alexander J. Smola, Choon Hui Teo:
Tighter Bounds for Structured Estimation. NIPS 2008: 281-288 - [c85]Julian J. McAuley, Tibério S. Caetano, Alexander J. Smola:
Robust Near-Isometric Matching via Structured Learning of Graphical Models. NIPS 2008: 1057-1064 - [c84]Novi Quadrianto, Le Song, Alexander J. Smola:
Kernelized Sorting. NIPS 2008: 1289-1296 - [c83]Xinhua Zhang, Le Song, Arthur Gretton, Alexander J. Smola:
Kernel Measures of Independence for non-iid Data. NIPS 2008: 1937-1944 - [c82]Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola:
Improving Maximum Margin Matrix Factorization. ECML/PKDD (1) 2008: 14 - [c81]Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola:
Adaptive collaborative filtering. RecSys 2008: 275-282 - [i5]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample Problem. CoRR abs/0805.2368 (2008) - [i4]Tibério S. Caetano, Julian J. McAuley, Li Cheng, Quoc V. Le, Alexander J. Smola:
Learning Graph Matching. CoRR abs/0806.2890 (2008) - [i3]Julian J. McAuley, Tibério S. Caetano, Alexander J. Smola:
Robust Near-Isometric Matching via Structured Learning of Graphical Models. CoRR abs/0809.3618 (2008) - 2007
- [j26]S. V. N. Vishwanathan, Alexander J. Smola, René Vidal:
Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. Int. J. Comput. Vis. 73(1): 95-119 (2007) - [j25]Sören Sonnenburg, Mikio L. Braun, Cheng Soon Ong, Samy Bengio, Léon Bottou, Geoffrey Holmes, Yann LeCun, Klaus-Robert Müller, Fernando Pereira, Carl Edward Rasmussen, Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Pascal Vincent, Jason Weston, Robert C. Williamson:
The Need for Open Source Software in Machine Learning. J. Mach. Learn. Res. 8: 2443-2466 (2007) - [c80]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Approach to Comparing Distributions. AAAI 2007: 1637-1641 - [c79]Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. ALT 2007: 13-31 - [c78]Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf:
A Hilbert Space Embedding for Distributions. Discovery Science 2007: 40-41 - [c77]Qinfeng Shi, Yasemin Altun, Alexander J. Smola, S. V. N. Vishwanathan:
Semi-Markov Models for Sequence Segmentation. EMNLP-CoNLL 2007: 640-648 - [c76]Tibério S. Caetano, Li Cheng, Quoc V. Le, Alexander J. Smola:
Learning Graph Matching. ICCV 2007: 1-8 - [c75]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt:
A dependence maximization view of clustering. ICML 2007: 815-822 - [c74]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised feature selection via dependence estimation. ICML 2007: 823-830 - [c73]Le Song, Justin Bedo, Karsten M. Borgwardt, Arthur Gretton, Alexander J. Smola:
Gene selection via the BAHSIC family of algorithms. ISMB/ECCB (Supplement of Bioinformatics) 2007: 490-498 - [c72]Choon Hui Teo, Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
A scalable modular convex solver for regularized risk minimization. KDD 2007: 727-736 - [c71]Alexander J. Smola:
Learning Graph Matching. MLG 2007 - [c70]Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le Song, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Statistical Test of Independence. NIPS 2007: 585-592 - [c69]Alexander J. Smola, S. V. N. Vishwanathan, Quoc V. Le:
Bundle Methods for Machine Learning. NIPS 2007: 1377-1384 - [c68]Le Song, Alexander J. Smola, Karsten M. Borgwardt, Arthur Gretton:
Colored Maximum Variance Unfolding. NIPS 2007: 1385-1392 - [c67]Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alexander J. Smola:
Convex Learning with Invariances. NIPS 2007: 1489-1496 - [c66]Markus Weimer, Alexandros Karatzoglou, Quoc V. Le, Alexander J. Smola:
COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . NIPS 2007: 1593-1600 - [i2]Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo:
Supervised Feature Selection via Dependence Estimation. CoRR abs/0704.2668 (2007) - [i1]Quoc V. Le, Alexander J. Smola:
Direct Optimization of Ranking Measures. CoRR abs/0704.3359 (2007) - 2006
- [j24]Stéphane Canu, Alexander J. Smola:
Kernel methods and the exponential family. Neurocomputing 69(7-9): 714-720 (2006) - [j23]S. V. N. Vishwanathan, Karsten M. Borgwardt, Omri Guttman, Alexander J. Smola:
Kernel extrapolation. Neurocomputing 69(7-9): 721-729 (2006) - [j22]S. V. N. Vishwanathan, Nicol N. Schraudolph, Alexander J. Smola:
Step Size Adaptation in Reproducing Kernel Hilbert Space. J. Mach. Learn. Res. 7: 1107-1133 (2006) - [j21]Ichiro Takeuchi, Quoc V. Le, Tim D. Sears, Alexander J. Smola:
Nonparametric Quantile Estimation. J. Mach. Learn. Res. 7: 1231-1264 (2006) - [j20]Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola:
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. J. Mach. Learn. Res. 7: 1283-1314 (2006) - [c65]Yasemin Altun, Alexander J. Smola:
Unifying Divergence Minimization and Statistical Inference Via Convex Duality. COLT 2006: 139-153 - [c64]Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun:
Transductive Gaussian Process Regression with Automatic Model Selection. ECML 2006: 306-317 - [c63]Quoc V. Le, Alexander J. Smola, Thomas Gärtner:
Simpler knowledge-based support vector machines. ICML 2006: 521-528 - [c62]Julian J. McAuley, Tibério S. Caetano, Alexander J. Smola, Matthias O. Franz:
Learning high-order MRF priors of color images. ICML 2006: 617-624 - [c61]Hao Shen, Knut Hüper, Alexander J. Smola:
Newton-Like Methods for Nonparametric Independent Component Analysis. ICONIP (1) 2006: 1068-1077 - [c60]Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans-Peter Kriegel, Bernhard Schölkopf, Alexander J. Smola:
Integrating structured biological data by Kernel Maximum Mean Discrepancy. ISMB (Supplement of Bioinformatics) 2006: 49-57 - [c59]Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola:
A Kernel Method for the Two-Sample-Problem. NIPS 2006: 513-520 - [c58]Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Bernhard Schölkopf:
Correcting Sample Selection Bias by Unlabeled Data. NIPS 2006: 601-608 - 2005
- [j19]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Learning the Kernel with Hyperkernels. J. Mach. Learn. Res. 6: 1043-1071 (2005) - [j18]Arthur Gretton, Ralf Herbrich, Alexander J. Smola, Olivier Bousquet, Bernhard Schölkopf:
Kernel Methods for Measuring Independence. J. Mach. Learn. Res. 6: 2075-2129 (2005) - [j17]Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola:
Experimentally optimal nu in support vector regression for different noise models and parameter settings. Neural Networks 18(2): 205- (2005) - [j16]Gaëlle Loosli, Stéphane Canu, S. V. N. Vishwanathan, Alexander J. Smola, M. Chattopadhyay:
Boîte à outils SVM simple et rapide. Rev. d'Intelligence Artif. 19(4-5): 741-767 (2005) - [c57]Arthur Gretton, Alexander J. Smola, Olivier Bousquet, Ralf Herbrich, Andrei Belitski, Mark Augath, Yusuke Murayama, Jon Pauls, Bernhard Schölkopf, Nikos K. Logothetis:
Kernel Constrained Covariance for Dependence Measurement. AISTATS 2005: 112-119 - [c56]Alexander J. Smola, S. V. N. Vishwanathan, Thomas Hofmann:
Kernel Methods for Missing Variables. AISTATS 2005: 325-332 - [c55]Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf:
Measuring Statistical Dependence with Hilbert-Schmidt Norms. ALT 2005: 63-77 - [c54]Vladimir Nikulin, Alexander J. Smola:
Parametric model-based clustering. Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security 2005: 190-201 - [c53]Stéphane Canu, Alexander J. Smola:
Kernel methods and the exponential family. ESANN 2005: 447-454 - [c52]Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola:
Joint Regularization. ESANN 2005: 455-460 - [c51]Quoc V. Le, Alexander J. Smola, Stéphane Canu:
Heteroscedastic Gaussian process regression. ICML 2005: 489-496 - [c50]Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alexander J. Smola, Hans-Peter Kriegel:
Protein function prediction via graph kernels. ISMB (Supplement of Bioinformatics) 2005: 47-56 - [c49]Alexandros Karatzoglou, S. V. N. Vishwanathan, Nicol N. Schraudolph, Alexander J. Smola:
Step size-adapted online support vector learning. ISSPA 2005: 823-826 - [c48]Vladimir Nikulin, Alexander J. Smola:
Universal Clustering with Regularization in Probabilistic Space. MLDM 2005: 142-152 - [c47]Thomas Gärtner, Quoc V. Le, Simon Burton, Alexander J. Smola, S. V. N. Vishwanathan:
Large-Scale Multiclass Transduction. NIPS 2005: 411-418 - [c46]Zhenghua Yu, S. V. N. Vishwanathan, Alex Smola:
NICTA at TRECVID 2005 Shot Boundary Detection Task. TRECVID 2005 - 2004
- [j15]Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola:
Experimentally optimal v in support vector regression for different noise models and parameter settings. Neural Networks 17(1): 127-141 (2004) - [j14]Alexander J. Smola, Bernhard Schölkopf:
A tutorial on support vector regression. Stat. Comput. 14(3): 199-222 (2004) - [j13]Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Online learning with kernels. IEEE Trans. Signal Process. 52(8): 2165-2176 (2004) - [c45]Yasemin Altun, Thomas Hofmann, Alexander J. Smola:
Gaussian process classification for segmenting and annotating sequences. ICML 2004 - [c44]Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola:
Learning with non-positive kernels. ICML 2004 - [c43]Chiranjib Bhattacharyya, Pannagadatta K. Shivaswamy, Alexander J. Smola:
A Second Order Cone programming Formulation for Classifying Missing Data. NIPS 2004: 153-160 - [c42]S. V. N. Vishwanathan, Alexander J. Smola:
Binet-Cauchy Kernels. NIPS 2004: 1441-1448 - [c41]Yasemin Altun, Alexander J. Smola, Thomas Hofmann:
Exponential Families for Conditional Random Fields. UAI 2004: 2-9 - 2003
- [j12]Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. IEEE Trans. Pattern Anal. Mach. Intell. 25(5): 623-633 (2003) - [j11]Arnulf B. A. Graf, Alexander J. Smola, Silvio Borer:
Classification in a normalized feature space using support vector machines. IEEE Trans. Neural Networks 14(3): 597-605 (2003) - [c40]Alexander J. Smola, Risi Kondor:
Kernels and Regularization on Graphs. COLT 2003: 144-158 - [c39]Arthur Gretton, Ralf Herbrich, Alexander J. Smola:
The kernel mutual information. ICASSP (4) 2003: 880-884 - [c38]Cheng Soon Ong, Alexander J. Smola:
Machine Learning with Hyperkernels. ICML 2003: 568-575 - [c37]S. V. N. Vishwanathan, Alexander J. Smola, M. Narasimha Murty:
SimpleSVM. ICML 2003: 760-767 - [c36]Alexander J. Smola, Vishy Vishwanathan, Eleazar Eskin:
Laplace Propagation. NIPS 2003: 441-448 - [e1]Shahar Mendelson, Alexander J. Smola:
Advanced Lectures on Machine Learning, Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures. Lecture Notes in Computer Science 2600, Springer 2003, ISBN 3-540-00529-3 [contents] - 2002
- [b2]Bernhard Schölkopf, Alexander Johannes Smola:
Learning with Kernels: support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning series, MIT Press 2002, ISBN 9780262194754, pp. I-XVIII, 1-626 - [j10]Glenn Fung, Olvi L. Mangasarian, Alexander J. Smola:
Minimal Kernel Classifiers. J. Mach. Learn. Res. 3: 303-321 (2002) - [c35]Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Large Margin Classification for Moving Targets. ALT 2002: 113-127 - [c34]Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alexander J. Smola:
Multi-Instance Kernels. ICML 2002: 179-186 - [c33]Bernhard Schölkopf, Alexander J. Smola:
A Short Introduction to Learning with Kernels. Machine Learning Summer School 2002: 41-64 - [c32]Alexander J. Smola, Bernhard Schölkopf:
Bayesian Kernel Methods. Machine Learning Summer School 2002: 65-117 - [c31]Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson:
Hyperkernels. NIPS 2002: 478-485 - [c30]Gunnar Rätsch, Alexander J. Smola, Sebastian Mika:
Adapting Codes and Embeddings for Polychotomies. NIPS 2002: 513-520 - [c29]S. V. N. Vishwanathan, Alexander J. Smola:
Fast Kernels for String and Tree Matching. NIPS 2002: 569-576 - 2001
- [j9]Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson:
Regularized Principal Manifolds. J. Mach. Learn. Res. 1: 179-209 (2001) - [j8]Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson:
Estimating the Support of a High-Dimensional Distribution. Neural Comput. 13(7): 1443-1471 (2001) - [j7]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. IEEE Trans. Inf. Theory 47(6): 2516-2532 (2001) - [c28]Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf:
An improved training algorithm for kernel Fisher discriminants. AISTATS 2001: 209-215 - [c27]Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola:
A Generalized Representer Theorem. COLT/EuroCOLT 2001: 416-426 - [c26]Adam Kowalczyk, Alexander J. Smola, Robert C. Williamson:
Kernel Machines and Boolean Functions. NIPS 2001: 439-446 - [c25]Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson:
Online Learning with Kernels. NIPS 2001: 785-792 - 2000
- [j6]Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett:
New Support Vector Algorithms. Neural Comput. 12(5): 1207-1245 (2000) - [c24]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers of Linear Function Classes. COLT 2000: 309-319 - [c23]Colin Campbell, Nello Cristianini, Alexander J. Smola:
Query Learning with Large Margin Classifiers. ICML 2000: 111-118 - [c22]Alexander J. Smola, Bernhard Schölkopf:
Sparse Greedy Matrix Approximation for Machine Learning. ICML 2000: 911-918 - [c21]Athanassia Chalimourda, Bernhard Schölkopf, Alexander J. Smola:
Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. IJCNN (5) 2000: 199-204 - [c20]Alexander J. Smola, Zoltán L. Óvári, Robert C. Williamson:
Regularization with Dot-Product Kernels. NIPS 2000: 308-314 - [c19]Alexander J. Smola, Peter L. Bartlett:
Sparse Greedy Gaussian Process Regression. NIPS 2000: 619-625 - [c18]Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, Takashi Onoda, Klaus-Robert Müller:
Robust Ensemble Learning for Data Mining. PAKDD 2000: 341-344
1990 – 1999
- 1999
- [j5]Bernhard Schölkopf, Klaus-Robert Müller, Alexander J. Smola:
Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. Inform. Forsch. Entwickl. 14(3): 154-163 (1999) - [j4]Bernhard Schölkopf, Sebastian Mika, Christopher J. C. Burges, Phil Knirsch, Klaus-Robert Müller, Gunnar Rätsch, Alexander J. Smola:
Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks 10(5): 1000-1017 (1999) - [c17]Alexander J. Smola, Robert C. Williamson, Sebastian Mika, Bernhard Schölkopf:
Regularized Principal Manifolds. EuroCOLT 1999: 214-229 - [c16]Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf:
Entropy Numbers, Operators and Support Vector Kernels. EuroCOLT 1999: 285-299 - [c15]Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Christian Lemmen, Alexander J. Smola, Thomas Lengauer, Klaus-Robert Müller:
Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. German Conference on Bioinformatics 1999: 37-43 - [c14]Alexander J. Smola, John Shawe-Taylor, Bernhard Schölkopf, Robert C. Williamson:
The Entropy Regularization Information Criterion. NIPS 1999: 342-348 - [c13]Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Invariant Feature Extraction and Classification in Kernel Spaces. NIPS 1999: 526-532 - [c12]Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Takashi Onoda, Sebastian Mika:
v-Arc: Ensemble Learning in the Presence of Outliers. NIPS 1999: 561-567 - [c11]Bernhard Schölkopf, Robert C. Williamson, Alexander J. Smola, John Shawe-Taylor, John C. Platt:
Support Vector Method for Novelty Detection. NIPS 1999: 582-588 - 1998
- [b1]Alexander J. Smola:
Learning with kernels. Technical University of Berlin, Germany, 1998, ISBN 978-3-88457-349-5, pp. 1-187 - [j3]Alexander J. Smola, Bernhard Schölkopf:
On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. Algorithmica 22(1/2): 211-231 (1998) - [j2]Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Nonlinear Component Analysis as a Kernel Eigenvalue Problem. Neural Comput. 10(5): 1299-1319 (1998) - [j1]Alexander J. Smola, Bernhard Schölkopf, Klaus-Robert Müller:
The connection between regularization operators and support vector kernels. Neural Networks 11(4): 637-649 (1998) - [c10]Bernhard Schölkopf, Alexander J. Smola, Phil Knirsch, Chris Burges:
Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. DAGM-Symposium 1998: 125-132 - [c9]Bernhard Schölkopf, Peter L. Bartlett, Alexander J. Smola, Robert C. Williamson:
Shrinking the Tube: A New Support Vector Regression Algorithm. NIPS 1998: 330-336 - [c8]Sebastian Mika, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller, Matthias Scholz, Gunnar Rätsch:
Kernel PCA and De-Noising in Feature Spaces. NIPS 1998: 536-542 - [c7]Alexander J. Smola, Thilo-Thomas Frieß, Bernhard Schölkopf:
Semiparametric Support Vector and Linear Programming Machines. NIPS 1998: 585-591 - 1997
- [c6]Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller:
Kernel Principal Component Analysis. ICANN 1997: 583-588 - [c5]Klaus-Robert Müller, Alexander J. Smola, Gunnar Rätsch, Bernhard Schölkopf, Jens Kohlmorgen, Vladimir Vapnik:
Predicting Time Series with Support Vector Machines. ICANN 1997: 999-1004 - [c4]Alexander J. Smola, Bernhard Schölkopf:
From Regularization Operators to Support Vector Kernels. NIPS 1997: 343-349 - [c3]Bernhard Schölkopf, Patrice Y. Simard, Alexander J. Smola, Vladimir Vapnik:
Prior Knowledge in Support Vector Kernels. NIPS 1997: 640-646 - 1996
- [c2]Harris Drucker, Christopher J. C. Burges, Linda Kaufman, Alexander J. Smola, Vladimir Vapnik:
Support Vector Regression Machines. NIPS 1996: 155-161 - [c1]Vladimir Vapnik, Steven E. Golowich, Alexander J. Smola:
Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. NIPS 1996: 281-287
Coauthor Index
Quoc V. Le
aka: Quoc Viet Le
aka: Quoc Viet Le
Zachary C. Lipton
aka: Zachary Chase Lipton
aka: Zachary Chase Lipton
Jonas Mueller 0001
aka: Jonas W. Mueller
aka: Jonas W. Mueller
Sashank J. Reddi
aka: Sashank Jakkam Reddi
aka: Sashank Jakkam Reddi
[i57] [c144] [i16] [j39] [c87] [i5] [j25] [c80] [c79] [c78] [c70] [c60] [c59] [c58] [j18] [j17] [c57] [c55] [j15] [j14] [j12] [b2] [c33] [c32] [j9] [j8] [j7] [c28] [c27] [j6] [c24] [c22] [c21] [c18] [j5] [j4] [c17] [c16] [c15] [c14] [c13] [c12] [c11] [j3] [j2] [j1] [c10] [c9] [c8] [c7] [c6] [c5] [c4] [c3]
S. V. N. Vishwanathan
aka: Vishy Vishwanathan
aka: Vishy Vishwanathan
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