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ORCIDAlexander J. Smola
Alexander Johannes 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
- 2020
[c204]Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola:
Meta-Q-Learning. ICLR 2020- [c203]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 - [c202]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 - [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 - [i65]Chenguang Wang, Zihao Ye, Aston Zhang, Zheng Zhang, Alexander J. Smola:
Transformer on a Diet. CoRR abs/2002.06170 (2020) - [i64]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) - [i63]Rasool Fakoor, Pratik Chaudhari, Jonas Mueller, Alexander J. Smola:
TraDE: Transformers for Density Estimation. CoRR abs/2004.02441 (2020) - [i62]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) - [i61]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) - [i60]Hyokun Yun, Michael Froh, Roshan Makhijani, Brian Luc, Alex Smola, Trishul Chilimbi:
Tiering as a Stochastic Submodular Optimization Problem. CoRR abs/2005.07893 (2020) - [i59]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) - [i58]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning. CoRR abs/2006.15199 (2020) - [i57]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) - [i56]Louis Abraham, Gary Bécigneul, Benjamin Coleman, Bernhard Schölkopf, Anshumali Shrivastava, Alexander J. Smola:
Bloom Origami Assays: Practical Group Testing. CoRR abs/2008.02641 (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 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: 275 - [c195]Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola:
P3O: Policy-on Policy-off Policy Optimization. UAI 2019: 371 - [i55]Alexander Ratner, Dan Alistarh, Gustavo Alonso, David G. Andersen, Peter Bailis, Sarah Bird, Nicholas Carlini, Bryan Catanzaro, Eric 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 R. 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 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]Dougal 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 Curto, Irene Zarza, Feng Yang, Alexander J. Smola, Luc Van Gool:
F2F: A Library For Fast Kernel Expansions. CoRR abs/1702.08159 (2017) - [i43]Joachim Curto, 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]Dougal 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]