Smola, Alexander J.

84 publications

JMLR 2023 Flexible Model Aggregation for Quantile Regression Rasool Fakoor, Taesup Kim, Jonas Mueller, Alexander J. Smola, Ryan J. Tibshirani

NeurIPS 2023 Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition Shuhuai Ren, Aston Zhang, Yi Zhu, Shuai Zhang, Shuai Zheng, Mu Li, Alexander J Smola, Xu Sun

NeurIPS 2022 Adaptive Interest for Emphatic Reinforcement Learning Martin Klissarov, Rasool Fakoor, Jonas W Mueller, Kavosh Asadi, Taesup Kim, Alexander J Smola

NeurIPS 2022 Faster Deep Reinforcement Learning with Slower Online Network Kavosh Asadi, Rasool Fakoor, Omer Gottesman, Taesup Kim, Michael L. Littman, Alexander J Smola

NeurIPS 2022 Graph Reordering for Cache-Efficient near Neighbor Search Benjamin Coleman, Santiago Segarra, Alexander J Smola, Anshumali Shrivastava

CVPRW 2022 ResNeSt: Split-Attention Networks 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

NeurIPS 2021 Continuous Doubly Constrained Batch Reinforcement Learning Rasool Fakoor, Jonas W Mueller, Kavosh Asadi, Pratik Chaudhari, Alexander J Smola

NeurIPS 2021 Deep Explicit Duration Switching Models for Time Series Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Turkmen, Harold Soh, Alexander J Smola, Bernie Wang, Tim Januschowski

NeurIPS 2021 Mixture Proportion Estimation and PU Learning:A Modern Approach Saurabh Garg, Yifan Wu, Alexander J Smola, Sivaraman Balakrishnan, Zachary Lipton

AAAI 2021 Symbolic Music Generation with Transformer-GANs Aashiq Muhamed, Liang Li, Xingjian Shi, Suri Yaddanapudi, Wayne Chi, Dylan Jackson, Rahul Suresh, Zachary C. Lipton, Alexander J. Smola

NeurIPS 2020 Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation Rasool Fakoor, Jonas W Mueller, Nick Erickson, Pratik Chaudhari, Alexander J Smola

ICLR 2020 Meta-Q-Learning Rasool Fakoor, Pratik Chaudhari, Stefano Soatto, Alexander J. Smola

UAI 2019 Efficient Multitask Feature and Relationship Learning Han Zhao, Otilia Stretcu, Alexander J. Smola, Geoffrey J. Gordon

ECML-PKDD 2019 FastPoint: Scalable Deep Point Processes Ali Caner Türkmen, Yuyang Wang, Alexander J. Smola

UAI 2019 P3O: Policy-on Policy-Off Policy Optimization Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola

ICMLW 2019 P3O: Policy-on Policy-Off Policy Optimization Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola

AISTATS 2018 A Generic Approach for Escaping Saddle Points Sashank J. Reddi, Manzil Zaheer, Suvrit Sra, Barnabás Póczos, Francis R. Bach, Ruslan Salakhutdinov, Alexander J. Smola

AAAI 2018 Variational Reasoning for Question Answering with Knowledge Graph Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song

AISTATS 2017 Attributing Hacks Ziqi Liu, Alexander J. Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng

AISTATS 2017 Data Driven Resource Allocation for Distributed Learning Travis Dick, Mu Li, Venkata Krishna Pillutla, Colin White, Nina Balcan, Alexander J. Smola

NeurIPS 2017 Deep Sets Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan Salakhutdinov, Alexander J Smola

ICLR 2017 Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton

ICLR 2017 Joint Training of Ratings and Reviews with Recurrent Recommender Networks Chao-Yuan Wu, Amr Ahmed, Alex Beutel, Alexander J. Smola

ICML 2017 Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data Manzil Zaheer, Amr Ahmed, Alexander J. Smola

ICCV 2017 Sampling Matters in Deep Embedding Learning Chao-Yuan Wu, R. Manmatha, Alexander J. Smola, Philipp Krahenbuhl

AISTATS 2016 AdaDelay: Delay Adaptive Distributed Stochastic Optimization Suvrit Sra, Adams Wei Yu, Mu Li, Alexander J. Smola

AISTATS 2016 Exponential Stochastic Cellular Automata for Massively Parallel Inference Manzil Zaheer, Michael L. Wick, Jean-Baptiste Tristan, Alexander J. Smola, Guy L. Steele Jr.

NeurIPS 2016 Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization Sashank J. Reddi, Suvrit Sra, Barnabas Poczos, Alexander J Smola

JMLR 2016 Trend Filtering on Graphs Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani

NeurIPS 2016 Variance Reduction in Stochastic Gradient Langevin Dynamics Kumar Avinava Dubey, Sashank J. Reddi, Sinead A Williamson, Barnabas Poczos, Alexander J Smola, Eric P Xing

AISTATS 2015 A La Carte - Learning Fast Kernels Zichao Yang, Andrew Gordon Wilson, Alexander J. Smola, Le Song

UAI 2015 Communication Efficient Coresets for Empirical Loss Minimization Sashank J. Reddi, Barnabás Póczos, Alexander J. Smola

AAAI 2015 Doubly Robust Covariate Shift Correction Sashank Jakkam Reddi, Barnabás Póczos, Alexander J. Smola

NeurIPS 2015 Fast and Guaranteed Tensor Decomposition via Sketching Yining Wang, Hsiao-Yu Tung, Alexander J Smola, Anima Anandkumar

NeurIPS 2015 On Variance Reduction in Stochastic Gradient Descent and Its Asynchronous Variants Sashank J. Reddi, Ahmed Hefny, Suvrit Sra, Barnabas Poczos, Alexander J Smola

AISTATS 2015 Preferential Attachment in Graphs with Affinities Jay Lee, Manzil Zaheer, Stephan Günnemann, Alexander J. Smola

AISTATS 2015 Trend Filtering on Graphs Yu-Xiang Wang, James Sharpnack, Alexander J. Smola, Ryan J. Tibshirani

NeurIPS 2014 Communication Efficient Distributed Machine Learning with the Parameter Server Mu Li, David G Andersen, Alexander J Smola, Kai Yu

NeurIPS 2014 Spectral Methods for Indian Buffet Process Inference Hsiao-Yu Tung, Alexander J Smola

NeurIPS 2013 Variance Reduction for Stochastic Gradient Optimization Chong Wang, Xi Chen, Alexander J Smola, Eric P Xing

ICML 2012 Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations Nando de Freitas, Alexander J. Smola, Masrour Zoghi

UAI 2012 Hokusai - Sketching Streams in Real Time Sergiy Matusevych, Alexander J. Smola, Amr Ahmed

MLJ 2011 Guest Editorial: Model Selection and Optimization in Machine Learning Süreyya Özögür-Akyüz, Devrim Ünay, Alexander J. Smola

ICML 2010 Hilbert Space Embeddings of Hidden Markov Models Le Song, Byron Boots, Sajid M. Siddiqi, Geoffrey J. Gordon, Alexander J. Smola

UAI 2010 Super-Samples from Kernel Herding Yutian Chen, Max Welling, Alexander J. Smola

ICML 2009 Feature Hashing for Large Scale Multitask Learning Kilian Q. Weinberger, Anirban Dasgupta, John Langford, Alexander J. Smola, Josh Attenberg

ICML 2009 Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems Le Song, Jonathan Huang, Alexander J. Smola, Kenji Fukumizu

CVPR 2008 Discriminative Human Action Segmentation and Recognition Using Semi-Markov Model Qinfeng Shi, Li Wang, Li Cheng, Alexander J. Smola

ICML 2008 Estimating Labels from Label Proportions Novi Quadrianto, Alexander J. Smola, Tibério S. Caetano, Quoc V. Le

ECML-PKDD 2008 Improving Maximum Margin Matrix Factorization Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola

MLJ 2008 Improving Maximum Margin Matrix Factorization Markus Weimer, Alexandros Karatzoglou, Alexander J. Smola

ICML 2008 Tailoring Density Estimation via Reproducing Kernel Moment Matching Le Song, Xinhua Zhang, Alexander J. Smola, Arthur Gretton, Bernhard Schölkopf

ICML 2007 A Dependence Maximization View of Clustering Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt

ALT 2007 A Hilbert Space Embedding for Distributions Alexander J. Smola, Arthur Gretton, Le Song, Bernhard Schölkopf

AAAI 2007 A Kernel Approach to Comparing Distributions Arthur Gretton, Karsten M. Borgwardt, Malte J. Rasch, Bernhard Schölkopf, Alexander J. Smola

ICCV 2007 Learning Graph Matching Tibério S. Caetano, Li Cheng, Quoc V. Le, Alexander J. Smola

ICML 2007 Supervised Feature Selection via Dependence Estimation Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo

ICML 2006 Learning High-Order MRF Priors of Color Images Julian J. McAuley, Tibério S. Caetano, Alexander J. Smola, Matthias O. Franz

JMLR 2006 Nonparametric Quantile Estimation Ichiro Takeuchi, Quoc V. Le, Timothy D. Sears, Alexander J. Smola

JMLR 2006 Second Order Cone Programming Approaches for Handling Missing and Uncertain Data Pannagadatta K. Shivaswamy, Chiranjib Bhattacharyya, Alexander J. Smola

ICML 2006 Simpler Knowledge-Based Support Vector Machines Quoc V. Le, Alexander J. Smola, Thomas Gärtner

ECML-PKDD 2006 Transductive Gaussian Process Regression with Automatic Model Selection Quoc V. Le, Alexander J. Smola, Thomas Gärtner, Yasemin Altun

COLT 2006 Unifying Divergence Minimization and Statistical Inference via Convex Duality Yasemin Altun, Alexander J. Smola

ICML 2005 Heteroscedastic Gaussian Process Regression Quoc V. Le, Alexander J. Smola, Stéphane Canu

JMLR 2005 Learning the Kernel with Hyperkernels Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson

ALT 2005 Measuring Statistical Dependence with Hilbert-Schmidt Norms Arthur Gretton, Olivier Bousquet, Alexander J. Smola, Bernhard Schölkopf

UAI 2004 Exponential Families for Conditional Random Fields Yasemin Altun, Alexander J. Smola, Thomas Hofmann

ICML 2004 Gaussian Process Classification for Segmenting and Annotating Sequences Yasemin Altun, Thomas Hofmann, Alexander J. Smola

ICML 2004 Learning with Non-Positive Kernels Cheng Soon Ong, Xavier Mary, Stéphane Canu, Alexander J. Smola

COLT 2003 Kernels and Regularization on Graphs Alexander J. Smola, Risi Kondor

ICML 2003 Machine Learning with Hyperkernels Cheng Soon Ong, Alexander J. Smola

ICML 2003 SimpleSVM S. V. N. Vishwanathan, Alexander J. Smola, M. Narasimha Murty

ALT 2002 Large Margin Classification for Moving Targets Jyrki Kivinen, Alexander J. Smola, Robert C. Williamson

JMLR 2002 Minimal Kernel Classifiers (Kernel Machines Section) Glenn M. Fung, Olvi L. Mangasarian, Alexander J. Smola

ICML 2002 Multi-Instance Kernels Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alexander J. Smola

COLT 2001 A Generalized Representer Theorem Bernhard Schölkopf, Ralf Herbrich, Alexander J. Smola

AISTATS 2001 An Improved Training Algorithm for Kernel Fisher Discriminants Sebastian Mika, Alexander J. Smola, Bernhard Schölkopf

NeCo 2001 Estimating the Support of a High-Dimensional Distribution Bernhard Schölkopf, John C. Platt, John Shawe-Taylor, Alexander J. Smola, Robert C. Williamson

JMLR 2001 Regularized Principal Manifolds (Kernel Machines Section) Alexander J. Smola, Sebastian Mika, Bernhard Schölkopf, Robert C. Williamson

COLT 2000 Entropy Numbers of Linear Function Classes Robert C. Williamson, Alexander J. Smola, Bernhard Schölkopf

NeCo 2000 New Support Vector Algorithms Bernhard Schölkopf, Alexander J. Smola, Robert C. Williamson, Peter L. Bartlett

ICML 2000 Query Learning with Large Margin Classifiers Colin Campbell, Nello Cristianini, Alexander J. Smola

ICML 2000 Sparse Greedy Matrix Approximation for Machine Learning Alexander J. Smola, Bernhard Schölkopf

NeCo 1998 Nonlinear Component Analysis as a Kernel Eigenvalue Problem Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller