Support Vector Regression Machines. - Harris Drucker; Christopher J. C. Burges; Linda Kaufman; Alexander J. Smola; Vladimir Vapnik (NIPS, 1996)

Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. - Vladimir Vapnik; Steven E. Golowich; Alexander J. Smola (NIPS, 1996)

Predicting Time Series with Support Vector Machines. - Klaus-Robert Müller; Alexander J. Smola; Gunnar Rätsch; Bernhard Schölkopf; Jens Kohlmorgen; Vladimir Vapnik (ICANN, 1997)

Kernel Principal Component Analysis. - Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller (ICANN, 1997)

Prior Knowledge in Support Vector Kernels. - Bernhard Schölkopf; Patrice Y. Simard; Alexander J. Smola; Vladimir Vapnik (NIPS, 1997)

From Regularization Operators to Support Vector Kernels. - Alexander J. Smola; Bernhard Schölkopf (NIPS, 1997)

Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces. - Bernhard Schölkopf; Alexander J. Smola; Phil Knirsch; Chris Burges (DAGM-Symposium, 1998)

Kernel PCA and De-Noising in Feature Spaces. - Sebastian Mika; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller; Matthias Scholz; Gunnar Rätsch (NIPS, 1998)

Shrinking the Tube: A New Support Vector Regression Algorithm. - Bernhard Schölkopf; Peter L. Bartlett; Alexander J. Smola; Robert C. Williamson (NIPS, 1998)

Semiparametric Support Vector and Linear Programming Machines. - Alexander J. Smola; Thilo-Thomas Frieß; Bernhard Schölkopf (NIPS, 1998)

On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion. - Alexander J. Smola; Bernhard Schölkopf (Algorithmica, 1998)

Nonlinear Component Analysis as a Kernel Eigenvalue Problem. - Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller (Neural Comput., 1998)

The connection between regularization operators and support vector kernels. - Alexander J. Smola; Bernhard Schölkopf; Klaus-Robert Müller (Neural Networks, 1998)

Learning with kernels. - Alexander J. Smola (1998)

Entropy Numbers, Operators and Support Vector Kernels. - Robert C. Williamson; Alexander J. Smola; Bernhard Schölkopf (EuroCOLT, 1999)

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites. - Alexander Zien; Gunnar Rätsch; Sebastian Mika; Bernhard Schölkopf; Christian Lemmen; Alexander J. Smola; Thomas Lengauer; Klaus-Robert Müller (German Conference on Bioinformatics, 1999)

Invariant Feature Extraction and Classification in Kernel Spaces. - Sebastian Mika; Gunnar Rätsch; Jason Weston; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller (NIPS, 1999)

v-Arc: Ensemble Learning in the Presence of Outliers. - Gunnar Rätsch; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller; Takashi Onoda; Sebastian Mika (NIPS, 1999)

Support Vector Method for Novelty Detection. - Bernhard Schölkopf; Robert C. Williamson; Alexander J. Smola; John Shawe-Taylor; John C. Platt (NIPS, 1999)

The Entropy Regularization Information Criterion. - Alexander J. Smola; John Shawe-Taylor; Bernhard Schölkopf; Robert C. Williamson (NIPS, 1999)

Lernen mit Kernen: Support-Vektor-Methoden zur Analyse hochdimensionaler Daten. - Bernhard Schölkopf; Klaus-Robert Müller; Alexander J. Smola (Inform. Forsch. Entwickl., 1999)

Input space versus feature space in kernel-based methods. - Bernhard Schölkopf; Sebastian Mika; Christopher J. C. Burges; Phil Knirsch; Klaus-Robert Müller; Gunnar Rätsch; Alexander J. Smola (IEEE Trans. Neural Networks, 1999)

Entropy Numbers of Linear Function Classes. - Robert C. Williamson; Alexander J. Smola; Bernhard Schölkopf (COLT, 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 (ICML, 2000)

Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments. - Athanassia Chalimourda; Bernhard Schölkopf; Alexander J. Smola (IJCNN (5), 2000)

Sparse Greedy Gaussian Process Regression. - Alexander J. Smola; Peter L. Bartlett (NIPS, 2000)

Regularization with Dot-Product Kernels. - Alexander J. Smola; Zoltán L. Óvári; Robert C. Williamson (NIPS, 2000)

Robust Ensemble Learning for Data Mining. - Gunnar Rätsch; Bernhard Schölkopf; Alexander J. Smola; Sebastian Mika; Takashi Onoda; Klaus-Robert Müller (PAKDD, 2000)

New Support Vector Algorithms. - Bernhard Schölkopf; Alexander J. Smola; Robert C. Williamson; Peter L. Bartlett (Neural Comput., 2000)

An improved training algorithm for kernel Fisher discriminants. - Sebastian Mika; Alexander J. Smola; Bernhard Schölkopf (AISTATS, 2001)

A Generalized Representer Theorem. - Bernhard Schölkopf; Ralf Herbrich; Alexander J. Smola (COLT/EuroCOLT, 2001)

Kernel Machines and Boolean Functions. - Adam Kowalczyk; Alexander J. Smola; Robert C. Williamson (NIPS, 2001)

Regularized Principal Manifolds. - Alexander J. Smola; Sebastian Mika; Bernhard Schölkopf; Robert C. Williamson (J. Mach. Learn. Res., 2001)

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

Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators. - Robert C. Williamson; Alexander J. Smola; Bernhard Schölkopf (IEEE Trans. Inf. Theory, 2001)

Learning with Kernels: support vector machines, regularization, optimization, and beyond. - Bernhard Schölkopf; Alexander Johannes Smola (MIT Press, 2002)

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

Multi-Instance Kernels. - Thomas Gärtner 001; Peter A. Flach; Adam Kowalczyk; Alexander J. Smola (ICML, 2002)

A Short Introduction to Learning with Kernels. - Bernhard Schölkopf; Alexander J. Smola (Machine Learning Summer School, 2002)

Bayesian Kernel Methods. - Alexander J. Smola; Bernhard Schölkopf (Machine Learning Summer School, 2002)

Hyperkernels. - Cheng Soon Ong; Alexander J. Smola; Robert C. Williamson (NIPS, 2002)

Adapting Codes and Embeddings for Polychotomies. - Gunnar Rätsch; Alexander J. Smola; Sebastian Mika (NIPS, 2002)

Fast Kernels for String and Tree Matching. - S. V. N. Vishwanathan; Alexander J. Smola (NIPS, 2002)

Minimal Kernel Classifiers. - Glenn Fung; Olvi L. Mangasarian; Alexander J. Smola (J. Mach. Learn. Res., 2002)

Kernels and Regularization on Graphs. - Alexander J. Smola; Risi Kondor (COLT, 2003)

The kernel mutual information. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola (ICASSP (4), 2003)

Machine Learning with Hyperkernels. - Cheng Soon Ong; Alexander J. Smola (ICML, 2003)

SimpleSVM. - S. V. N. Vishwanathan; Alexander J. Smola; M. Narasimha Murty (ICML, 2003)

Laplace Propagation. - Alexander J. Smola; Vishy Vishwanathan; Eleazar Eskin (NIPS, 2003)

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces. - Sebastian Mika; Gunnar Rätsch; Jason Weston; Bernhard Schölkopf; Alexander J. Smola; Klaus-Robert Müller (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Classification in a normalized feature space using support vector machines. - Arnulf B. A. Graf; Alexander J. Smola; Silvio Borer (IEEE Trans. Neural Networks, 2003)

Gaussian process classification for segmenting and annotating sequences. - Yasemin Altun; Thomas Hofmann 001; Alexander J. Smola (ICML, 2004)

Learning with non-positive kernels. - Cheng Soon Ong; Xavier Mary; Stéphane Canu; Alexander J. Smola (ICML, 2004)

A Second Order Cone programming Formulation for Classifying Missing Data. - Chiranjib Bhattacharyya; Pannagadatta K. Shivaswamy; Alexander J. Smola (NIPS, 2004)

Binet-Cauchy Kernels. - S. V. N. Vishwanathan; Alexander J. Smola (NIPS, 2004)

Exponential Families for Conditional Random Fields. - Yasemin Altun; Alexander J. Smola; Thomas Hofmann 001 (UAI, 2004)

Experimentally optimal v in support vector regression for different noise models and parameter settings. - Athanassia Chalimourda; Bernhard Schölkopf; Alexander J. Smola (Neural Networks, 2004)

A tutorial on support vector regression. - Alexander J. Smola; Bernhard Schölkopf (Stat. Comput., 2004)

Online learning with kernels. - Jyrki Kivinen; Alexander J. Smola; Robert C. Williamson (IEEE Trans. Signal Process., 2004)

Kernel Constrained Covariance for Dependence Measurement. - Arthur Gretton; Alexander J. Smola; Olivier Bousquet; Ralf Herbrich; Andrei Belitski; Mark Augath; Yusuke Murayama; Jon Pauls; Bernhard Schölkopf; Nikos K. Logothetis (AISTATS, 2005)

Kernel Methods for Missing Variables. - Alexander J. Smola; S. V. N. Vishwanathan; Thomas Hofmann 001 (AISTATS, 2005)

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

Parametric model-based clustering. - Vladimir Nikulin; Alexander J. Smola (Data Mining, Intrusion Detection, Information Assurance, and Data Networks Security, 2005)

Joint Regularization. - Karsten M. Borgwardt; Omri Guttman; S. V. N. Vishwanathan; Alexander J. Smola (ESANN, 2005)

Heteroscedastic Gaussian process regression. - Quoc V. Le; Alexander J. Smola; Stéphane Canu (ICML, 2005)

Protein function prediction via graph kernels. - Karsten M. Borgwardt; Cheng Soon Ong; Stefan Schönauer; S. V. N. Vishwanathan; Alexander J. Smola; Hans-Peter Kriegel (ISMB (Supplement of Bioinformatics), 2005)

Step size-adapted online support vector learning. - Alexandros Karatzoglou; S. V. N. Vishwanathan; Nicol N. Schraudolph; Alexander J. Smola (ISSPA, 2005)

Universal Clustering with Regularization in Probabilistic Space. - Vladimir Nikulin; Alexander J. Smola (MLDM, 2005)

Large-Scale Multiclass Transduction. - Thomas Gärtner 001; Quoc V. Le; Simon Burton 003; Alexander J. Smola; S. V. N. Vishwanathan (NIPS, 2005)

NICTA at TRECVID 2005 Shot Boundary Detection Task. - Zhenghua Yu; S. V. N. Vishwanathan; Alex Smola (TRECVID, 2005)

Kernel Methods for Measuring Independence. - Arthur Gretton; Ralf Herbrich; Alexander J. Smola; Olivier Bousquet; Bernhard Schölkopf (J. Mach. Learn. Res., 2005)

Learning the Kernel with Hyperkernels. - Cheng Soon Ong; Alexander J. Smola; Robert C. Williamson (J. Mach. Learn. Res., 2005)

Experimentally optimal nu in support vector regression for different noise models and parameter settings. - Athanassia Chalimourda; Bernhard Schölkopf; Alexander J. Smola (Neural Networks, 2005)

Boîte à outils SVM simple et rapide. - Gaëlle Loosli; Stéphane Canu; S. V. N. Vishwanathan; Alexander J. Smola; M. Chattopadhyay (Rev. d'Intelligence Artif., 2005)

Unifying Divergence Minimization and Statistical Inference Via Convex Duality. - Yasemin Altun; Alexander J. Smola (COLT, 2006)

Transductive Gaussian Process Regression with Automatic Model Selection. - Quoc V. Le; Alexander J. Smola; Thomas Gärtner 001; Yasemin Altun (ECML, 2006)

Simpler knowledge-based support vector machines. - Quoc V. Le; Alexander J. Smola; Thomas Gärtner 001 (ICML, 2006)

Learning high-order MRF priors of color images. - Julian J. McAuley; Tibério S. Caetano; Alexander J. Smola; Matthias O. Franz (ICML, 2006)

Newton-Like Methods for Nonparametric Independent Component Analysis. - Hao Shen 002; Knut Hüper; Alexander J. Smola (ICONIP (1), 2006)

Integrating structured biological data by Kernel Maximum Mean Discrepancy. - Karsten M. Borgwardt; Arthur Gretton; Malte J. Rasch; Hans-Peter Kriegel; Bernhard Schölkopf; Alexander J. Smola (ISMB (Supplement of Bioinformatics), 2006)

A Kernel Method for the Two-Sample-Problem. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2006)

Correcting Sample Selection Bias by Unlabeled Data. - Jiayuan Huang; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt; Bernhard Schölkopf (NIPS, 2006)

Kernel methods and the exponential family. - Stéphane Canu; Alexander J. Smola (Neurocomputing, 2006)

Kernel extrapolation. - S. V. N. Vishwanathan; Karsten M. Borgwardt; Omri Guttman; Alexander J. Smola (Neurocomputing, 2006)

Second Order Cone Programming Approaches for Handling Missing and Uncertain Data. - Pannagadatta K. Shivaswamy; Chiranjib Bhattacharyya; Alexander J. Smola (J. Mach. Learn. Res., 2006)

Nonparametric Quantile Estimation. - Ichiro Takeuchi; Quoc V. Le; Tim D. Sears; Alexander J. Smola (J. Mach. Learn. Res., 2006)

Step Size Adaptation in Reproducing Kernel Hilbert Space. - S. V. N. Vishwanathan; Nicol N. Schraudolph; Alexander J. Smola (J. Mach. Learn. Res., 2006)

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

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

Semi-Markov Models for Sequence Segmentation. - Qinfeng Shi; Yasemin Altun; Alexander J. Smola; S. V. N. Vishwanathan (EMNLP-CoNLL, 2007)

Learning Graph Matching. - Tibério S. Caetano; Li Cheng 001; Quoc V. Le; Alexander J. Smola (ICCV, 2007)

A dependence maximization view of clustering. - Le Song; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt (ICML, 2007)

Supervised feature selection via dependence estimation. - Le Song; Alexander J. Smola; Arthur Gretton; Karsten M. Borgwardt; Justin Bedo (ICML, 2007)

Gene selection via the BAHSIC family of algorithms. - Le Song; Justin Bedo; Karsten M. Borgwardt; Arthur Gretton; Alexander J. Smola (ISMB/ECCB (Supplement of Bioinformatics), 2007)

A scalable modular convex solver for regularized risk minimization. - Choon Hui Teo; Alexander J. Smola; S. V. N. Vishwanathan; Quoc V. Le (KDD, 2007)

Learning Graph Matching. - Alexander J. Smola (MLG, 2007)

A Kernel Statistical Test of Independence. - Arthur Gretton; Kenji Fukumizu; Choon Hui Teo; Le Song; Bernhard Schölkopf; Alexander J. Smola (NIPS, 2007)

Bundle Methods for Machine Learning. - Alexander J. Smola; S. V. N. Vishwanathan; Quoc V. Le (NIPS, 2007)

Colored Maximum Variance Unfolding. - Le Song; Alexander J. Smola; Karsten M. Borgwardt; Arthur Gretton (NIPS, 2007)

Convex Learning with Invariances. - Choon Hui Teo; Amir Globerson; Sam T. Roweis; Alexander J. Smola (NIPS, 2007)

COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking . - Markus Weimer; Alexandros Karatzoglou; Quoc V. Le; Alexander J. Smola (NIPS, 2007)

Direct Optimization of Ranking Measures - Quoc V. Le; Alexander J. Smola (CoRR, 2007)

Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes. - S. V. N. Vishwanathan; Alexander J. Smola; René Vidal (Int. J. Comput. Vis., 2007)

The Need for Open Source Software in Machine Learning. - Sören Sonnenburg; Mikio L. Braun; Cheng Soon Ong; Samy Bengio; Léon Bottou; Geoffrey Holmes 001; Yann LeCun; Klaus-Robert Müller; Fernando Pereira 003; Carl Edward Rasmussen; Gunnar Rätsch; Bernhard Schölkopf; et al. (J. Mach. Learn. Res., 2007)

Discriminative human action segmentation and recognition using semi-Markov model. - Qinfeng Shi; Li Wang 033; Li Cheng 001; Alexander J. Smola (CVPR, 2008)

Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning. - Ahmed H. El Zein; Eric McCreath; Alistair P. Rendell; Alexander J. Smola (ICCS (1), 2008)

Tailoring density estimation via reproducing kernel moment matching. - Le Song; Xinhua Zhang; Alexander J. Smola; Arthur Gretton; Bernhard Schölkopf (ICML, 2008)

Combining near-optimal feature selection with gSpan. - Karsten M. Borgwardt; Xifeng Yan; Marisa Thoma; Hong Cheng 001; Arthur Gretton; Le Song; Alex Smola; Jiawei Han 001; Philip S. Yu; Hans-Peter Kriegel (MLG@ICML, 2008)

Tighter Bounds for Structured Estimation. - Olivier Chapelle; Chuong B. Do; Quoc V. Le; Alexander J. Smola; Choon Hui Teo (NIPS, 2008)

Robust Near-Isometric Matching via Structured Learning of Graphical Models. - Julian J. McAuley; Tibério S. Caetano; Alexander J. Smola (NIPS, 2008)

Kernelized Sorting. - Novi Quadrianto; Le Song; Alexander J. Smola (NIPS, 2008)

Kernel Measures of Independence for non-iid Data. - Xinhua Zhang; Le Song; Arthur Gretton; Alexander J. Smola (NIPS, 2008)

Adaptive collaborative filtering. - Markus Weimer; Alexandros Karatzoglou; Alexander J. Smola (RecSys, 2008)

Improving maximum margin matrix factorization. - Markus Weimer; Alexandros Karatzoglou; Alexander J. Smola (Mach. Learn., 2008)

Hilbert space embeddings of conditional distributions with applications to dynamical systems. - Le Song; Jonathan Huang; Alexander J. Smola; Kenji Fukumizu (ICML, 2009)

Feature hashing for large scale multitask learning. - Kilian Q. Weinberger; Anirban Dasgupta 001; John Langford 001; Alexander J. Smola; Josh Attenberg (ICML, 2009)

Distribution Matching for Transduction. - Novi Quadrianto; James Petterson; Alexander J. Smola (NIPS, 2009)

Slow Learners are Fast. - Martin Zinkevich; Alexander J. Smola; John Langford 001 (NIPS, 2009)

Near-optimal Supervised Feature Selection among Frequent Subgraphs. - Marisa Thoma; Hong Cheng 001; Arthur Gretton; Jiawei Han 001; Hans-Peter Kriegel; Alexander J. Smola; Le Song; Philip S. Yu; Xifeng Yan; Karsten M. Borgwardt (SDM, 2009)

Relative Novelty Detection. - Alexander J. Smola; Le Song; Choon Hui Teo (AISTATS, 2009)

Estimating Labels from Label Proportions. - Novi Quadrianto; Alexander J. Smola; Tibério S. Caetano; Quoc V. Le (J. Mach. Learn. Res., 2009)

Hash Kernels. - Qinfeng Shi; James Petterson; Gideon Dror; John Langford 001; Alexander J. Smola; Alexander L. Strehl; Vishy Vishwanathan (AISTATS, 2009)

Hash Kernels for Structured Data. - Qinfeng Shi; James Petterson; Gideon Dror; John Langford 001; Alexander J. Smola; S. V. N. Vishwanathan (J. Mach. Learn. Res., 2009)

Learning Graph Matching. - Tibério S. Caetano; Julian J. McAuley; Li Cheng 001; Quoc V. Le; Alexander J. Smola (IEEE Trans. Pattern Anal. Mach. Intell., 2009)

Distributed Flow Algorithms for Scalable Similarity Visualization. - Novi Quadrianto; Dale Schuurmans; Alexander J. Smola (ICDM Workshops, 2010)

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

Optimal Web-Scale Tiering as a Flow Problem. - Gilbert Leung; Novi Quadrianto; Alexander J. Smola; Kostas Tsioutsiouliklis (NIPS, 2010)

Word Features for Latent Dirichlet Allocation. - James Petterson; Alexander J. Smola; Tibério S. Caetano; Wray L. Buntine; Shravan M. Narayanamurthy (NIPS, 2010)

Multitask Learning without Label Correspondences. - Novi Quadrianto; Alexander J. Smola; Tibério S. Caetano; S. V. N. Vishwanathan; James Petterson (NIPS, 2010)

Parallelized Stochastic Gradient Descent. - Martin Zinkevich; Markus Weimer; Alexander J. Smola; Lihong Li 001 (NIPS, 2010)

Super-Samples from Kernel Herding. - Yutian Chen 001; Max Welling; Alexander J. Smola (UAI, 2010)

IntervalRank: isotonic regression with listwise and pairwise constraints. - Taesup Moon; Alexander J. Smola; Yi Chang 001; Zhaohui Zheng 001 (WSDM, 2010)

Collaborative Filtering on a Budget. - Alexandros Karatzoglou; Alexander J. Smola; Markus Weimer (AISTATS, 2010)

Bundle Methods for Regularized Risk Minimization. - Choon Hui Teo; S. V. N. Vishwanathan; Alexander J. Smola; Quoc V. Le (J. Mach. Learn. Res., 2010)

Kernelized Sorting. - Novi Quadrianto; Alexander J. Smola; Le Song; Tinne Tuytelaars (IEEE Trans. Pattern Anal. Mach. Intell., 2010)

Wearable sensor activity analysis using semi-Markov models with a grammar. - Owen Thomas; Peter Sunehag; Gideon Dror; Sungrack Yun; Sungwoong Kim; Matthew W. Robards; Alexander J. Smola; Daniel Green; Philo Saunders (Pervasive Mob. Comput., 2010)

An Architecture for Parallel Topic Models. - Alexander J. Smola; Shravan M. Narayanamurthy (Proc. VLDB Endow., 2010)

Discriminative frequent subgraph mining with optimality guarantees. - Marisa Thoma; Hong Cheng 001; Arthur Gretton; Jiawei Han 001; Hans-Peter Kriegel; Alexander J. Smola; Le Song; Philip S. Yu; Xifeng Yan; Karsten M. Borgwardt (Stat. Anal. Data Min., 2010)

Scalable distributed inference of dynamic user interests for behavioral targeting. - Amr Ahmed 001; Yucheng Low; Mohamed Aly 002; Vanja Josifovski; Alexander J. Smola (KDD, 2011)

Multiple domain user personalization. - Yucheng Low; Deepak Agarwal; Alexander J. Smola (KDD, 2011)

Collaborative competitive filtering: learning recommender using context of user choice. - Shuang-Hong Yang; Bo Long; Alexander J. Smola; Hongyuan Zha; Zhaohui Zheng 001 (SIGIR, 2011)

Bid generation for advanced match in sponsored search. - Andrei Z. Broder; Evgeniy Gabrilovich; Vanja Josifovski; George Mavromatis; Alexander J. Smola (WSDM, 2011)

Scalable clustering of news search results. - Srinivas Vadrevu; Choon Hui Teo; Suju Rajan; Kunal Punera; Byron Dom; Alexander J. Smola; Yi Chang 001; Zhaohui Zheng 001 (WSDM, 2011)

Unified analysis of streaming news. - Amr Ahmed 001; Qirong Ho; Jacob Eisenstein; Eric P. Xing; Alexander J. Smola; Choon Hui Teo (WWW, 2011)

WWW 2011 invited tutorial overview: latent variable models on the internet. - Amr Ahmed 001; Alexander J. Smola (WWW (Companion Volume), 2011)

Like like alike: joint friendship and interest propagation in social networks. - Shuang-Hong Yang; Bo Long; Alexander J. Smola; Narayanan Sadagopan; Zhaohui Zheng 001; Hongyuan Zha (WWW, 2011)

Parallel Online Learning - Daniel J. Hsu; Nikos Karampatziakis; John Langford 001; Alexander J. Smola (CoRR, 2011)

Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models. - Qinfeng Shi; Li Cheng 001; Li Wang 033; Alexander J. Smola (Int. J. Comput. Vis., 2011)

Linear-Time Estimators for Propensity Scores. - Deepak Agarwal; Lihong Li 001; Alexander J. Smola (AISTATS, 2011)

Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text. - Amr Ahmed 001; Qirong Ho; Choon Hui Teo; Jacob Eisenstein; Alexander J. Smola; Eric P. Xing (AISTATS, 2011)

Guest editorial: model selection and optimization in machine learning. - Süreyya Özögür-Akyüz; Devrim Ünay; Alexander J. Smola (Mach. Learn., 2011)

Web-scale multi-task feature selection for behavioral targeting. - Amr Ahmed 001; Mohamed Aly 002; Abhimanyu Das; Alexander J. Smola; Tasos Anastasakos (CIKM, 2012)

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

Linear support vector machines via dual cached loops. - Shin Matsushima; S. V. N. Vishwanathan; Alexander J. Smola (KDD, 2012)

FastEx: Hash Clustering with Exponential Families. - Amr Ahmed 001; Sujith Ravi; Shravan M. Narayanamurthy; Alexander J. Smola (NIPS, 2012)

Learning Networks of Heterogeneous Influence. - Nan Du 002; Le Song; Alexander J. Smola; Ming Yuan 001 (NIPS, 2012)

Friend or frenemy?: predicting signed ties in social networks. - Shuang-Hong Yang; Alexander J. Smola; Bo Long; Hongyuan Zha; Yi Chang 001 (SIGIR, 2012)

Hokusai - Sketching Streams in Real Time. - Sergiy Matusevych; Alexander J. Smola; Amr Ahmed 001 (UAI, 2012)

Scalable inference in latent variable models. - Amr Ahmed 001; Mohamed Aly 002; Joseph Gonzalez 001; Shravan M. Narayanamurthy; Alexander J. Smola (WSDM, 2012)

Fair and balanced: learning to present news stories. - Amr Ahmed 001; Choon Hui Teo; S. V. N. Vishwanathan; Alexander J. Smola (WSDM, 2012)

Discovering geographical topics in the twitter stream. - Liangjie Hong; Amr Ahmed 001; Siva Gurumurthy; Alexander J. Smola; Kostas Tsioutsiouliklis (WWW, 2012)

Regret Bounds for Deterministic Gaussian Process Bandits - Nando de Freitas; Alexander J. Smola; Masrour Zoghi (CoRR, 2012)

A Kernel Two-Sample Test. - Arthur Gretton; Karsten M. Borgwardt; Malte J. Rasch; Bernhard Schölkopf; Alexander J. Smola (J. Mach. Learn. Res., 2012)

Feature Selection via Dependence Maximization. - Le Song; Alexander J. Smola; Arthur Gretton; Justin Bedo; Karsten M. Borgwardt (J. Mach. Learn. Res., 2012)

Instant foodie: predicting expert ratings from grassroots. - Chenhao Tan; Ed H. Chi; David A. Huffaker; Gueorgi Kossinets; Alexander J. Smola (CIKM, 2013)

Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling. - Amr Ahmed 001; Liangjie Hong; Alexander J. Smola (ICML (3), 2013)

Fastfood - Computing Hilbert Space Expansions in loglinear time. - Quoc V. Le; Tamás Sarlós; Alexander J. Smola (ICML (3), 2013)

The dataminer's guide to scalable mixed-membership and nonparametric bayesian models. - Amr Ahmed 001; Alexander J. Smola (KDD, 2013)

Variance Reduction for Stochastic Gradient Optimization. - Chong Wang 002; Xi Chen 010; Alexander J. Smola; Eric P. Xing (NIPS, 2013)

Hierarchical geographical modeling of user locations from social media posts. - Amr Ahmed 001; Liangjie Hong; Alexander J. Smola (WWW, 2013)

Distributed large-scale natural graph factorization. - Amr Ahmed 001; Nino Shervashidze; Shravan M. Narayanamurthy; Vanja Josifovski; Alexander J. Smola (WWW, 2013)

Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts. - Vidhya Navalpakkam; LaDawn Jentzsch; Rory Sayres; Sujith Ravi; Amr Ahmed 001; Alexander J. Smola (WWW, 2013)

Randomized Nonlinear Component Analysis. - David Lopez-Paz; Suvrit Sra; Alexander J. Smola; Zoubin Ghahramani; Bernhard Schölkopf (ICML, 2014)

The Falling Factorial Basis and Its Statistical Applications. - Yu-Xiang Wang 003; Alexander J. Smola; Ryan J. Tibshirani (ICML, 2014)

Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS). - Qiming Diao; Minghui Qiu; Chao-Yuan Wu; Alexander J. Smola; Jing Jiang 001; Chong Wang 002 (KDD, 2014)

Reducing the sampling complexity of topic models. - Aaron Q. Li; Amr Ahmed 001; Sujith Ravi; Alexander J. Smola (KDD, 2014)

Efficient mini-batch training for stochastic optimization. - Mu Li 003; Tong Zhang 001; Yuqiang Chen; Alexander J. Smola (KDD, 2014)

Communication Efficient Distributed Machine Learning with the Parameter Server. - Mu Li 003; David G. Andersen; Alexander J. Smola; Kai Yu (NIPS, 2014)

Spectral Methods for Indian Buffet Process Inference. - Hsiao-Yu Fish Tung; Alexander J. Smola (NIPS, 2014)

Scaling Distributed Machine Learning with the Parameter Server. - Mu Li 003; David G. Andersen; Jun Woo Park; Alexander J. Smola; Amr Ahmed 001; Vanja Josifovski; James Long; Eugene J. Shekita; Bor-Yiing Su (OSDI, 2014)

Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising. - Amr Ahmed 001; Abhimanyu Das; Alexander J. Smola (WSDM, 2014)

Taxonomy discovery for personalized recommendation. - Yuchen Zhang; Amr Ahmed 001; Vanja Josifovski; Alexander J. Smola (WSDM, 2014)

CoBaFi: collaborative bayesian filtering. - Alex Beutel; Kenton Murray; Christos Faloutsos; Alexander J. Smola (WWW, 2014)

Fastfood: Approximate Kernel Expansions in Loglinear Time. - Quoc Viet Le; Tamás Sarlós; Alexander Johannes Smola (CoRR, 2014)

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

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

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

Deep Fried Convnets. - Zichao Yang; Marcin Moczulski; Misha Denil; Nando de Freitas; Alexander J. Smola; Le Song; Ziyu Wang 001 (ICCV, 2015)

Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods. - Seth R. Flaxman; Andrew Gordon Wilson; Daniel B. Neill; Hannes Nickisch; Alexander J. Smola (ICML, 2015)

Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo. - Yu-Xiang Wang 003; Stephen E. Fienberg; Alexander J. Smola (ICML, 2015)

Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams. - Nan Du 002; Mehrdad Farajtabar; Amr Ahmed 001; Alexander J. Smola; Le Song (KDD, 2015)

Who Supported Obama in 2012?: Ecological Inference through Distribution Regression. - Seth R. Flaxman; Yu-Xiang Wang 003; Alexander J. Smola (KDD, 2015)

Annotating Needles in the Haystack without Looking: Product Information Extraction from Emails. - Weinan Zhang 001; Amr Ahmed 001; Jie Yang; Vanja Josifovski; Alexander J. Smola (KDD, 2015)

Cuckoo Linear Algebra. - Li Zhou 006; David G. Andersen; Mu Li 003; Alexander J. Smola (KDD, 2015)

On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants. - Sashank J. Reddi; Ahmed Hefny; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (NIPS, 2015)

Fast and Guaranteed Tensor Decomposition via Sketching. - Yining Wang; Hsiao-Yu Fish Tung; Alexander J. Smola; Anima Anandkumar (NIPS, 2015)

Fast Differentially Private Matrix Factorization. - Ziqi Liu; Yu-Xiang Wang 003; Alexander J. Smola (RecSys, 2015)

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

Inferring Movement Trajectories from GPS Snippets. - Mu Li 003; Amr Ahmed 001; Alexander J. Smola (WSDM, 2015)

ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly. - Alex Beutel; Amr Ahmed 001; Alexander J. Smola (WWW, 2015)

Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning. - Mu Li 003; Dave G. Andersen; Alexander J. Smola (CoRR, 2015)

AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization. - Suvrit Sra; Adams Wei Yu; Mu Li 003; Alexander J. Smola (CoRR, 2015)

AdaDelay: Delay Adaptive Distributed Stochastic Optimization. - Suvrit Sra; Adams Wei Yu; Mu Li 003; 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. (AISTATS, 2016)

Stochastic Frank-Wolfe methods for nonconvex optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (Allerton, 2016)

Fast incremental method for smooth nonconvex optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CDC, 2016)

Stacked Attention Networks for Image Question Answering. - Zichao Yang; Xiaodong He 001; Jianfeng Gao 001; Li Deng 001; Alexander J. Smola (CVPR, 2016)

Stochastic Variance Reduction for Nonconvex Optimization. - Sashank J. Reddi; Ahmed Hefny; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (ICML, 2016)

Hierarchical Attention Networks for Document Classification. - Zichao Yang; Diyi Yang; Chris Dyer; Xiaodong He 001; Alexander J. Smola; Eduard H. Hovy (HLT-NAACL, 2016)

Variance Reduction in Stochastic Gradient Langevin Dynamics. - Kumar Avinava Dubey; Sashank J. Reddi; Sinead A. Williamson; Barnabás Póczos; Alexander J. Smola; Eric P. Xing (NIPS, 2016)

Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (NIPS, 2016)

Using Navigation to Improve Recommendations in Real-Time. - Chao-Yuan Wu; Christopher V. Alvino; Alexander J. Smola; Justin Basilico (RecSys, 2016)

DiFacto: Distributed Factorization Machines. - Mu Li 003; Ziqi Liu; Alexander J. Smola; Yu-Xiang Wang 003 (WSDM, 2016)

Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering. - Chao-Yuan Wu; Alex Beutel; Amr Ahmed 001; Alexander J. Smola (WWW (Companion Volume), 2016)

AIDE: Fast and Communication Efficient Distributed Optimization. - Sashank J. Reddi; Jakub Konecný; Peter Richtárik; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast Incremental Method for Nonconvex Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Fast Stochastic Methods for Nonsmooth Nonconvex Optimization. - Sashank J. Reddi; Suvrit Sra; Barnabás Póczos; Alexander J. Smola (CoRR, 2016)

Trend Filtering on Graphs. - Yu-Xiang Wang 003; James Sharpnack; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2016)

Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference. - Seth R. Flaxman; Daniel B. Neill; Alexander J. Smola (ACM Trans. Intell. Syst. Technol., 2016)

Data Driven Resource Allocation for Distributed Learning. - Travis Dick; Mu Li 003; Venkata Krishna Pillutla; Colin White; Maria-Florina Balcan; Alexander J. Smola (AAAI Workshops, 2017)

Attributing Hacks. - Ziqi Liu; Alexander J. Smola; Kyle Soska; Yu-Xiang Wang 003; Qinghua Zheng (AISTATS, 2017)

Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning. - Rajarshi Das; Shehzaad Dhuliawala; Manzil Zaheer; Luke Vilnis; Ishan Durugkar; Akshay Krishnamurthy; Alex Smola; Andrew McCallum (AKBC@NIPS, 2017)

Neural Machine Translation with Recurrent Attention Modeling. - Zichao Yang; Zhiting Hu; Yuntian Deng; Chris Dyer; Alexander J. Smola (EACL (2), 2017)

Sampling Matters in Deep Embedding Learning. - R. Manmatha; Chao-Yuan Wu; Alexander J. Smola; Philipp Krähenbühl (ICCV, 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 (Poster), 2017)

Joint Training of Ratings and Reviews with Recurrent Recommender Networks. - Chao-Yuan Wu; Amr Ahmed 001; Alex Beutel; Alexander J. Smola (ICLR (Workshop), 2017)

Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data. - Manzil Zaheer; Amr Ahmed 001; Alexander J. Smola (ICML, 2017)

Canopy Fast Sampling with Cover Trees. - Manzil Zaheer; Satwik Kottur; Amr Ahmed 001; José M. F. Moura; Alexander J. Smola (ICML, 2017)

Deep Sets. - Manzil Zaheer; Satwik Kottur; Siamak Ravanbakhsh; Barnabás Póczos; Ruslan Salakhutdinov; Alexander J. Smola (NIPS, 2017)

Neural Survival Recommender. - How Jing; Alexander J. Smola (WSDM, 2017)

Recurrent Recommender Networks. - Chao-Yuan Wu; Amr Ahmed 001; Alex Beutel; Alexander J. Smola; How Jing (WSDM, 2017)

HashBox: Hash Hierarchical Segmentation exploiting Bounding Box Object Detection. - Joachim de Curtò; Irene Zarza; Alexander J. Smola; Luc Van Gool (CoRR, 2017)

F2F: A Library For Fast Kernel Expansions. - Joachim de Curtò; Irene Zarza; Feng Yang; Alexander J. Smola; Luc Van Gool (CoRR, 2017)

Spectral Methods for Nonparametric Models. - Hsiao-Yu Fish Tung; Chao-Yuan Wu; Manzil Zaheer; Alexander J. Smola (CoRR, 2017)

Efficient Multi-task Feature and Relationship Learning. - Han Zhao 002; Otilia Stretcu; Renato Negrinho; Alexander J. Smola; Geoffrey J. Gordon (CoRR, 2017)

State Space LSTM Models with Particle MCMC Inference. - Xun Zheng; Manzil Zaheer; Amr Ahmed 001; Yuan Wang; Eric P. Xing; Alexander J. Smola (CoRR, 2017)

Variational Reasoning for Question Answering With Knowledge Graph. - Yuyu Zhang; Hanjun Dai; Zornitsa Kozareva; Alexander J. Smola; Le Song (AAAI, 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 (AISTATS, 2018)

Compressed Video Action Recognition. - Chao-Yuan Wu; Manzil Zaheer; Hexiang Hu; R. Manmatha; Alexander J. Smola; Philipp Krähenbühl (CVPR, 2018)

Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning. - Rajarshi Das; Shehzaad Dhuliawala; Manzil Zaheer; Luke Vilnis; Ishan Durugkar; Akshay Krishnamurthy; Alex Smola; Andrew McCallum (ICLR (Poster), 2018)

Learning Steady-States of Iterative Algorithms over Graphs. - Hanjun Dai; Zornitsa Kozareva; Bo Dai 001; Alexander J. Smola; Le Song (ICML, 2018)

Detecting and Correcting for Label Shift with Black Box Predictors. - Zachary C. Lipton; Yu-Xiang Wang 003; Alexander J. Smola (ICML, 2018)

Algorithms, Data, Hardware and Tools: A Perfect Storm. - Alex Smola (KDD, 2018)

Deep Graphs. - Emmanouil Antonios Platanios; Alex Smola (CoRR, 2018)

Deep Factors with Gaussian Processes for Forecasting. - Danielle C. Maddix; Yuyang Wang 001; Alex Smola (CoRR, 2018)

Dive into Deep Learning for Natural Language Processing. - Haibin Lin; Xingjian Shi; Leonard Lausen; Aston Zhang; He He 001; Sheng Zha; Alexander J. Smola (EMNLP/IJCNLP (2), 2019)

Recognizing Variables from Their Data via Deep Embeddings of Distributions. - Jonas Mueller 001; Alex Smola (ICDM, 2019)

Deep Factors for Forecasting. - Yuyang Wang 001; Alex Smola; Danielle C. Maddix; Jan Gasthaus; Dean P. Foster; Tim Januschowski (ICML, 2019)

FastPoint: Scalable Deep Point Processes. - Ali Caner Türkmen; Yuyang Wang 001; Alexander J. Smola (ECML/PKDD (2), 2019)

Efficient Multitask Feature and Relationship Learning. - Han Zhao 002; Otilia Stretcu; Alexander J. Smola; Geoffrey J. Gordon (UAI, 2019)

P3O: Policy-on Policy-off Policy Optimization. - Rasool Fakoor; Pratik Chaudhari; Alexander J. Smola (UAI, 2019)

SysML: The New Frontier of Machine Learning Systems. - 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; et al. (CoRR, 2019)

Language Models with Transformers. - Chenguang Wang 001; Mu Li 003; Alexander J. Smola (CoRR, 2019)

Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs. - Minjie Wang; Lingfan Yu; Da Zheng; Quan Gan; Yu Gai; Zihao Ye 001; Mufei Li; Jinjing Zhou; Qi Huang 001; Chao Ma 025; Ziyue Huang; Qipeng Guo; et al. (CoRR, 2019)

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

An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph. - Jiarui Jin; Jiarui Qin; Yuchen Fang; Kounianhua Du; Weinan Zhang 001; Yong Yu 001; Zheng Zhang 001; Alexander J. Smola (KDD, 2020)

Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data. - Jonas Mueller 001; Xingjian Shi; Alexander J. Smola (KDD, 2020)

Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation. - Rasool Fakoor; Jonas Mueller 001; Nick Erickson; Pratik Chaudhari; Alexander J. Smola (NeurIPS, 2020)

Elastic Machine Learning Algorithms in Amazon SageMaker. - 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; et al. (SIGMOD Conference, 2020)

Transformer on a Diet. - Chenguang Wang 001; Zihao Ye 001; Aston Zhang; Zheng Zhang 001; Alexander J. Smola (CoRR, 2020)

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data. - Nick Erickson; Jonas Mueller 001; Alexander Shirkov; Hang Zhang 005; Pedro Larroy; Mu Li 003; Alexander J. Smola (CoRR, 2020)

TraDE: Transformers for Density Estimation. - Rasool Fakoor; Pratik Chaudhari; Jonas Mueller 001; Alexander J. Smola (CoRR, 2020)

Improving Semantic Segmentation via Self-Training. - Yi Zhu 001; Zhongyue Zhang; Chongruo Wu; Zhi Zhang 005; Tong He 002; Hang Zhang 005; R. Manmatha; Mu Li 003; Alexander J. Smola (CoRR, 2020)

Tiering as a Stochastic Submodular Optimization Problem. - Hyokun Yun; Michael Froh; Roshan Makhijani; Brian Luc; Alex Smola; Trishul Chilimbi (CoRR, 2020)

DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning. - Rasool Fakoor; Pratik Chaudhari; Alexander J. Smola (CoRR, 2020)

Bloom Origami Assays: Practical Group Testing. - Louis Abraham; Gary Bécigneul; Benjamin Coleman; Bernhard Schölkopf; Anshumali Shrivastava; Alexander J. Smola (CoRR, 2020)

Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network. - Jiarui Jin; Kounianhua Du; Weinan Zhang 001; Jiarui Qin; Yuchen Fang; Yong Yu 001; Zheng Zhang 001; Alexander J. Smola (CoRR, 2020)

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 (AAAI, 2021)

Deep Explicit Duration Switching Models for Time Series. - Abdul Fatir Ansari; Konstantinos Benidis; Richard Kurle; Ali Caner Türkmen; Harold Soh; Alexander J. Smola; Bernie Wang 001; Tim Januschowski (NeurIPS, 2021)

Continuous Doubly Constrained Batch Reinforcement Learning. - Rasool Fakoor; Jonas Mueller 001; Kavosh Asadi; Pratik Chaudhari; Alexander J. Smola (NeurIPS, 2021)

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

Benchmarking Multimodal AutoML for Tabular Data with Text Fields. - Xingjian Shi; Jonas Mueller 001; Nick Erickson; Mu Li 003; Alexander J. Smola (NeurIPS Datasets and Benchmarks, 2021)

Continuous Doubly Constrained Batch Reinforcement Learning. - Rasool Fakoor; Jonas Mueller 001; Pratik Chaudhari; Alexander J. Smola (CoRR, 2021)

Deep Quantile Aggregation. - Taesup Kim; Rasool Fakoor; Jonas Mueller 001; Alexander J. Smola; Ryan J. Tibshirani (CoRR, 2021)

IRLI: Iterative Re-partitioning for Learning to Index. - Gaurav Gupta; Tharun Medini; Anshumali Shrivastava; Alexander J. Smola (CoRR, 2021)

Dive into Deep Learning. - Aston Zhang; Zachary C. Lipton; Mu Li 003; Alexander J. Smola (CoRR, 2021)

Deep Q-Network with Proximal Iteration. - Kavosh Asadi; Rasool Fakoor; Omer Gottesman; Michael L. Littman; Alexander J. Smola (CoRR, 2021)

ResNeSt: Split-Attention Networks. - Hang Zhang 005; Chongruo Wu; Zhongyue Zhang; Yi Zhu 001; Haibin Lin; Zhi Zhang 005; Yue Sun; Tong He 002; Jonas Mueller 001; R. Manmatha; Mu Li 003; Alexander J. Smola (CVPR Workshops, 2022)

Partial and Asymmetric Contrastive Learning for Out-of-Distribution Detection in Long-Tailed Recognition. - Haotao Wang; Aston Zhang; Yi Zhu 001; Shuai Zheng 004; Mu Li 003; Alex J. Smola; Zhangyang Wang (ICML, 2022)

Multimodal AutoML for Image, Text and Tabular Data. - Nick Erickson; Xingjian Shi; James Sharpnack; Alexander J. Smola (KDD, 2022)

BLISS: A Billion scale Index using Iterative Re-partitioning. - Gaurav Gupta; Tharun Medini; Anshumali Shrivastava; Alexander J. Smola (KDD, 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 (NeurIPS, 2022)

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

Data drift correction via time-varying importance weight estimator. - Rasool Fakoor; Jonas Mueller 001; Zachary C. Lipton; Pratik Chaudhari; Alexander J. Smola (CoRR, 2022)

GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network. - Jiarui Jin; Kounianhua Du; Weinan Zhang 001; Jiarui Qin; Yuchen Fang; Yong Yu 001; Zheng Zhang 001; Alexander J. Smola (ACM Trans. Inf. Syst., 2022)

A Cheaper and Better Diffusion Language Model with Soft-Masked Noise. - Jiaao Chen; Aston Zhang; Mu Li 003; Alex Smola; Diyi Yang (EMNLP, 2023)

Automatic Chain of Thought Prompting in Large Language Models. - Zhuosheng Zhang 001; Aston Zhang; Mu Li 003; Alex Smola (ICLR, 2023)

Parameter-Efficient Fine-Tuning Design Spaces. - Jiaao Chen; Aston Zhang; Xingjian Shi; Mu Li 003; Alex Smola; Diyi Yang (ICLR, 2023)

RLSbench: Domain Adaptation Under Relaxed Label Shift. - Saurabh Garg; Nick Erickson; James Sharpnack; Alex Smola; Sivaraman Balakrishnan; Zachary Chase Lipton (ICML, 2023)

Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition. - Shuhuai Ren; Aston Zhang; Yi Zhu 001; Shuai Zhang; Shuai Zheng 004; Mu Li 003; Alexander J. Smola; Xu Sun 001 (NeurIPS, 2023)

Flexible Model Aggregation for Quantile Regression. - Rasool Fakoor; Taesup Kim; Jonas Mueller 001; Alexander J. Smola; Ryan J. Tibshirani (J. Mach. Learn. Res., 2023)

Time-Varying Propensity Score to Bridge the Gap between the Past and Present. - Rasool Fakoor; Jonas Mueller 001; Zachary Chase Lipton; Pratik Chaudhari; Alex Smola (ICLR, 2024)

Multimodal Chain-of-Thought Reasoning in Language Models. - Zhuosheng Zhang 001; Aston Zhang; Mu Li 003; Hai Zhao 001; George Karypis; Alex Smola (Trans. Mach. Learn. Res., 2024)

Improving Semantic Segmentation via Efficient Self-Training. - Yi Zhu 001; Zhongyue Zhang; Chongruo Wu; Zhi Zhang 005; Tong He 002; Hang Zhang 005; R. Manmatha; Mu Li 003; Alexander J. Smola (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

L3Ms - Lagrange Large Language Models. - Guneet S. Dhillon; Xingjian Shi; Yee Whye Teh; Alex Smola (ICLR, 2025)

RPGBENCH: Evaluating Large Language Models as Role-Playing Game Engines. - Pengfei Yu; Dongming Shen; Silin Meng; Jaewon Lee; Weisu Yin; Andrea Yaoyun Cui; Zhenlin Xu; Yi Zhu 001; Xingjian Shi; Mu Li 003; Alex Smola (CoRR, 2025)

EmergentTTS-Eval: Evaluating TTS Models on Complex Prosodic, Expressiveness, and Linguistic Challenges Using Model-as-a-Judge. - Ruskin Raj Manku; Yuzhi Tang; Xingjian Shi; Mu Li 003; Alex Smola (CoRR, 2025)