Alexander J. Smola
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Machine learning scientist
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Alexander J. Smolacomputer-science Degrees
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Machine Learning
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Computer Science
Alexander J. Smola's Degrees
- PhD Computer Science University of Technology Sydney
- Masters Computer Science University of Technology Sydney
- Bachelors Computer Science University of Technology Sydney
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Why Is Alexander J. Smola Influential?
(Suggest an Edit or Addition)Alexander J. Smola's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- A tutorial on support vector regression (2004) (10276)
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem (1998) (8278)
- Learning with kernels (1998) (6780)
- Advances in kernel methods: support vector learning (1999) (5368)
- Estimating the Support of a High-Dimensional Distribution (2001) (5079)
- Support Vector Regression Machines (1996) (4145)
- Hierarchical Attention Networks for Document Classification (2016) (3798)
- A Kernel Two-Sample Test (2012) (3479)
- Learning with Kernels: support vector machines, regularization, optimization, and beyond (2001) (3425)
- Support Vector Method for Function Approximation, Regression Estimation and Signal Processing (1996) (2813)
- New Support Vector Algorithms (2000) (2761)
- Kernel Principal Component Analysis (1997) (2323)
- Support Vector Method for Novelty Detection (1999) (1861)
- A Kernel Method for the Two-Sample-Problem (2006) (1785)
- Stacked Attention Networks for Image Question Answering (2015) (1657)
- A Generalized Representer Theorem (2001) (1648)
- Scaling Distributed Machine Learning with the Parameter Server (2014) (1586)
- Correcting Sample Selection Bias by Unlabeled Data (2006) (1551)
- Kernel methods in machine learning (2007) (1434)
- Deep Sets (2017) (1434)
- Measuring Statistical Dependence with Hilbert-Schmidt Norms (2005) (1392)
- Input space versus feature space in kernel-based methods (1999) (1265)
- Parallelized Stochastic Gradient Descent (2010) (1256)
- Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006) (1132)
- Online learning with kernels (2001) (1097)
- Advances in Large Margin Classifiers (2000) (1069)
- Kernel PCA and De-Noising in Feature Spaces (1998) (1062)
- Predicting Time Series with Support Vector Machines (1997) (1023)
- Feature hashing for large scale multitask learning (2009) (946)
- Kernels and Regularization on Graphs (2003) (906)
- Protein function prediction via graph kernels (2005) (890)
- A Hilbert Space Embedding for Distributions (2007) (834)
- Sampling Matters in Deep Embedding Learning (2017) (748)
- Sparse Greedy Matrix Approximation for Machine Learning (2000) (745)
- A Kernel Statistical Test of Independence (2007) (715)
- The connection between regularization operators and support vector kernels (1998) (690)
- ResNeSt: Split-Attention Networks (2020) (680)
- Efficient mini-batch training for stochastic optimization (2014) (644)
- Recurrent Recommender Networks (2017) (582)
- Distributed large-scale natural graph factorization (2013) (568)
- Multi-Instance Kernels (2002) (545)
- Covariate Shift by Kernel Mean Matching (2009) (524)
- Stochastic Variance Reduction for Nonconvex Optimization (2016) (510)
- Communication Efficient Distributed Machine Learning with the Parameter Server (2014) (503)
- Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites (2000) (477)
- Probabilities for SV Machines (2000) (451)
- Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs (2019) (450)
- An architecture for parallel topic models (2010) (448)
- Learning Graph Matching (2007) (446)
- Query Learning with Large Margin Classifiers (2000) (433)
- COFI RANK - Maximum Margin Matrix Factorization for Collaborative Ranking (2007) (424)
- Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS) (2014) (421)
- Like like alike: joint friendship and interest propagation in social networks (2011) (413)
- Discovering geographical topics in the twitter stream (2012) (408)
- Fastfood: Approximate Kernel Expansions in Loglinear Time (2013) (408)
- Sparse Greedy Gaussian Process Regression (2000) (403)
- Nonparametric Quantile Estimation (2006) (398)
- Learning the Kernel with Hyperkernels (2005) (368)
- Slow Learners are Fast (2009) (366)
- Fast Kernels for String and Tree Matching (2002) (361)
- Prior Knowledge in Support Vector Kernels (1997) (360)
- Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning (2017) (358)
- Feature Selection via Dependence Maximization (2012) (347)
- Kernel Methods for Measuring Independence (2005) (345)
- On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion (1998) (333)
- Supervised feature selection via dependence estimation (2007) (332)
- Detecting and Correcting for Label Shift with Black Box Predictors (2018) (330)
- Neural Information Processing Systems (1997) (326)
- Hilbert space embeddings of conditional distributions with applications to dynamical systems (2009) (304)
- Variational Reasoning for Question Answering with Knowledge Graph (2017) (300)
- Kernel Methods for Missing Variables (2005) (284)
- Bundle Methods for Regularized Risk Minimization (2010) (280)
- Scalable inference in latent variable models (2012) (280)
- Regression estimation with support vector learning machines (1996) (261)
- Learning with non-positive kernels (2004) (259)
- Compressed Video Action Recognition (2017) (248)
- Deep Fried Convnets (2014) (246)
- Hash Kernels for Structured Data (2009) (246)
- Second Order Cone Programming Approaches for Handling Missing and Uncertain Data (2006) (245)
- Hilbert Space Embeddings of Hidden Markov Models (2010) (229)
- Estimating labels from label proportions (2008) (224)
- Dynamic Alignment Kernels (2000) (224)
- Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo (2015) (222)
- Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces (2003) (221)
- Super-Samples from Kernel Herding (2010) (221)
- The Need for Open Source Software in Machine Learning (2007) (219)
- Invariant Feature Extraction and Classification in Kernel Spaces (1999) (218)
- Shrinking the Tube: A New Support Vector Regression Algorithm (1998) (218)
- Trend Filtering on Graphs (2014) (215)
- AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020) (215)
- Reducing the sampling complexity of topic models (2014) (210)
- Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy (2016) (207)
- Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators (2001) (200)
- On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants (2015) (191)
- A scalable modular convex solver for regularized risk minimization (2007) (191)
- Parameter Server for Distributed Machine Learning (2013) (177)
- Scalable distributed inference of dynamic user interests for behavioral targeting (2011) (177)
- Heteroscedastic Gaussian process regression (2005) (176)
- Support Vector Machine Reference Manual (1998) (173)
- Randomized Nonlinear Component Analysis (2014) (168)
- Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams (2015) (166)
- Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016) (166)
- Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization (2016) (158)
- Learning Networks of Heterogeneous Influence (2012) (158)
- Fastfood - Computing Hilbert Space Expansions in loglinear time (2013) (155)
- Collaborative competitive filtering: learning recommender using context of user choice (2011) (151)
- Bundle Methods for Machine Learning (2007) (150)
- Improving maximum margin matrix factorization (2008) (149)
- Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes (2007) (147)
- Human Action Segmentation and Recognition Using Discriminative Semi-Markov Models (2011) (144)
- Neural Survival Recommender (2017) (141)
- Experimentally optimal v in support vector regression for different noise models and parameter settings (2004) (139)
- Unifying Divergence Minimization and Statistical Inference Via Convex Duality (2006) (138)
- Predicting Structured Data (Neural Information Processing) (2007) (137)
- Learning Steady-States of Iterative Algorithms over Graphs (2018) (133)
- Kernel PCA pattern reconstruction via approximate pre-images. (1998) (132)
- AIDE: Fast and Communication Efficient Distributed Optimization (2016) (132)
- Discriminative human action segmentation and recognition using semi-Markov model (2008) (131)
- Classification in a normalized feature space using support vector machines (2003) (128)
- Regularized Principal Manifolds (1999) (125)
- A Tutorial Introduction (2001) (125)
- Asymptotically Optimal Choice of ε-Loss for Support Vector Machines (1998) (124)
- A la Carte - Learning Fast Kernels (2014) (122)
- Variance Reduction for Stochastic Gradient Optimization (2013) (122)
- Stochastic Frank-Wolfe methods for nonconvex optimization (2016) (120)
- Word Features for Latent Dirichlet Allocation (2010) (119)
- Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009) (119)
- Regularization with Dot-Product Kernels (2000) (117)
- A dependence maximization view of clustering (2007) (117)
- Friend or frenemy?: predicting signed ties in social networks (2012) (116)
- Fast and Guaranteed Tensor Decomposition via Sketching (2015) (114)
- Direct Optimization of Ranking Measures (2007) (113)
- Hierarchical geographical modeling of user locations from social media posts (2013) (110)
- Deep Factors for Forecasting (2019) (110)
- Colored Maximum Variance Unfolding (2007) (109)
- Convex Learning with Invariances (2007) (106)
- General cost functions for support vector regression. (1998) (105)
- Semiparametric Support Vector and Linear Programming Machines (1998) (104)
- Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (2012) (104)
- Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts (2013) (104)
- Classification on proximity data with LP-machines (1999) (104)
- Fast Differentially Private Matrix Factorization (2015) (102)
- Linear programs for automatic accuracy control in regression. (1999) (102)
- Kernelized Sorting (2008) (101)
- From Regularization Operators to Support Vector Kernels (1997) (99)
- An improved training algorithm for kernel Fisher discriminants (2001) (98)
- Fast Approximation of Support Vector Kernel Expansions, and an Interpretation of Clustering as Approximation in Feature Spaces (1998) (95)
- Kernel methods and the exponential family (2006) (95)
- Variance Reduction in Stochastic Gradient Langevin Dynamics (2016) (89)
- Unified analysis of streaming news (2011) (88)
- A Second Order Cone programming Formulation for Classifying Missing Data (2004) (85)
- A Short Introduction to Learning with Kernels (2002) (82)
- Taxonomy discovery for personalized recommendation (2014) (81)
- A Generic Approach for Escaping Saddle points (2017) (81)
- Hash Kernels (2009) (80)
- Gaussian process classification for segmenting and annotating sequences (2004) (80)
- Language Models with Transformers (2019) (79)
- Online Inference for the Infinite Topic-Cluster Model: Storylines from Streaming Text (2011) (78)
- Elastic Machine Learning Algorithms in Amazon SageMaker (2020) (78)
- Fast Kronecker Inference in Gaussian Processes with non-Gaussian Likelihoods (2015) (77)
- An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph (2020) (76)
- Relative Novelty Detection (2009) (73)
- Tighter Bounds for Structured Estimation (2008) (72)
- Generalization Bounds and Consistency for Structured Labeling (2007) (70)
- Sparse Kernel Feature Analysis (2002) (70)
- Collaborative Email-Spam Filtering with the Hashing-Trick (2009) (69)
- Nonparametric Quantile Regression (2005) (67)
- Meta-Q-Learning (2019) (67)
- Fast incremental method for smooth nonconvex optimization (2016) (64)
- Hyperkernels (2002) (64)
- Minimal Kernel Classifiers (2003) (62)
- Kernel Constrained Covariance for Dependence Measurement (2005) (61)
- Gene selection via the BAHSIC family of algorithms (2007) (61)
- Tailoring density estimation via reproducing kernel moment matching (2008) (60)
- CoBaFi: collaborative bayesian filtering (2014) (60)
- Exponential Families for Conditional Random Fields (2004) (58)
- Doubly Robust Covariate Shift Correction (2015) (57)
- Gaussian Processes and SVM: Mean Field and Leave-One-Out (2000) (56)
- The kernel mutual information (2003) (56)
- Discriminative frequent subgraph mining with optimality guarantees (2010) (56)
- Learning via Hilbert Space Embedding of Distributions (2007) (55)
- Laplace Propagation (2003) (55)
- DiFacto: Distributed Factorization Machines (2016) (55)
- Who Supported Obama in 2012?: Ecological Inference through Distribution Regression (2015) (54)
- Introduction to support vector learning (1999) (51)
- Fast Stochastic Methods for Nonsmooth Nonconvex Optimization (2016) (51)
- Neural Machine Translation with Recurrent Attention Modeling (2016) (51)
- Inferring Movement Trajectories from GPS Snippets (2015) (48)
- Multitask Learning without Label Correspondences (2010) (47)
- Collaborative Filtering on a Budget (2010) (47)
- Latent LSTM Allocation: Joint Clustering and Non-Linear Dynamic Modeling of Sequence Data (2017) (47)
- Regularization Networks and Support Vector Machines (2000) (45)
- ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly (2014) (44)
- Improving Semantic Segmentation via Self-Training (2020) (43)
- Distribution Matching for Transduction (2009) (43)
- Fast Incremental Method for Nonconvex Optimization (2016) (43)
- IntervalRank: isotonic regression with listwise and pairwise constraints (2010) (43)
- A Kernel Approach to Comparing Distributions (2007) (42)
- Adapting Codes and Embeddings for Polychotomies (2002) (42)
- Nested Chinese Restaurant Franchise Process: Applications to User Tracking and Document Modeling (2013) (41)
- Fair and balanced: learning to present news stories (2012) (40)
- Introduction to Machine Learning (2020) (40)
- Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising (2014) (40)
- The Falling Factorial Basis and Its Statistical Applications (2014) (39)
- Simpler knowledge-based support vector machines (2006) (39)
- Learning high-order MRF priors of color images (2006) (38)
- Gaussian Processes for Independence Tests with Non-iid Data in Causal Inference (2015) (38)
- Kernel-Based Machine Learning Lab (2016) (38)
- Step Size Adaptation in Reproducing Kernel Hilbert Space (2006) (37)
- Wearable sensor activity analysis using semi-Markov models with a grammar (2010) (37)
- Bounds on Error Expectation for SVM (2000) (37)
- State Space LSTM Models with Particle MCMC Inference (2017) (36)
- Linear Discriminant and Support Vector Classifiers (2000) (35)
- Multiple domain user personalization (2011) (35)
- Entropy Numbers, Operators and Support Vector Kernels (1999) (34)
- Support Vector Machines and Kernel Algorithms (2002) (33)
- Support Vector Machines and Kernel Algorithms (2002) (33)
- Kernel Measures of Independence for non-iid Data (2008) (32)
- Adaptive collaborative filtering (2008) (32)
- AdaDelay: Delay Adaptive Distributed Stochastic Optimization (2016) (31)
- Joint Training of Ratings and Reviews with Recurrent Recommender Networks (2016) (31)
- P3O: Policy-on Policy-off Policy Optimization (2019) (31)
- Concepts and Tools (2001) (30)
- Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation (2020) (30)
- Communication Efficient Coresets for Empirical Loss Minimization (2015) (30)
- GACV for Support Vector Machines (2000) (30)
- A Review of Kernel Methods in Machine Learning (2006) (29)
- AdaDelay: Delay Adaptive Distributed Stochastic Convex Optimization (2015) (29)
- Deep Factors with Gaussian Processes for Forecasting (2018) (29)
- Bioresorbable terpolymers based on L-lactide, glycolide and trimethylene carbonate with shape memory behaviour (2014) (29)
- Scalable clustering of news search results (2011) (28)
- Introduction to Large Margin Classifiers (2000) (26)
- Binet-Cauchy Kernels (2004) (26)
- Support Vector Machines (2005) (25)
- Kernel Methods and Support Vector Machines (2003) (25)
- Asymptotically optimal choice of varepsilon-loss for support vector machines. (1998) (24)
- Machine learning using hyperkernels (2003) (24)
- Annotating Needles in the Haystack without Looking: Product Information Extraction from Emails (2015) (24)
- Robust Ensemble Learning for Data Mining (2000) (24)
- Kernel method for percentile feature extraction (2000) (23)
- Bayesian Kernel Methods (2002) (23)
- Symbolic Music Generation with Transformer-GANs (2021) (23)
- Fast hierarchical Gaussian processes (2015) (22)
- Parallel Online Learning (2011) (22)
- Adaptive Margin Support Vector Machines (2000) (22)
- Linear-Time Estimators for Propensity Scores (2011) (22)
- Bid generation for advanced match in sponsored search (2011) (21)
- Linear support vector machines via dual cached loops (2012) (21)
- MLSys: The New Frontier of Machine Learning Systems (2019) (21)
- SysML: The New Frontier of Machine Learning Systems (2019) (20)
- Computing the Bayes Kernel Classifier (2000) (20)
- Efficient Multitask Feature and Relationship Learning (2017) (20)
- Hokusai - Sketching Streams in Real Time (2012) (20)
- Exponential Stochastic Cellular Automata for Massively Parallel Inference (2016) (20)
- Entropy Numbers of Linear Function Classes (2000) (20)
- Energy-Based Models (2007) (19)
- Large-Scale Multiclass Transduction (2005) (19)
- Using Navigation to Improve Recommendations in Real-Time (2016) (18)
- TraDE: Transformers for Density Estimation (2020) (18)
- Stochastic Frank-Wolfe methods for nonconvex optimization (2016) (18)
- Kernel Machines and Boolean Functions (2001) (18)
- Hilbert space embeddings in dynamical systems (2003) (18)
- Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering (2015) (17)
- FastPoint: Scalable Deep Point Processes (2019) (17)
- Support vector machine learning (2001) (17)
- Convex Cost Functions for Support Vector Regression (1998) (17)
- Machine Learning with Hyperkernels (2003) (17)
- Spectral Methods for Indian Buffet Process Inference (2014) (16)
- Guest editorial: model selection and optimization in machine learning (2011) (16)
- Beyond the Margin (2000) (16)
- A Tutorial Review of RKHS Methods in Machine Learning (2005) (15)
- Advanced Lectures on Machine Learning (2003) (15)
- An Adversarial View of Covariate Shift and A Minimax Approach (2009) (15)
- Data Driven Resource Allocation for Distributed Learning (2015) (15)
- Kernel extrapolation (2006) (15)
- v-Arc: Ensemble Learning in the Presence of Outliers (1999) (15)
- Preferential Attachment in Graphs with Affinities (2015) (14)
- Large Margin Classification for Moving Targets (2002) (14)
- Graph Partitioning via Parallel Submodular Approximation to Accelerate Distributed Machine Learning (2015) (14)
- Semi-Markov Models for Sequence Segmentation (2007) (14)
- A Short Tour of Kernel Methods for Graphs (2005) (14)
- Boîte à outils SVM simple et rapide (2005) (13)
- Invariances in Classification: an efficient SVM implementation (2005) (13)
- Quantization Functionals and Regularized Principal Manifolds (1998) (13)
- Newton-Like Methods for Nonparametric Independent Component Analysis (2006) (12)
- Multimodal AutoML on Structured Tables with Text Fields (2021) (12)
- Continuous Doubly Constrained Batch Reinforcement Learning (2021) (12)
- Instant foodie: predicting expert ratings from grassroots (2013) (12)
- Robust Near-Isometric Matching via Structured Learning of Graphical Models (2008) (11)
- Kernel Fisher Discriminant (2001) (11)
- Regret Bounds for Deterministic Gaussian Process Bandits (2012) (11)
- Web-scale multi-task feature selection for behavioral targeting (2012) (11)
- Une boîte à outils rapide et simple pour les SVM (2004) (10)
- Experimentally optimal nu in support vector regression for different noise models and parameter settings (2005) (10)
- Generalized Support Vector Machines (2000) (9)
- FastEx: Hash Clustering with Exponential Families (2012) (9)
- Natural Regularization from Generative Models (2000) (9)
- A General Regression Framework for Learning String-to-String Mappings (2007) (9)
- Optimal Web-Scale Tiering as a Flow Problem (2010) (9)
- Parametric model-based clustering (2005) (8)
- The Nested Chinese Restaurant Franchise Process: User Tracking and Document Modeling (2013) (8)
- Lernen mit Kernen (1999) (8)
- Go for a Walk and Arrive at the Answer: Reasoning Over Knowledge Bases with Reinforcement Learning (2017) (8)
- Transductive Gaussian Process Regression with Automatic Model Selection (2006) (8)
- Entropy Numbers for Convex Combinations and MLPs (2000) (8)
- Deep Quantile Aggregation (2021) (7)
- Large Margin Bank Boundaries for Ordinal Regression (2000) (7)
- Efficient Algorithms for Max-Margin Structured Classification (2007) (7)
- Generalization bounds and learning rates for Regularized principal manifolds (1998) (7)
- Exponential Families and Kernels (2004) (7)
- Recognizing Variables from Their Data via Deep Embeddings of Distributions (2019) (7)
- Generalization Bounds for Convex Combinations of Kernel Functions (1998) (6)
- Delayed Proximal Gradient Methods (2013) (6)
- Performance Evaluation of the NVIDIA GeForce 8800 GTX GPU for Machine Learning (2008) (6)
- Deep Explicit Duration Switching Models for Time Series (2021) (6)
- Biodegradowalne polimery z pamięcią kształtu (2010) (6)
- Elements of Statistical Learning Theory (2001) (6)
- Advanced Lectures on Machine Learning: Machine Learning Summer School 2002, Canberra, Australia, February 11-22, 2002, Revised Lectures (2003) (5)
- Lernen mit Kernen Support-Vektor-Methoden zur Analyse hochdimensionaler Daten (1999) (5)
- Optimal Message Scheduling for Aggregation (2018) (5)
- Support Vectors and Statistical Mechanics (2000) (5)
- Transformer on a Diet (2020) (5)
- Spectral Methods for Nonparametric Models (2017) (4)
- Improving Maximum Margin Matrix Margin Factorization (2008) (4)
- Boron-chelate assisted synthesis of new bipyrazole derivatives (2018) (4)
- A Compression Framework for Generating User Profiles (2010) (4)
- Faster, Simpler, More Accurate: Practical Automated Machine Learning with Tabular, Text, and Image Data (2020) (4)
- Distributed Flow Algorithms for Scalable Similarity Visualization (2010) (3)
- Dive into Deep Learning (2021) (3)
- A project of bioresorbable self-expanding vascular stents. The crimping process numerical simulation (2018) (3)
- The Entropy Regularization Information Criterion (1999) (3)
- Kernel peA and DeNoising in Feature Spaces (1998) (3)
- Choosing /spl nu/ in support vector regression with different noise models-theory and experiments (2000) (3)
- Combining near-optimal feature selection with gSpan (2008) (3)
- Attributing Hacks (2016) (2)
- Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network (2020) (2)
- Badania mechanizmu biodegradacji in vivo terpolimerów z pamięcią kształtu (2011) (2)
- McKernel: A Library for Approximate Kernel Expansions in Log-linear Time (2017) (2)
- Kernel Feature Extraction (2001) (2)
- Bayesian Kernel Models (2003) (2)
- NICTA at TRECVID 2005 Shot Boundary Detection Task (2005) (2)
- DDPG++: Striving for Simplicity in Continuous-control Off-Policy Reinforcement Learning (2020) (2)
- Bloom Origami Assays: Practical Group Testing (2020) (2)
- COLLABORATIVE SPAM FILTERING WITH THE HASHING TRICK (2009) (2)
- Natural Regularization in SVMs (2015) (1)
- Cuckoo Linear Algebra (2015) (1)
- Nowe semikrystaliczne bioresorbowalne materiały z pamięcią kształtu (2009) (1)
- Canopy Fast Sampling with Cover Trees (2017) (1)
- OPTIMIZED MAXIMUM MEAN DISCREPANCY (2016) (1)
- Margin Distribution and Soft Margin (2000) (1)
- Learning as Search Optimization (2007) (1)
- Single-Class Problems: Quantile Estimation and Novelty Detection (2001) (1)
- A kernel method to comparing distributions (2007) (1)
- Joint Hacking and Latent Hazard Rate Estimation (2016) (1)
- Correlates of homicide: new space/time interaction tests for spatiotemporal point processes (2013) (1)
- Robust Ensemble Learning for Data (2000) (1)
- Bound on the Leave-One-Out Error for Density Support Estimation using nu-SVMs (2001) (1)
- Step size-adapted online support vector learning (2005) (1)
- Spectral Methods for the Hierarchical Dirichlet Process (2015) (1)
- Towards a Strategy for Boosting Regressors (2000) (1)
- [Surgical prevention of acute stroke in atherosclerotic carotid stenosis]. (2015) (1)
- Proc.17th Int Conference on Machine Learning (2000) (1)
- A Machine Learning Approach to Technology Enhanced Learning (2010) (1)
- F2F: A Library For Fast Kernel Expansions (2017) (1)
- svlab - A Kernel Methods Package (2003) (1)
- The BAHSIC family of gene selection algorithms (2006) (1)
- Attributing hacks with survival trend filtering (2017) (1)
- ACCAMS (2015) (1)
- Measurement and modeling of eye-mouse behavior (2013) (1)
- Joint Regularization (2005) (1)
- MFSPFA: An Enhanced Filter based Feature Selection Algorithm (2020) (0)
- Kangaroo Mother Care and Traditional Care (2019) (0)
- Measuring Similarity with Kernels (2007) (0)
- Scalable graph kernels with approximate matching of subtree patterns (2010) (0)
- Learning Theory Revisited (2001) (0)
- Otrzymywanie nowych, wysokoelastycznych i bioresorbowalnych kopolimerów węglanów alifatycznych (2013) (0)
- Risk and Loss Functions (2001) (0)
- Degradacja hydrolityczna rusztowań komórkowych formowanych z terpolimerów; L-laktydu, glikolidu i TMC, oraz L-laktydu, glikolidu i ε-kaprolaktonu (2015) (0)
- Sample based generalisation bounds (2004) (0)
- Maximal Margin Perception (2000) (0)
- Pre-Images and Reduced Set Methods (2001) (0)
- Appendix to Supervised Feature Selection via Dependence Estimation (2006) (0)
- Email-Spam Filtering with the Hashing-Trick (2009) (0)
- Gaussian Process Belief Propagation (2007) (0)
- 非凸最適化のための確率的Frank‐Wolfe法【Powered by NICT】 (2016) (0)
- Supplementary Material : Compressed Video Action Recognition (2018) (0)
- 1 Covariate Shift by Kernel Mean Matching (2008) (0)
- Ëñóðð¸öøððøø¸ë Óððóôô¸òò Ë Blockinùùöññò×× Úò Blockin Blockin× Ò Ääööö Ååöööò Ðð××׬¬ö× ½½½½»½½»¼¿ ½½½¿¿ (1999) (0)
- Sparse GreedyGaussian Pro ess Regression (2001) (0)
- Appendix : Kernelized Sorting (2008) (0)
- Ocena wpływu simwastatyny na degradację terpolimeru z pamięcią kształtu (2012) (0)
- Kernel Ma hines and Boolean Fun tions (2002) (0)
- Sparse GreedyGaussian Pro ess Regression (2001) (0)
- Machine Learning Program , National ICT for Australia , Canberra , ACT 0200 , Australia (2003) (0)
- Notation and Symbols (2001) (0)
- Tiering as a Stochastic Submodular Optimization Problem (2020) (0)
- Supplementary Material for “ Trend Filtering on Graphs ” (2015) (0)
- Universal Clustering with Regularization in Probabilistic Space (2005) (0)
- Structured Prediction Based on Discriminative Models (2007) (0)
- Discriminative Learning of Prediction Suffix Trees with the Perceptron Algorithm (2007) (0)
- Leave-One-Out Methods (2000) (0)
- A (stochastic) Em in General (2016) (0)
- Graphical Models for the Internet (2011) (0)
- Sample Based GeneralizationBounds 1 (1999) (0)
- HashBox: Hash Hierarchical Segmentation exploiting Bounding Box Object Detection (2017) (0)
- Dive into Deep Learning for Natural Language Processing (2019) (0)
- Invariant Feature Extra tion andClassi ation in Kernel Spa (2000) (0)
- Database Management Systems for Non-Volatile Memory (2015) (0)
- Segmentation of Instances by Hashing (2017) (0)
- RI:Small:Collaborative Research: Distributed Inference Algorithms for Machine Learning (2012) (0)
- Deep Graphs (2018) (0)
- United States Patent ( 10 ) Patent No . : US 8 , 160 , 668 B 2 Pav ( 45 ) Date of Patent : Apr . 17 , 2012 ( 54 ) PATHOLOGICAL CONDITION DETECTOR 3 (2017) (0)
- Support-Vektor-Methoden zur Analyse hochdimensionaler Daten (1999) (0)
- Kernel-based measures of dependence in the Macaque visual cortex (2005) (0)
- Ëôôö×× Ööööý Ååøööü Ôôöóüüññøøóò Óö Åå Blockinòò Äääöòòòò (0)
- Advances in Large Margin Classiiers Library of Congress Cataloging-in-publication Data Advances in Large Margin Classiiers / Edited Includes Bibliographical References and Index. Isbn 0-xxx-xxxxx-x (alk. Paper) 1. Machine Learning. 2. Algorithms. 3. Kernel Functions (1999) (0)
- Advances in Large Margin Classiiers Library of Congress Cataloging-in-publication Data Advances in Large Margin Classiiers / Edited Includes Bibliographical References and Index. Isbn 0-xxx-xxxxx-x (alk. Paper) 1. Machine Learning. 2. Algorithms. 3. Kernel Functions (1999) (0)
- Ëôôö×× Ööööý Ååøööü Ôôöóüüññøøóò Óö Åå Blockinòò Äääöòòòò (0)
- Segmentation of Objects by Hashing (2017) (0)
- Właściwości i parametry pamięci kształtu biodegradowalnych mieszanek otrzymanych z kopolimeru L-laktyd/glikolid i poli(bursztynianu butylenu) (2014) (0)
- IRLI: Iterative Re-partitioning for Learning to Index (2021) (0)
- Kernel-based dependence detection in the Macaque visual cortex (2005) (0)
- Deep Q-Network with Proximal Iteration (2021) (0)
- Kernel Algorithms Distribution Embeddings in Reproducing Kernel Hilbert Spaces (2009) (0)
- Algorithms, Data, Hardware and Tools: A Perfect Storm (2018) (0)
- Integrating User Feedback under Identity Uncertainty in Knowledge Base Construction (2019) (0)
- Effect of depth of occurrence in contacts on characteristics of p-AlxGa1−xAs-p-GaAs-n-GaAs heterostructures (1991) (0)
- Biodegradowalny polimerowy system uwalniania radiouczulaczabadania in vitro (2010) (0)
- WWW 2011 invited tutorial overview: latent variable models on the internet (2011) (0)
- Logic, Trees and Kernels (2003) (0)
- KDD tutorial: The Dataminer Guide to Scalable Mixed-Membership and Nonparametric Bayesian Models (2013) (0)
- [Anastomosis using the Valtrac ring--pro and con]. (2000) (0)
- Regioned Episodic Reinforcement Learning (2021) (0)
- Accepted for NIPS ’ 99 SV Estimation of a Distribution ’ s Support (1999) (0)
- Improving Maximum Margin Matrix Factorization (best machine learning paper award) (2008) (0)
- Kernel Conditional Graphical Models (2007) (0)
- Structured Prediction Using Probabilistic Models (2007) (0)
- The Entropy Regularisation Information Criterion (2000) (0)
- The dataminer's guide to scalable mixed-membership and nonparametric bayesian models (2013) (0)
- Zmiany struktury i wybranych właściwości terpolimerów z pamięcią kształtu wywołanych w procesie otrzymywania implantów metodą wtrysku oraz ich sterylizacji (2010) (0)
- Modeling Structure via Graphical Models (2007) (0)
- Kernel Extrapolations for Enzyme Classification (2004) (0)
- Synthesis and properties of bioresorbable and highly flexible 1,3-trimethylene carbonate/ε-caprolactone copolymers (2012) (0)
- Instance Hash Segmentation (2017) (0)
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