Barnabás Póczos
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Barnabás Póczoscomputer-science Degrees
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Computer Science
Barnabás Póczos's Degrees
- PhD Machine Learning Carnegie Mellon University
- Masters Computer Science Eötvös Loránd University
- Bachelors Computer Science Eötvös Loránd University
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Why Is Barnabás Póczos Influential?
(Suggest an Edit or Addition)Barnabás Póczos'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
- Deep Sets (2017) (1434)
- Gradient Descent Provably Optimizes Over-parameterized Neural Networks (2018) (963)
- MMD GAN: Towards Deeper Understanding of Moment Matching Network (2017) (576)
- Stochastic Variance Reduction for Nonconvex Optimization (2016) (510)
- Neural Architecture Search with Bayesian Optimisation and Optimal Transport (2018) (449)
- One Network to Solve Them All — Solving Linear Inverse Problems Using Deep Projection Models (2017) (308)
- High Dimensional Bayesian Optimisation and Bandits via Additive Models (2015) (275)
- Characterizing and Avoiding Negative Transfer (2018) (238)
- Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima (2017) (218)
- Gradient Descent Can Take Exponential Time to Escape Saddle Points (2017) (212)
- On Variance Reduction in Stochastic Gradient Descent and its Asynchronous Variants (2015) (191)
- Competence-based Curriculum Learning for Neural Machine Translation (2019) (190)
- Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities (2018) (177)
- Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels (2019) (172)
- Parallelised Bayesian Optimisation via Thompson Sampling (2018) (166)
- Multi-fidelity Bayesian Optimisation with Continuous Approximations (2017) (162)
- Deep Learning with Sets and Point Clouds (2016) (158)
- Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization (2016) (158)
- Equivariance Through Parameter-Sharing (2017) (155)
- Estimation of Renyi Entropy and Mutual Information Based on Generalized Nearest-Neighbor Graphs (2010) (153)
- On the Decreasing Power of Kernel and Distance Based Nonparametric Hypothesis Tests in High Dimensions (2014) (143)
- Learning to predict the cosmological structure formation (2018) (136)
- AIDE: Fast and Communication Efficient Distributed Optimization (2016) (132)
- Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly (2019) (124)
- Point Cloud GAN (2018) (121)
- Stochastic Frank-Wolfe methods for nonconvex optimization (2016) (120)
- Group Anomaly Detection using Flexible Genre Models (2011) (110)
- Gaussian Process Bandit Optimisation with Multi-fidelity Evaluations (2016) (110)
- Learning Theory for Distribution Regression (2014) (108)
- Nonparametric Divergence Estimation with Applications to Machine Learning on Distributions (2011) (107)
- Nonparametric von Mises Estimators for Entropies, Divergences and Mutual Informations (2015) (104)
- Predicting enhancer-promoter interaction from genomic sequence with deep neural networks (2016) (103)
- CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding (2017) (103)
- Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints (2016) (101)
- Politeness Transfer: A Tag and Generate Approach (2020) (97)
- Hierarchical Probabilistic Models for Group Anomaly Detection (2011) (90)
- Variance Reduction in Stochastic Gradient Langevin Dynamics (2016) (89)
- Nonparametric Estimation of Renyi Divergence and Friends (2014) (88)
- Copula-based Kernel Dependency Measures (2012) (83)
- A Generic Approach for Escaping Saddle points (2017) (81)
- Distribution to Distribution Regression (2013) (81)
- Distribution-Free Distribution Regression (2013) (80)
- On the Estimation of alpha-Divergences (2011) (79)
- A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations (2018) (77)
- Two-stage sampled learning theory on distributions (2014) (74)
- ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations (2019) (73)
- Autonomous discovery of battery electrolytes with robotic experimentation and machine-learning (2019) (72)
- Transformation Autoregressive Networks (2018) (72)
- Classifier Two Sample Test for Video Anomaly Detections (2018) (70)
- Learning Local Search Heuristics for Boolean Satisfiability (2019) (69)
- High Dimensional Bayesian Optimization via Restricted Projection Pursuit Models (2016) (68)
- Fast incremental method for smooth nonconvex optimization (2016) (64)
- Nonparametric kernel estimators for image classification (2012) (63)
- Estimating Cosmological Parameters from the Dark Matter Distribution (2016) (63)
- Bayesian Nonparametric Kernel-Learning (2015) (60)
- Minimax Distribution Estimation in Wasserstein Distance (2018) (59)
- A MACHINE LEARNING APPROACH FOR DYNAMICAL MASS MEASUREMENTS OF GALAXY CLUSTERS (2014) (59)
- Enabling Dark Energy Science with Deep Generative Models of Galaxy Images (2016) (59)
- Undercomplete Blind Subspace Deconvolution (2007) (58)
- Doubly Robust Covariate Shift Correction (2015) (57)
- Online group-structured dictionary learning (2011) (54)
- Generalized Exponential Concentration Inequality for Renyi Divergence Estimation (2014) (54)
- Multi-fidelity Gaussian Process Bandit Optimisation (2016) (53)
- Fast Stochastic Methods for Nonsmooth Nonconvex Optimization (2016) (51)
- A Deep Reinforcement Learning Approach for Global Routing (2019) (49)
- Seq2Seq2Sentiment: Multimodal Sequence to Sequence Models for Sentiment Analysis (2018) (48)
- Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators (2016) (48)
- Exponential Concentration of a Density Functional Estimator (2014) (47)
- DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING (2015) (46)
- Hypothesis Transfer Learning via Transformation Functions (2016) (46)
- Bayesian active learning for posterior estimation (2015) (44)
- Fast Incremental Method for Nonconvex Optimization (2016) (43)
- Fast Distribution To Real Regression (2013) (41)
- Active learning and search on low-rank matrices (2013) (41)
- Cautious Deep Learning (2018) (40)
- A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters (2019) (40)
- Boolean Matrix Factorization and Noisy Completion via Message Passing (2015) (40)
- Nonparametric Estimation of Conditional Information and Divergences (2012) (39)
- Kernel Change-point Detection with Auxiliary Deep Generative Models (2018) (38)
- Separation theorem for independent subspace analysis and its consequences (2012) (38)
- Nonparametric Density Estimation under Adversarial Losses (2018) (38)
- Adaptivity and Computation-Statistics Tradeoffs for Kernel and Distance based High Dimensional Two Sample Testing (2015) (38)
- The Statistical Recurrent Unit (2017) (37)
- Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses (2019) (37)
- Independent subspace analysis using geodesic spanning trees (2005) (35)
- The Role of Machine Learning in the Next Decade of Cosmology (2019) (34)
- End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC (2018) (33)
- Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data (2015) (33)
- Analysis of k-Nearest Neighbor Distances with Application to Entropy Estimation (2016) (32)
- Cross-Entropy Optimization for Independent Process Analysis (2006) (32)
- Implicit Kernel Learning (2019) (32)
- End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data (2019) (31)
- High-Throughput Robotic Phenotyping of Energy Sorghum Crops (2017) (30)
- Communication Efficient Coresets for Empirical Loss Minimization (2015) (30)
- LBS Autoencoder: Self-Supervised Fitting of Articulated Meshes to Point Clouds (2019) (30)
- On the Estimation of α-Divergences (2011) (29)
- Minimizing FLOPs to Learn Efficient Sparse Representations (2020) (28)
- Asynchronous Parallel Bayesian Optimisation via Thompson Sampling (2017) (27)
- ICA and ISA using Schweizer-Wolff measure of dependence (2008) (27)
- On the High Dimensional Power of a Linear-Time Two Sample Test under Mean-shift Alternatives (2015) (27)
- Independent Subspace Analysis Using k-Nearest Neighborhood Distances (2005) (27)
- Query efficient posterior estimation in scientific experiments via Bayesian active learning (2017) (26)
- Data-driven Random Fourier Features using Stein Effect (2017) (26)
- Hierarchical Machine Learning for High-Fidelity 3D Printed Biopolymers. (2020) (25)
- Fast Function to Function Regression (2014) (25)
- The Multi-fidelity Multi-armed Bandit (2016) (24)
- Contextual Parameter Generation for Knowledge Graph Link Prediction (2020) (23)
- REGO: Rank-based Estimation of Renyi Information using Euclidean Graph Optimization (2010) (23)
- Elucidating multi-physics interactions in suspensions for the design of polymeric dispersants: a hierarchical machine learning approach (2017) (23)
- Learning when to stop thinking and do something! (2009) (22)
- A FIRST LOOK AT CREATING MOCK CATALOGS WITH MACHINE LEARNING TECHNIQUES (2013) (21)
- Efficient Meta Lifelong-Learning with Limited Memory (2020) (21)
- Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments (2019) (20)
- A Flexible Multi-Objective Bayesian Optimization Approach using Random Scalarizations (2018) (20)
- ProBO: a Framework for Using Probabilistic Programming in Bayesian Optimization (2019) (19)
- Independent Subspace Analysis on Innovations (2005) (19)
- Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques (2009) (18)
- Nonparanormal Information Estimation (2017) (18)
- Neural Kalman filter (2005) (18)
- Support Distribution Machines (2012) (18)
- Stochastic Neural Networks with Monotonic Activation Functions (2016) (18)
- End-to-End Physics Event Classification with the CMS Open Data: Applying Image-based Deep Learning on Detector Data to Directly Classify Collision Events at the LHC (2018) (17)
- Scale Invariant Conditional Dependence Measures (2013) (17)
- Undercomplete Blind Subspace Deconvolution Via Linear Prediction (2007) (16)
- Influence Functions for Machine Learning: Nonparametric Estimators for Entropies, Divergences and Mutual Informations (2014) (16)
- Linear-Time Learning on Distributions with Approximate Kernel Embeddings (2015) (16)
- Independent Process Analysis Without a Priori Dimensional Information (2007) (15)
- Nonparametric Density Estimation with Adversarial Losses (2018) (14)
- Deep generative models for galaxy image simulations (2020) (14)
- Gaussian Process Optimisation with Multi-fidelity Evaluations (2017) (14)
- Molecular Engineering of Superplasticizers for Metakaolin‐Portland Cement Blends with Hierarchical Machine Learning (2018) (13)
- Ockham's Razor at Work: Modeling of the ``Homunculus'' (2002) (13)
- Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector (2018) (12)
- BrainZoom: High Resolution Reconstruction from Multi-modal Brain Signals (2017) (11)
- Non-combinatorial estimation of independent autoregressive sources (2006) (11)
- Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets (2020) (10)
- End-to-End Event Classification of High-Energy Physics Data (2018) (10)
- Anomaly Detection for Astronomical Data (2010) (10)
- Near-Orthogonality Regularization in Kernel Methods (2017) (10)
- FuSSO: Functional Shrinkage and Selection Operator (2013) (10)
- Auto-regressive independent process analysis without combinatorial efforts (2010) (9)
- Collaborative Filtering via Group-Structured Dictionary Learning (2012) (9)
- On Estimating L22 Divergence (2015) (9)
- Minimax Reconstruction Risk of Convolutional Sparse Dictionary Learning (2018) (9)
- On the High-dimensional Power of Linear-time Kernel Two-Sample Testing under Mean-difference Alternatives (2014) (8)
- Theorem for K-Independent Subspace Analysis with Sufficient Conditions (2006) (8)
- Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction (2020) (8)
- ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language (2019) (8)
- Unsupervised Program Synthesis for Images using Tree-Structured LSTM (2020) (7)
- On Estimating $L_2^2$ Divergence (2014) (7)
- Post Nonlinear Independent Subspace Analysis (2007) (7)
- Deep Mean Maps (2015) (7)
- Hidden Markov model finds behavioral patterns of users working with a headmouse driven writing tool (2004) (7)
- An Analysis of Active Learning with Uniform Feature Noise (2014) (7)
- Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM (2016) (6)
- Utilize Old Coordinates: Faster Doubly Stochastic Gradients for Kernel Methods (2016) (6)
- Machine learning approaches to admixture design for clay-based cements (2018) (6)
- Efficient Learning on Point Sets (2013) (6)
- k-NN Regression on Functional Data with Incomplete Observations (2014) (6)
- Nonparametric Density Estimation under Besov IPM Losses (2019) (6)
- Nonparametric Risk and Stability Analysis for Multi-Task Learning Problems (2016) (6)
- Minimax Estimation of Quadratic Fourier Functionals (2018) (5)
- Optimal Exact Matrix Completion Under new Parametrization (2020) (5)
- Cheminformatics for accelerated design of chemical admixtures (2020) (5)
- Robust Handwriting Recognition with Limited and Noisy Data (2020) (5)
- D-optimal Bayesian Interrogation for Parameter and Noise Identification of Recurrent Neural Networks (2008) (5)
- Exploring End-to-end Deep Learning Applications for Event Classification at CMS (2019) (5)
- Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming (2018) (5)
- Nonparametric distribution regression applied to sensor modeling (2016) (5)
- Kernel MMD, the Median Heuristic and Distance Correlation in High Dimensions (2014) (5)
- Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent (2018) (5)
- Differentiable Unrolled Alternating Direction Method of Multipliers for OneNet (2019) (5)
- Robust Plant Phenotyping via Model-Based Optimization (2018) (4)
- Efficient Nonparametric Smoothness Estimation (2016) (4)
- Conditional Distance Variance and Correlation (2012) (4)
- Budgeted Distribution Learning of Belief Net Parameters (2010) (4)
- Nonparametric Divergence Estimation and its Applications to Machine Learning (2011) (4)
- Optimal Adaptive Matrix Completion (2020) (4)
- Nonparametric divergence estimators for independent subspace analysis (2011) (4)
- Generative Adversarial Image Refinement for Handwriting Recognition (2017) (4)
- Consistent, Two-Stage Sampled Distribution Regression via Mean Embedding (2014) (3)
- On Estimating L 22 Divergence (2014) (3)
- End-to-end particle and event identification at the Large Hadron Collider with CMS Open Data (2019) (3)
- Learning on Distributions (2014) (3)
- Understanding the relationship between Functional and Structural Connectivity of Brain Networks (2015) (3)
- Machine learning-aided modeling of fixed income instruments (2018) (3)
- Developing Creative AI to Generate Sculptural Objects (2019) (3)
- Non-negative matrix factorization extended by sparse code shrinkage and by weight sparsification (2002) (3)
- Local Connectome Fingerprinting Reveals the Uniqueness of Individual White Matter Architecture (2016) (3)
- Kalman-filtering using local interactions (2003) (3)
- On the Reconstruction Risk of Convolutional Sparse Dictionary Learning (2017) (2)
- Recurrent Estimation of Distributions (2017) (2)
- Group k-Sparse Temporal Convolutional Neural Networks: Unsupervised Pretraining for Video Classification (2019) (2)
- Nonparametric independent process analysis (2011) (2)
- Separation Theorem for Independent Subspace Analysis (2005) (2)
- Bayesian Active Learning for Posterior Estimation - IJCAI-15 Distinguished Paper (2015) (2)
- Non-negative matrix factorization extended by sparse code shrinkage and weight sparsification non-negative matrix factorization algorithms (2002) (2)
- Separation Theorem for Independent Subspace Analysis with Sufficient Conditions (2006) (2)
- Nonlinear ISA with Auxiliary Variables for Learning Speech Representations (2020) (1)
- Unsupervised program synthesis for images by sampling without replacement (2020) (1)
- Robust Density Estimation under Besov IPM Losses (2020) (1)
- Connoisseur : Can GANs Learn Simple 1 D Parametric Distributions ? (2018) (1)
- Improving Molecule Properties Through 2-Stage VAE (2022) (1)
- Learning Theory for Vector-Valued Distribution Regression (2015) (1)
- Machine Learning Algorithms for Matching Theories, Simulations, and Observations in Cosmology (2018) (1)
- RotationOut as a Regularization Method for Neural Network (2019) (1)
- Online Dictionary Learning with Group Structure Inducing Norms (2011) (1)
- Cost-Aware Bayesian Optimization via Information Directed Sampling (2020) (1)
- Learning Robust Joint Representations for Multimodal Sentiment Analysis (2018) (1)
- Robust Nonparametric Copula Based Dependence Estimators (2011) (1)
- A Cross-Entropy Method that Optimizes Partially Decomposable Problems: A New Way to Interpret NMR Spectra (2010) (1)
- Hallucinating Point Cloud into 3D Sculptural Object (2018) (1)
- CDS&E: Collaborative Research: Machine Learning for Automated Discovery and Control in Turbulent Plasma (2015) (0)
- Distribution Regression - the Set Kernel Heuristic is Consistent (2014) (0)
- 0-701 Machine Learning: Assignment 2 Q1) Support Vector Machines (pulkit) Svm [20 Points] (2014) (0)
- VideoOneNet: Bidirectional Convolutional Recurrent OneNet with Trainable Data Steps for Video Processing (2020) (0)
- IARY DEEP GENERATIVE MODELS (2019) (0)
- 10-701 Machine Learning : Assignment 3 Due on April 1 st , 2014 at 11 : 59 am (2014) (0)
- Distribution Learning of Belief Net Parameters (2015) (0)
- Deep Generative Models of Galaxy Images for the Calibration of the Next Generation of Weak Lensing Surveys (2017) (0)
- 12-2011 Robust Nonparametric Copula Based Dependence Estimators (2015) (0)
- Minimax Rates of Distribution Estimationin Wasserstein Distance (2019) (0)
- Non-negative matrix factorization extended by sparse code shrinkage and by weight sparsi £ cation (2003) (0)
- Competitive spiking and indirect entropy minimization of rate code: Efficient search for hidden components (2004) (0)
- Autonomous Electrolyte Discovery for Batteries with Experimentally Informed Bayesian Optimization (2020) (0)
- Target Seq 2 Seq Forward Translation Forward Translation Backward Translation Backward Translation Sentiment Sentiment Prediction 1 23 4 5 Encoder RNN Decoder RNN Embedded Representation Prediction RNN (2018) (0)
- 2 Online update equations for the minimum point of f̂ t (2011) (0)
- Vector-valued Distribution Regression - Keep It Simple and Consistent (2015) (0)
- Consistent Vector-valued Regression on Probability Measures (2015) (0)
- Proposal Estimating Probability Distributions and their Properties (2018) (0)
- Simple consistent distribution regression on compact metric domains (2014) (0)
- A Simple and Consistent Technique for Vector-valued Distribution Regression (2015) (0)
- Distribution Regression - Make It Simple and Consistent (2015) (0)
- Consistent Distribution Regression via Mean Embedding (2014) (0)
- Multifidelity Experiment Selection and Optimization (2017) (0)
- Optimal Regression on Sets (2016) (0)
- Draft version November 7 , 2018 Preprint typeset using L A TEX style emulateapj v . 5 / 2 / 11 DYNAMICAL MASS MEASUREMENTS OF CONTAMINATED GALAXY CLUSTERS USING MACHINE LEARNING (2018) (0)
- Two-Stage Sampled Distribution Regression on Separable Topological Domains ∗ (2014) (0)
- Learned Interpolation for 3D Generation (2019) (0)
- Neural Adaptation of the Kalman-Gain (2004) (0)
- on CMS information server CMS CR-2018 / 379 The Compact Muon Solenoid Experiment (2018) (0)
- Transformation Function Based Methods for Model Shift (2016) (0)
- 10-701 Machine Learning : Assignment 1 Due on Februrary 20 , 2014 at 12 noon (2014) (0)
- Machine Learning to Recognize Phenomena in Large Scale Simulations (2011) (0)
- Covariate Distribution Aware Meta-learning (2020) (0)
- 10-701 Machine Learning : Assignment 2 Due on March 11 , 2014 at 11 : 59 (2014) (0)
- Multiset multicover methods for discriminative marker selection (2022) (0)
- Regression on Probability Measures: A Simple and Consistent Algorithm (2015) (0)
- Rates of Convergence of Nonparametric Estimators for Model Shift (2016) (0)
- Consistent Vector-valued Distribution Regression (2015) (0)
- Vector-valued distribution regression: a simple and consistent approach (2014) (0)
- M L ] 3 0 Ja n 20 18 Bayesian Nonparametric Kernel-Learning (2018) (0)
- Cost Component Analysis (2003) (0)
- Distribution Regression with Minimax-Optimal Guarantee (2016) (0)
- Machine Learning Department School of Computer Science 2012 Support Distribution Machines (2015) (0)
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