Barnabás Póczos

Barnabás Póczos's AcademicInfluence.com Rankings

Barnabás Póczos

Computer Science

#9032

World Rank

#9491

Historical Rank

Algorithms

#362

World Rank

#367

Historical Rank

Machine Learning

#3856

World Rank

#3902

Historical Rank

Database

#6027

World Rank

#6250

Historical Rank

computer-science Degrees

Download Badge

Computer Science

Barnabás Póczos's Degrees

PhD Machine Learning Carnegie Mellon University
Masters Computer Science Eötvös Loránd University
Bachelors Computer Science Eötvös Loránd University

Similar Degrees You Can Earn

Why Is Barnabás Póczos Influential?

(Suggest an Edit or Addition)

(See a Problem?)

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)

This paper list is powered by the following services:

What Schools Are Affiliated With Barnabás Póczos?

Barnabás Póczos is affiliated with the following schools:

Carnegie Mellon University

Barnabás Póczos's Academic­Influence.com Rankings

Barnabás Póczos's Degrees

Similar Degrees You Can Earn

Why Is Barnabás Póczos Influential?

Barnabás Póczos's Published Works

Published Works

What Schools Are Affiliated With Barnabás Póczos?

Barnabás Póczos's AcademicInfluence.com Rankings