Nando De Freitas
#129,944
Most Influential Person Now
Nando De Freitas's AcademicInfluence.com Rankings
Nando De Freitascomputer-science Degrees
Computer Science
#5661
World Rank
#5979
Historical Rank
Algorithms
#197
World Rank
#200
Historical Rank
Machine Learning
#1584
World Rank
#1606
Historical Rank
Artificial Intelligence
#1818
World Rank
#1850
Historical Rank
Download Badge
Computer Science
Nando De Freitas's Degrees
- PhD Computer Science University of British Columbia
Similar Degrees You Can Earn
Why Is Nando De Freitas Influential?
(Suggest an Edit or Addition)Nando De Freitas'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
- Sequential Monte Carlo Methods in Practice (2001) (3667)
- Taking the Human Out of the Loop: A Review of Bayesian Optimization (2016) (2990)
- Dueling Network Architectures for Deep Reinforcement Learning (2015) (2624)
- An Introduction to MCMC for Machine Learning (2004) (2502)
- A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010) (2022)
- Matching Words and Pictures (2003) (1797)
- The Unscented Particle Filter (2000) (1757)
- Learning to learn by gradient descent by gradient descent (2016) (1599)
- Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks (2000) (1438)
- A Boosted Particle Filter: Multitarget Detection and Tracking (2004) (1231)
- An Introduction to Sequential Monte Carlo Methods (2001) (1217)
- Predicting Parameters in Deep Learning (2013) (1153)
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning (2016) (1144)
- Sample Efficient Actor-Critic with Experience Replay (2016) (645)
- Sequential Monte Carlo in Practice (2001) (335)
- Neural Programmer-Interpreters (2015) (325)
- A Statistical Model for General Contextual Object Recognition (2004) (320)
- Bayesian Optimization in a Billion Dimensions via Random Embeddings (2013) (311)
- Bayesian Optimization in High Dimensions via Random Embeddings (2013) (306)
- LipNet: End-to-End Sentence-level Lipreading (2016) (290)
- From Group to Individual Labels Using Deep Features (2015) (276)
- Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning (2018) (269)
- Reinforcement and Imitation Learning for Diverse Visuomotor Skills (2018) (253)
- Deep Fried Convnets (2014) (246)
- Learning to Learn without Gradient Descent by Gradient Descent (2016) (243)
- Portfolio Allocation for Bayesian Optimization (2010) (240)
- A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot (2009) (237)
- Robust Visual Tracking for Multiple Targets (2006) (234)
- Playing hard exploration games by watching YouTube (2018) (223)
- Learned Optimizers that Scale and Generalize (2017) (219)
- Narrowing the Gap: Random Forests In Theory and In Practice (2013) (206)
- Learning Where to Attend with Deep Architectures for Image Tracking (2011) (197)
- A Generalist Agent (2022) (194)
- Fast particle smoothing: if I had a million particles (2006) (188)
- Parallel Multiscale Autoregressive Density Estimation (2017) (183)
- Robust Imitation of Diverse Behaviors (2017) (174)
- Inductive Principles for Restricted Boltzmann Machine Learning (2010) (169)
- Active Policy Learning for Robot Planning and Exploration under Uncertainty (2007) (168)
- Rao-Blackwellised particle filtering for fault diagnosis (2002) (158)
- Active Preference Learning with Discrete Choice Data (2007) (157)
- Hyperbolic Attention Networks (2018) (150)
- Critic Regularized Regression (2020) (150)
- A Bayesian interactive optimization approach to procedural animation design (2010) (148)
- Toward Practical N2 Monte Carlo: the Marginal Particle Filter (2005) (137)
- Acme: A Research Framework for Distributed Reinforcement Learning (2020) (135)
- LipNet: Sentence-level Lipreading (2016) (134)
- Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks (2016) (133)
- Diagnosis by a waiter and a Mars explorer (2004) (118)
- Sample Efficient Adaptive Text-to-Speech (2018) (108)
- Variational MCMC (2001) (106)
- On correlation and budget constraints in model-based bandit optimization with application to automatic machine learning (2014) (105)
- Exponential Regret Bounds for Gaussian Process Bandits with Deterministic Observations (2012) (104)
- Hyperparameter Selection for Offline Reinforcement Learning (2020) (102)
- Learning about Individuals from Group Statistics (2005) (102)
- Extraction of Salient Sentences from Labelled Documents (2014) (100)
- Large-Scale Visual Speech Recognition (2018) (98)
- Modelling, Visualising and Summarising Documents with a Single Convolutional Neural Network (2014) (98)
- Robust Full Bayesian Learning for Radial Basis Networks (2001) (95)
- Scaling data-driven robotics with reward sketching and batch reinforcement learning (2019) (93)
- On Autoencoders and Score Matching for Energy Based Models (2011) (91)
- Few-shot Autoregressive Density Estimation: Towards Learning to Learn Distributions (2017) (88)
- Bayesian Multi-Scale Optimistic Optimization (2014) (86)
- Consistency of Online Random Forests (2013) (85)
- ACDC: A Structured Efficient Linear Layer (2015) (83)
- From Fields to Trees (2004) (81)
- Bayesian Optimization in AlphaGo (2018) (80)
- A tutorial on stochastic approximation algorithms for training Restricted Boltzmann Machines and Deep Belief Nets (2010) (78)
- Learning to Perform Physics Experiments via Deep Reinforcement Learning (2016) (78)
- Theoretical Analysis of Bayesian Optimisation with Unknown Gaussian Process Hyper-Parameters (2014) (73)
- Adaptive Hamiltonian and Riemann Manifold Monte Carlo (2013) (71)
- Adaptive MCMC with Bayesian Optimization (2012) (71)
- Real-Time Monitoring of Complex Industrial Processes with Particle Filters (2002) (69)
- Learning to Recognize Objects with Little Supervision (2008) (63)
- Compositional Obverter Communication Learning From Raw Visual Input (2018) (59)
- Meta-learning of Sequential Strategies (2019) (59)
- Generating Interpretable Images with Controllable Structure (2017) (58)
- Making Efficient Use of Demonstrations to Solve Hard Exploration Problems (2019) (56)
- Learning attentional policies for tracking and recognition in video with deep networks (2011) (56)
- RL Unplugged: Benchmarks for Offline Reinforcement Learning (2020) (56)
- RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning (2020) (55)
- Adaptive Hamiltonian and Riemann manifold Monte Carlo samplers (2013) (55)
- Cortical microcircuits as gated-recurrent neural networks (2017) (55)
- A Deep Architecture for Semantic Parsing (2014) (54)
- An Entropy Search Portfolio for Bayesian Optimization (2014) (53)
- Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM (2007) (53)
- Intrinsic Social Motivation via Causal Influence in Multi-Agent RL (2018) (52)
- Unbounded Bayesian Optimization via Regularization (2015) (52)
- Fast Krylov Methods for N-Body Learning (2005) (46)
- Reversible Jump MCMC Simulated Annealing for Neural Networks (2000) (44)
- Sequential Monte Carlo Methods for Neural Networks (2001) (42)
- Heteroscedastic Treed Bayesian Optimisation (2014) (41)
- Target-directed attention: Sequential decision-making for gaze planning (2008) (39)
- Nonparametric Bayesian Logic (2005) (39)
- Bayesian Feature Weighting for Unsupervised Learning, with Application to Object Recognition (2003) (37)
- "Name That Song!" A Probabilistic Approach to Querying on Music and Text (2002) (37)
- Learning Awareness Models (2018) (37)
- Learning to Learn for Global Optimization of Black Box Functions (2016) (36)
- New inference strategies for solving Markov Decision Processes using reversible jump MCMC (2009) (36)
- Task-Relevant Adversarial Imitation Learning (2019) (35)
- An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward (2009) (33)
- Bayesian Policy Learning with Trans-Dimensional MCMC (2007) (33)
- A Machine Learning Perspective on Predictive Coding with PAQ8 (2011) (33)
- Offline Learning from Demonstrations and Unlabeled Experience (2020) (32)
- Learning Compositional Neural Programs with Recursive Tree Search and Planning (2019) (32)
- The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneously (2017) (32)
- Rao-Blackwellised Particle Filtering via Data Augmentation (2001) (30)
- Learning Deep Features in Instrumental Variable Regression (2020) (30)
- Building machines that learn and think for themselves (2017) (29)
- Modular Meta-Learning with Shrinkage (2019) (29)
- A Constrained Semi-supervised Learning Approach to Data Association (2004) (29)
- Empirical Testing of Fast Kernel Density Estimation Algorithms (2005) (29)
- Hot Coupling: A Particle Approach to Inference and Normalization on Pairwise Undirected Graphs (2005) (29)
- Shaking the foundations: delusions in sequence models for interaction and control (2021) (26)
- Self-Avoiding Random Dynamics on Integer Complex Systems (2011) (24)
- Fast Computational Methods for Visually Guided Robots (2005) (24)
- One-Shot High-Fidelity Imitation: Training Large-Scale Deep Nets with RL (2018) (23)
- Deep Reinforcement Learning (2020) (23)
- Linear and Parallel Learning of Markov Random Fields (2013) (22)
- The Sound of an Album Cover: Probabilistic Multimedia and IR (2002) (21)
- A Framework for Data-Driven Robotics (2019) (21)
- Asymptotic Efficiency of Deterministic Estimators for Discrete Energy-Based Models: Ratio Matching and Pseudolikelihood (2011) (20)
- A Semi-supervised Learning Approach to Object Recognition with Spatial Integration of Local Features and Segmentation Cues (2006) (20)
- Fast maximum a-posteriori inference on Monte Carlo state spaces (2005) (19)
- Regularized Behavior Value Estimation (2021) (19)
- Toward the Implementation of a Quantum RBM (2011) (18)
- Bayesian Analysis of Continuous Time Markov Chains with Application to Phylogenetic Modelling (2016) (18)
- Deep Multi-Instance Transfer Learning (2014) (18)
- Herded Gibbs Sampling (2013) (17)
- Trans-dimensional MCMC for Bayesian policy learning (2007) (16)
- An interior-point stochastic approximation method and an L1-regularized delta rule (2008) (16)
- Deep Apprenticeship Learning for Playing Video Games (2015) (16)
- Recklessly Approximate Sparse Coding (2012) (16)
- Bayesian Latent Semantic Analysis of Multimedia Databases (2001) (16)
- Object Recognition as Machine Translation – Part 2: Exploiting Image Database Clustering Models (2001) (15)
- On Sparse, Spectral and Other Parameterizations of Binary Probabilistic Models (2012) (15)
- Conditional mean field (2006) (15)
- Beat Tracking the Graphical Model Way (2004) (14)
- Programmable Agents (2017) (14)
- RL Unplugged: A Collection of Benchmarks for Offline Reinforcement Learning (2020) (14)
- Exploiting correlation and budget constraints in Bayesian multi-armed bandit optimization (2013) (13)
- Semi-supervised reward learning for offline reinforcement learning (2020) (13)
- Bayesian Models for Massive Multimedia Databases: a New Frontier (2002) (13)
- Towards Learning Universal Hyperparameter Optimizers with Transformers (2022) (12)
- 1 Matching Words and Pictures (2003) (12)
- Predictive Adaptation of Hybrid Monte Carlo with Bayesian Parametric Bandits (2011) (12)
- Regret Bounds for Deterministic Gaussian Process Bandits (2012) (11)
- Preference galleries for material design (2007) (10)
- On Instrumental Variable Regression for Deep Offline Policy Evaluation (2021) (9)
- Large-scale multilingual audio visual dubbing (2020) (9)
- Active Offline Policy Selection (2021) (9)
- Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults (2012) (8)
- A Machine Learning Approach to Pattern Detection and Prediction for Environmental Monitoring and Water Sustainability (2011) (7)
- The Sound of an Album Cover: Probabilistic Multimedia and Information Retrieval (2003) (7)
- Intracluster Moves for Constrained Discrete-Space MCMC (2010) (6)
- Distributed Parameter Estimation in Probabilistic Graphical Models (2014) (6)
- Inference Strategies for Solving Semi−Markov Decision Processes (2012) (6)
- The Sound of an Album Cover: A Probabilistic Approach to Multimedia (2003) (5)
- Sequential Monte Carlo for model selection and estimation of neural networks (2000) (5)
- Hedging Strategies for Bayesian Optimization (2010) (4)
- Information Theory Tools to Rank MCMC Algorithms on Probabilistic Graphical Models (2006) (4)
- Classification Tree (2017) (4)
- UvA-DARE (Digital Academic Repository) Generalized belief propagation on tree robust structured region graphs Generalized Belief Propagation on Tree Robust Structured Region Graphs (2012) (4)
- Large-Flip Importance Sampling (2007) (4)
- Inference and Learning for Active Sensing, Experimental Design and Control (2009) (4)
- Empirically Evaluating Multiagent Reinforcement Learning Algorithms (2005) (3)
- Learning Compositional Neural Programs for Continuous Control (2020) (3)
- Bayesian optimization for adaptive MCMC (2011) (3)
- Owed to a Martingale: A Fast Bayesian On-Line EM Algorithm for Multinomial Models (2004) (3)
- Decentralized, Adaptive, Look-Ahead Particle Filtering (2012) (3)
- Sparsity priors and boosting for learning localized distributed feature representations (2010) (3)
- Insights on Fast Kernel Density Estimation Algorithms (2004) (3)
- Extraction of morphological QRS-based biomarkers in hypertrophic cardiomyopathy for risk stratification using L1 regularized logistic regression (2015) (3)
- Best arm identification via Bayesian gap-based exploration (2013) (2)
- Vision-Language Models as Success Detectors (2023) (2)
- Dynamic modelling and control of industrial processes with particle filtering algorithms (2004) (2)
- Bayesian Optimization with an Empirical Hardness Model for approximate Nearest Neighbour Search (2014) (2)
- Learning to Learn and Compositionality with Deep Recurrent Neural Networks: Learning to Learn and Compositionality (2016) (2)
- Efficient Learning of Practical Markov Random Fields with Exact Inference (2013) (2)
- A Blessing of Dimensionality: Measure Concentration and Probabilistic Inference (2003) (2)
- N−Body Games (2005) (1)
- Statistics Technical Report # 231 Bayesian Variable Selection for Semi-Supervised Learning , with Application to Object Recognition (2007) (1)
- Sequential Inference and Learning (1998) (1)
- Where do priors and causal models come from ? An experimental design perspective (2010) (1)
- UvA-DARE (Digital Academic Repository) Herded Gibbs Sampling Herded Gibbs Sampling (2013) (0)
- Recognized Maritime Picture: Geofeasibility Scores (2007) (0)
- Adaptive MCMC for high dimensional and high complexity problems (2012) (0)
- ADVANCES IN SCALABLE BAYESIAN COMPUTATION (2014) (0)
- Multi-step Planning for Automated Hyperparameter Optimization with OptFormer (2022) (0)
- SOLVE HARD EXPLORATION PROBLEMS (2020) (0)
- Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (2012) (2013) (0)
- EARNING A WARENESS M ODELS (2018) (0)
This paper list is powered by the following services:
