Gradient-Based Trajectory Optimization With Learned Dynamics.
|
ICRA |
2023 |
0 |
Hallucinated adversarial control for conservative offline policy evaluation.
|
UAI |
2023 |
0 |
A scalable Walsh-Hadamard regularizer to overcome the low-degree spectral bias of neural networks.
|
UAI |
2023 |
0 |
Lifelong bandit optimization: no prior and no regret.
|
UAI |
2023 |
0 |
Aligned Diffusion Schrödinger Bridges.
|
UAI |
2023 |
0 |
Tuning Legged Locomotion Controllers via Safe Bayesian Optimization.
|
CoRL |
2023 |
0 |
The Schrödinger Bridge between Gaussian Measures has a Closed Form.
|
AISTATS |
2023 |
0 |
Active Exploration via Experiment Design in Markov Chains.
|
AISTATS |
2023 |
0 |
BaCaDI: Bayesian Causal Discovery with Unknown Interventions.
|
AISTATS |
2023 |
0 |
Isotropic Gaussian Processes on Finite Spaces of Graphs.
|
AISTATS |
2023 |
0 |
Efficient Exploration in Continuous-time Model-based Reinforcement Learning.
|
NIPS/NeurIPS |
2023 |
0 |
A Dynamical System View of Langevin-Based Non-Convex Sampling.
|
NIPS/NeurIPS |
2023 |
0 |
Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning.
|
NIPS/NeurIPS |
2023 |
0 |
Stochastic Approximation Algorithms for Systems of Interacting Particles.
|
NIPS/NeurIPS |
2023 |
0 |
Optimistic Active Exploration of Dynamical Systems.
|
NIPS/NeurIPS |
2023 |
0 |
Implicit Manifold Gaussian Process Regression.
|
NIPS/NeurIPS |
2023 |
0 |
Learning To Dive In Branch And Bound.
|
NIPS/NeurIPS |
2023 |
0 |
Riemannian stochastic optimization methods avoid strict saddle points.
|
NIPS/NeurIPS |
2023 |
0 |
Anytime Model Selection in Linear Bandits.
|
NIPS/NeurIPS |
2023 |
0 |
Likelihood Ratio Confidence Sets for Sequential Decision Making.
|
NIPS/NeurIPS |
2023 |
0 |
Contextual Stochastic Bilevel Optimization.
|
NIPS/NeurIPS |
2023 |
0 |
Replicable Bandits.
|
ICLR |
2023 |
0 |
Model-based Causal Bayesian Optimization.
|
ICLR |
2023 |
0 |
Near-optimal Policy Identification in Active Reinforcement Learning.
|
ICLR |
2023 |
0 |
MARS: Meta-learning as Score Matching in the Function Space.
|
ICLR |
2023 |
0 |
Safe Risk-Averse Bayesian Optimization for Controller Tuning.
|
IEEE Robotics and Automation Letters |
2023 |
0 |
Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics.
|
MLJ |
2023 |
0 |
GoSafeOpt: Scalable safe exploration for global optimization of dynamical systems.
|
Artificial Intelligence |
2023 |
0 |
Adaptive Gaussian Process Change Point Detection.
|
ICML |
2022 |
0 |
Learning Long-Term Crop Management Strategies with CyclesGym.
|
NIPS/NeurIPS |
2022 |
0 |
Constrained Policy Optimization via Bayesian World Models.
|
ICLR |
2022 |
12 |
Meta-Learning Hypothesis Spaces for Sequential Decision-making.
|
ICML |
2022 |
3 |
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation.
|
ICML |
2022 |
0 |
The Dynamics of Riemannian Robbins-Monro Algorithms.
|
COLT |
2022 |
1 |
Learning to Cut by Looking Ahead: Cutting Plane Selection via Imitation Learning.
|
ICML |
2022 |
8 |
Interactively Learning Preference Constraints in Linear Bandits.
|
ICML |
2022 |
0 |
Meta-Learning Priors for Safe Bayesian Optimization.
|
CoRL |
2022 |
0 |
Neural Contextual Bandits without Regret.
|
AISTATS |
2022 |
0 |
Sensing Cox Processes via Posterior Sampling and Positive Bases.
|
AISTATS |
2022 |
0 |
Proximal Optimal Transport Modeling of Population Dynamics.
|
AISTATS |
2022 |
0 |
Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes.
|
AISTATS |
2022 |
0 |
Amortized Inference for Causal Structure Learning.
|
NIPS/NeurIPS |
2022 |
0 |
Near-Optimal Multi-Agent Learning for Safe Coverage Control.
|
NIPS/NeurIPS |
2022 |
0 |
Active Bayesian Causal Inference.
|
NIPS/NeurIPS |
2022 |
0 |
Movement Penalized Bayesian Optimization with Application to Wind Energy Systems.
|
NIPS/NeurIPS |
2022 |
0 |
Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces.
|
NIPS/NeurIPS |
2022 |
0 |
Supervised Training of Conditional Monge Maps.
|
NIPS/NeurIPS |
2022 |
0 |
Active Exploration for Inverse Reinforcement Learning.
|
NIPS/NeurIPS |
2022 |
0 |
Graph Neural Network Bandits.
|
NIPS/NeurIPS |
2022 |
0 |
A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits.
|
NIPS/NeurIPS |
2022 |
0 |
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning.
|
ICLR |
2022 |
0 |
Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking.
|
ICLR |
2022 |
0 |
Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning.
|
NIPS/NeurIPS |
2021 |
8 |
DiBS: Differentiable Bayesian Structure Learning.
|
NIPS/NeurIPS |
2021 |
30 |
PopSkipJump: Decision-Based Attack for Probabilistic Classifiers.
|
ICML |
2021 |
1 |
Cherry-Picking Gradients: Learning Low-Rank Embeddings of Visual Data via Differentiable Cross-Approximation.
|
ICCV |
2021 |
5 |
Bias-Robust Bayesian Optimization via Dueling Bandits.
|
ICML |
2021 |
6 |
Addressing the Long-term Impact of ML Decisions via Policy Regret.
|
IJCAI |
2021 |
4 |
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning.
|
ICML |
2021 |
8 |
Risk-averse Heteroscedastic Bayesian Optimization.
|
NIPS/NeurIPS |
2021 |
8 |
Online Submodular Resource Allocation with Applications to Rebalancing Shared Mobility Systems.
|
ICML |
2021 |
0 |
Hierarchical Skills for Efficient Exploration.
|
NIPS/NeurIPS |
2021 |
15 |
Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models.
|
NIPS/NeurIPS |
2021 |
1 |
Regret Bounds for Gaussian-Process Optimization in Large Domains.
|
NIPS/NeurIPS |
2021 |
3 |
Fast Projection Onto Convex Smooth Constraints.
|
ICML |
2021 |
2 |
Meta-Learning Reliable Priors in the Function Space.
|
NIPS/NeurIPS |
2021 |
15 |
Information Directed Reward Learning for Reinforcement Learning.
|
NIPS/NeurIPS |
2021 |
5 |
No-regret Algorithms for Capturing Events in Poisson Point Processes.
|
ICML |
2021 |
3 |
Robust Generalization despite Distribution Shift via Minimum Discriminating Information.
|
NIPS/NeurIPS |
2021 |
4 |
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems.
|
NIPS/NeurIPS |
2021 |
6 |
Misspecified Gaussian Process Bandit Optimization.
|
NIPS/NeurIPS |
2021 |
13 |
Safe and Efficient Model-free Adaptive Control via Bayesian Optimization.
|
ICRA |
2021 |
10 |
Risk-Averse Offline Reinforcement Learning.
|
ICLR |
2021 |
38 |
Learning Set Functions that are Sparse in Non-Orthogonal Fourier Bases.
|
AAAI |
2021 |
0 |
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees.
|
ICML |
2021 |
0 |
Logistic Q-Learning.
|
AISTATS |
2021 |
0 |
Online Active Model Selection for Pre-trained Classifiers.
|
AISTATS |
2021 |
0 |
Stochastic Linear Bandits Robust to Adversarial Attacks.
|
AISTATS |
2021 |
0 |
A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness.
|
AIES |
2021 |
0 |
Multi-Scale Representation Learning on Proteins.
|
NIPS/NeurIPS |
2021 |
0 |
Learning Graph Models for Retrosynthesis Prediction.
|
NIPS/NeurIPS |
2021 |
0 |
Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator.
|
ICLR |
2021 |
0 |
Mixed-Variable Bayesian Optimization.
|
IJCAI |
2020 |
29 |
Gradient Estimation with Stochastic Softmax Tricks.
|
NIPS/NeurIPS |
2020 |
54 |
Safe Reinforcement Learning via Curriculum Induction.
|
NIPS/NeurIPS |
2020 |
45 |
Corruption-Tolerant Gaussian Process Bandit Optimization.
|
AISTATS |
2020 |
41 |
A Real-Robot Dataset for Assessing Transferability of Learned Dynamics Models.
|
ICRA |
2020 |
5 |
Experimental Design for Optimization of Orthogonal Projection Pursuit Models.
|
AAAI |
2020 |
2 |
Learning to Play Sequential Games versus Unknown Opponents.
|
NIPS/NeurIPS |
2020 |
13 |
Coresets via Bilevel Optimization for Continual Learning and Streaming.
|
NIPS/NeurIPS |
2020 |
93 |
Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning.
|
NIPS/NeurIPS |
2020 |
38 |
Distributionally Robust Bayesian Optimization.
|
AISTATS |
2020 |
51 |
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models.
|
ICML |
2020 |
4 |
Mixed Strategies for Robust Optimization of Unknown Objectives.
|
AISTATS |
2020 |
8 |
Information Directed Sampling for Linear Partial Monitoring.
|
COLT |
2020 |
27 |
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems.
|
AAAI |
2020 |
0 |
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization.
|
ICRA |
2020 |
0 |
Convergence Analysis of Block Coordinate Algorithms with Determinantal Sampling.
|
AISTATS |
2020 |
0 |
Adaptive Sampling for Stochastic Risk-Averse Learning.
|
NIPS/NeurIPS |
2020 |
0 |
Contextual Games: Multi-Agent Learning with Side Information.
|
NIPS/NeurIPS |
2020 |
0 |
Multi-Player Bandits: The Adversarial Case.
|
JMLR |
2020 |
0 |
Adaptive Sequence Submodularity.
|
NIPS/NeurIPS |
2019 |
23 |
Safe Convex Learning under Uncertain Constraints.
|
AISTATS |
2019 |
32 |
Mobile Robotic Painting of Texture.
|
ICRA |
2019 |
11 |
Projection Free Online Learning over Smooth Sets.
|
AISTATS |
2019 |
20 |
Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature.
|
AISTATS |
2019 |
8 |
No-Regret Bayesian Optimization with Unknown Hyperparameters.
|
JMLR |
2019 |
37 |
Stochastic Bandits with Context Distributions.
|
NIPS/NeurIPS |
2019 |
12 |
Safe Exploration for Interactive Machine Learning.
|
NIPS/NeurIPS |
2019 |
45 |
Learning Generative Models across Incomparable Spaces.
|
ICML |
2019 |
76 |
Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference.
|
ICML |
2019 |
19 |
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning.
|
KDD |
2019 |
111 |
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces.
|
ICML |
2019 |
94 |
Efficiently Learning Fourier Sparse Set Functions.
|
NIPS/NeurIPS |
2019 |
9 |
No-Regret Learning in Unknown Games with Correlated Payoffs.
|
NIPS/NeurIPS |
2019 |
22 |
Consistent Online Optimization: Convex and Submodular.
|
AISTATS |
2019 |
10 |
Online Variance Reduction with Mixtures.
|
ICML |
2019 |
11 |
Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning.
|
IJCAI |
2019 |
33 |
AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs.
|
ICML |
2019 |
0 |
Fast Gaussian process based gradient matching for parameter identification in systems of nonlinear ODEs.
|
AISTATS |
2019 |
0 |
Teaching Multiple Concepts to a Forgetful Learner.
|
NIPS/NeurIPS |
2019 |
0 |
A Domain Agnostic Measure for Monitoring and Evaluating GANs.
|
NIPS/NeurIPS |
2019 |
0 |
Incentive-Compatible Forecasting Competitions.
|
AAAI |
2018 |
17 |
Provable Variational Inference for Constrained Log-Submodular Models.
|
NIPS/NeurIPS |
2018 |
3 |
Learning to Interact With Learning Agents.
|
AAAI |
2018 |
9 |
Submodularity on Hypergraphs: From Sets to Sequences.
|
AISTATS |
2018 |
17 |
Preventing Disparate Treatment in Sequential Decision Making.
|
IJCAI |
2018 |
34 |
Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features.
|
NIPS/NeurIPS |
2018 |
126 |
Information Directed Sampling and Bandits with Heteroscedastic Noise.
|
COLT |
2018 |
76 |
Online Variance Reduction for Stochastic Optimization.
|
COLT |
2018 |
19 |
Reinforced Imitation: Sample Efficient Deep Reinforcement Learning for Mapless Navigation by Leveraging Prior Demonstrations.
|
IEEE Robotics and Automation Letters |
2018 |
112 |
Discrete Sampling using Semigradient-based Product Mixtures.
|
UAI |
2018 |
2 |
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making.
|
NIPS/NeurIPS |
2018 |
100 |
Differentiable Submodular Maximization.
|
IJCAI |
2018 |
40 |
Streaming Non-Monotone Submodular Maximization: Personalized Video Summarization on the Fly.
|
AAAI |
2018 |
0 |
Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints.
|
AAAI |
2018 |
0 |
Learning User Preferences to Incentivize Exploration in the Sharing Economy.
|
AAAI |
2018 |
0 |
The Lyapunov Neural Network: Adaptive Stability Certification for Safe Learning of Dynamical Systems.
|
CoRL |
2018 |
0 |
Scalable k -Means Clustering via Lightweight Coresets.
|
KDD |
2018 |
0 |
Efficient Online Learning for Optimizing Value of Information: Theory and Application to Interactive Troubleshooting.
|
UAI |
2017 |
11 |
Uniform Deviation Bounds for k-Means Clustering.
|
ICML |
2017 |
13 |
Deletion-Robust Submodular Maximization: Data Summarization with "the Right to be Forgotten".
|
ICML |
2017 |
68 |
Probabilistic Submodular Maximization in Sub-Linear Time.
|
ICML |
2017 |
28 |
Proper Proxy Scoring Rules.
|
AAAI |
2017 |
17 |
Training Gaussian Mixture Models at Scale via Coresets.
|
JMLR |
2017 |
0 |
Distributed and Provably Good Seedings for k-Means in Constant Rounds.
|
ICML |
2017 |
27 |
Selecting Sequences of Items via Submodular Maximization.
|
AAAI |
2017 |
43 |
Differentiable Learning of Submodular Functions.
|
NIPS/NeurIPS |
2017 |
2 |
Stochastic Submodular Maximization: The Case of Coverage Functions.
|
NIPS/NeurIPS |
2017 |
45 |
Differentially Private Submodular Maximization: Data Summarization in Disguise.
|
ICML |
2017 |
26 |
Safe Model-based Reinforcement Learning with Stability Guarantees.
|
NIPS/NeurIPS |
2017 |
609 |
Interactive Submodular Bandit.
|
NIPS/NeurIPS |
2017 |
20 |
Virtual vs. real: Trading off simulations and physical experiments in reinforcement learning with Bayesian optimization.
|
ICRA |
2017 |
91 |
Non-monotone Continuous DR-submodular Maximization: Structure and Algorithms.
|
NIPS/NeurIPS |
2017 |
15 |
Improving Optimization-Based Approximate Inference by Clamping Variables.
|
UAI |
2017 |
0 |
Guarantees for Greedy Maximization of Non-submodular Functions with Applications.
|
ICML |
2017 |
198 |
Near-optimal Bayesian Active Learning with Correlated and Noisy Tests.
|
AISTATS |
2017 |
0 |
Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains.
|
AISTATS |
2017 |
0 |
Learning Probabilistic Submodular Diversity Models Via Noise Contrastive Estimation.
|
AISTATS |
2016 |
34 |
Horizontally Scalable Submodular Maximization.
|
ICML |
2016 |
8 |
Better safe than sorry: Risky function exploitation through safe optimization.
|
Cognitive Science |
2016 |
8 |
Variational Inference in Mixed Probabilistic Submodular Models.
|
NIPS/NeurIPS |
2016 |
23 |
Actively Learning Hemimetrics with Applications to Eliciting User Preferences.
|
ICML |
2016 |
11 |
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes.
|
NIPS/NeurIPS |
2016 |
141 |
Linear-Time Outlier Detection via Sensitivity.
|
IJCAI |
2016 |
12 |
Learning Sparse Additive Models with Interactions in High Dimensions.
|
AISTATS |
2016 |
15 |
Truncated Variance Reduction: A Unified Approach to Bayesian Optimization and Level-Set Estimation.
|
NIPS/NeurIPS |
2016 |
64 |
Learning Sparse Combinatorial Representations via Two-stage Submodular Maximization.
|
ICML |
2016 |
22 |
Fast and Provably Good Seedings for k-Means.
|
NIPS/NeurIPS |
2016 |
115 |
e-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem.
|
JMLR |
2016 |
22 |
Approximate K-Means++ in Sublinear Time.
|
AAAI |
2016 |
104 |
Cooperative Graphical Models.
|
NIPS/NeurIPS |
2016 |
1 |
Distributed Submodular Maximization.
|
JMLR |
2016 |
0 |
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization.
|
AAAI |
2016 |
0 |
Safe controller optimization for quadrotors with Gaussian processes.
|
ICRA |
2016 |
0 |
Strong Coresets for Hard and Soft Bregman Clustering with Applications to Exponential Family Mixtures.
|
AISTATS |
2016 |
0 |
Coresets for Nonparametric Estimation - the Case of DP-Means.
|
ICML |
2015 |
81 |
Efficient visual exploration and coverage with a micro aerial vehicle in unknown environments.
|
ICRA |
2015 |
109 |
Discovering Valuable items from Massive Data.
|
KDD |
2015 |
32 |
Distributed Submodular Cover: Succinctly Summarizing Massive Data.
|
NIPS/NeurIPS |
2015 |
47 |
Higher-Order Inference for Multi-class Log-Supermodular Models.
|
ICCV |
2015 |
19 |
Information Gathering in Networks via Active Exploration.
|
IJCAI |
2015 |
16 |
Sampling from Probabilistic Submodular Models.
|
NIPS/NeurIPS |
2015 |
31 |
Building Hierarchies of Concepts via Crowdsourcing.
|
IJCAI |
2015 |
38 |
Non-Monotone Adaptive Submodular Maximization.
|
IJCAI |
2015 |
28 |
Submodular Surrogates for Value of Information.
|
AAAI |
2015 |
44 |
Safe Exploration for Optimization with Gaussian Processes.
|
ICML |
2015 |
250 |
Scalable Variational Inference in Log-supermodular Models.
|
ICML |
2015 |
32 |
Sequential Information Maximization: When is Greedy Near-optimal?
|
COLT |
2015 |
51 |
Tradeoffs for Space, Time, Data and Risk in Unsupervised Learning.
|
AISTATS |
2015 |
15 |
Incentivizing Users for Balancing Bike Sharing Systems.
|
AAAI |
2015 |
182 |
Lazier Than Lazy Greedy.
|
AAAI |
2015 |
0 |
Robot navigation in dense human crowds: Statistical models and experimental studies of human-robot cooperation.
|
IJRR |
2015 |
0 |
From MAP to Marginals: Variational Inference in Bayesian Submodular Models.
|
NIPS/NeurIPS |
2014 |
76 |
Efficient Partial Monitoring with Prior Information.
|
NIPS/NeurIPS |
2014 |
15 |
Explore-exploit in top-N recommender systems via Gaussian processes.
|
RecSys |
2014 |
78 |
Active Detection via Adaptive Submodularity.
|
ICML |
2014 |
44 |
Fully autonomous focused exploration for robotic environmental monitoring.
|
ICRA |
2014 |
58 |
Near-Optimally Teaching the Crowd to Classify.
|
ICML |
2014 |
109 |
Streaming submodular maximization: massive data summarization on the fly.
|
KDD |
2014 |
305 |
Near Optimal Bayesian Active Learning for Decision Making.
|
AISTATS |
2014 |
56 |
Efficient Sampling for Learning Sparse Additive Models in High Dimensions.
|
NIPS/NeurIPS |
2014 |
11 |
Parallelizing exploration-exploitation tradeoffs in Gaussian process bandit optimization.
|
JMLR |
2014 |
0 |
Near-optimal Batch Mode Active Learning and Adaptive Submodular Optimization.
|
ICML |
2013 |
132 |
Distributed Submodular Maximization: Identifying Representative Elements in Massive Data.
|
NIPS/NeurIPS |
2013 |
253 |
Truthful incentives in crowdsourcing tasks using regret minimization mechanisms.
|
WWW |
2013 |
278 |
Active Learning for Multi-Objective Optimization.
|
ICML |
2013 |
134 |
Active Learning for Level Set Estimation.
|
IJCAI |
2013 |
125 |
High-Dimensional Gaussian Process Bandits.
|
NIPS/NeurIPS |
2013 |
158 |
Optimizing waypoints for monitoring spatiotemporal phenomena.
|
IJRR |
2013 |
94 |
Robot navigation in dense human crowds: the case for cooperation.
|
ICRA |
2013 |
140 |
Robust landmark selection for mobile robot navigation.
|
IROS |
2013 |
18 |
Parallelizing Exploration-Exploitation Tradeoffs with Gaussian Process Bandit Optimization.
|
ICML |
2012 |
401 |
Learning Fourier Sparse Set Functions.
|
AISTATS |
2012 |
48 |
Joint Optimization and Variable Selection of High-dimensional Gaussian Processes.
|
ICML |
2012 |
79 |
Crowdclustering.
|
NIPS/NeurIPS |
2011 |
127 |
Contextual Gaussian Process Bandit Optimization.
|
NIPS/NeurIPS |
2011 |
324 |
Scalable Training of Mixture Models via Coresets.
|
NIPS/NeurIPS |
2011 |
134 |
Dynamic Resource Allocation in Conservation Planning.
|
AAAI |
2011 |
35 |
Randomized Sensing in Adversarial Environments.
|
IJCAI |
2011 |
23 |
Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization.
|
JAIR |
2011 |
0 |
Efficient Minimization of Decomposable Submodular Functions.
|
NIPS/NeurIPS |
2010 |
130 |
Near-Optimal Bayesian Active Learning with Noisy Observations.
|
NIPS/NeurIPS |
2010 |
181 |
A Utility-Theoretic Approach to Privacy in Online Services.
|
JAIR |
2010 |
2 |
Inferring networks of diffusion and influence.
|
KDD |
2010 |
58 |
Unfreezing the robot: Navigation in dense, interacting crowds.
|
IROS |
2010 |
456 |
SFO: A Toolbox for Submodular Function Optimization.
|
JMLR |
2010 |
104 |
Submodular Dictionary Selection for Sparse Representation.
|
ICML |
2010 |
129 |
Discriminative Clustering by Regularized Information Maximization.
|
NIPS/NeurIPS |
2010 |
283 |
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization.
|
COLT |
2010 |
124 |
Informative path planning for an autonomous underwater vehicle.
|
ICRA |
2010 |
128 |
Budgeted Nonparametric Learning from Data Streams.
|
ICML |
2010 |
112 |
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design.
|
ICML |
2010 |
0 |
Optimal Value of Information in Graphical Models.
|
JAIR |
2009 |
129 |
Online Learning of Assignments.
|
NIPS/NeurIPS |
2009 |
62 |
Nonmyopic Adaptive Informative Path Planning for Multiple Robots.
|
IJCAI |
2009 |
123 |
Efficient Informative Sensing using Multiple Robots.
|
JAIR |
2009 |
0 |
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies.
|
JMLR |
2008 |
197 |
A Utility-Theoretic Approach to Privacy and Personalization.
|
AAAI |
2008 |
82 |
Selecting Observations against Adversarial Objectives.
|
NIPS/NeurIPS |
2007 |
46 |
Nonmyopic Informative Path Planning in Spatio-Temporal Models.
|
AAAI |
2007 |
88 |
Robust, low-cost, non-intrusive sensing and recognition of seated postures.
|
UIST |
2007 |
124 |
Near-optimal Observation Selection using Submodular Functions.
|
AAAI |
2007 |
333 |
Cost-effective outbreak detection in networks.
|
KDD |
2007 |
2317 |
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach.
|
ICML |
2007 |
205 |
Efficient Planning of Informative Paths for Multiple Robots.
|
IJCAI |
2007 |
0 |
Data association for topic intensity tracking.
|
ICML |
2006 |
64 |
Near-optimal Nonmyopic Value of Information in Graphical Models.
|
UAI |
2005 |
443 |
Trading off Prediction Accuracy and Power Consumption for Context-Aware Wearable Computing.
|
ISWC |
2005 |
156 |
Optimal Nonmyopic Value of Information in Graphical Models - Efficient Algorithms and Theoretical Limits.
|
IJCAI |
2005 |
88 |
Near-optimal sensor placements in Gaussian processes.
|
ICML |
2005 |
513 |
Unsupervised, Dynamic Identification of Physiological and Activity Context in Wearable Computing.
|
ISWC |
2003 |
190 |
SenSay: A Context-Aware Mobile Phone.
|
ISWC |
2003 |
372 |