Aldo Pacchiano
58 papers · 2017–2025 · 10 venues · across top CS/AI venues
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Venues
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Research topics
Machine Learning
(55)
Optimization & Theory
(42)
Reinforcement Learning
(29)
Learning Types Β· Machine Learning
(27)
Methods
(26)
Mathematics & Optimization
(19)
Optimization
(18)
Artificial Intelligence
(13)
Core AI
(10)
Deep Learning
(4)
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(4)
Core Methods
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Learning Paradigms
(4)
Learning Types Β· Deep Learning
(3)
Mathematics
(3)
Keywords
regret bound
(18)
reinforcement learning
(14)
multi-armed bandit
(8)
sample complexity
(7)
model selection
(7)
contextual bandit
(6)
online learning
(6)
policy optimization
(5)
linear bandit
(4)
function approximation
(4)
continuous control
(4)
policy gradient
(3)
linear programming relaxation
(3)
black-box optimization
(3)
eluder dimension
(3)
sample efficiency
(2)
stochastic gradient descent
(2)
deep reinforcement learning
(2)
active learning
(2)
markov decision process
(2)
Papers
Adaptive Exploration for Multi-Reward Multi-Policy Evaluation
ICML 2025
A Theoretical Framework for Partially-Observed Reward States in RLHF
ICLR 2025
Contextual Bandits with Stage-wise Constraints
JMLR 2025
Feasible Action Search for Bandit Linear Programs via Thompson Sampling
ICML 2025
Multiple-policy Evaluation via Density Estimation
ICML 2025
On the Hardness of Bandit Learning
COLT 2025
ORSO: Accelerating Reward Design via Online Reward Selection and Policy Optimization
ICLR 2025
Pure Exploration with Feedback Graphs
AISTATS 2025
Second Order Bounds for Contextual Bandits with Function Approximation
ICLR 2025
Data-Driven Online Model Selection With Regret Guarantees
AISTATS 2024
Improving Offline RL by Blending Heuristics
ICLR 2024
Provable Interactive Learning with Hindsight Instruction Feedback
ICML 2024
State-free Reinforcement Learning
NeurIPS 2024
An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Bandit
ALT 2023
Anytime Model Selection in Linear Bandits
NeurIPS 2023
A Unified Model and Dimension for Interactive Estimation
NeurIPS 2023
Dueling RL: Reinforcement Learning with Trajectory Preferences
AISTATS 2023
Experiment Planning with Function Approximation
NeurIPS 2023
Leveraging Offline Data in Online Reinforcement Learning
ICML 2023
Neural Design for Genetic Perturbation Experiments
ICLR 2023
Supervised Pretraining Can Learn In-Context Reinforcement Learning
NeurIPS 2023
Best of Both Worlds Model Selection
NeurIPS 2022
Learning General World Models in a Handful of Reward-Free Deployments
NeurIPS 2022
Meta Learning MDPs with linear transition models
AISTATS 2022
Online Nonsubmodular Minimization with Delayed Costs: From Full Information to Bandit Feedback
ICML 2022
Towards an Understanding of Default Policies in Multitask Policy Optimization
AISTATS 2022
Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity
NeurIPS 2022
Dynamic Balancing for Model Selection in Bandits and RL
ICML 2021
Learning the Truth From Only One Side of the Story
AISTATS 2021
Near Optimal Policy Optimization via REPS
NeurIPS 2021
Neural Pseudo-Label Optimism for the Bank Loan Problem
NeurIPS 2021
Online Model Selection for Reinforcement Learning with Function Approximation
AISTATS 2021
On the Theory of Reinforcement Learning with Once-per-Episode Feedback
NeurIPS 2021
Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection
NeurIPS 2021
Robustness Guarantees for Mode Estimation with an Application to Bandits
AAAI 2021
Sample Efficient Reinforcement Learning In Continuous State Spaces: A Perspective Beyond Linearity
ICML 2021
Stochastic Bandits with Linear Constraints
AISTATS 2021
Tactical Optimism and Pessimism for Deep Reinforcement Learning
NeurIPS 2021
Towards tractable optimism in model-based reinforcement learning
UAI 2021
Accelerated Message Passing for Entropy-Regularized MAP Inference
ICML 2020
Convergence Rates of Smooth Message Passing with Rounding in Entropy-Regularized MAP Inference
AISTATS 2020
Effective Diversity in Population Based Reinforcement Learning
NeurIPS 2020
ES-MAML: Simple Hessian-Free Meta Learning
ICLR 2020
Learning to Score Behaviors for Guided Policy Optimization
ICML 2020
Model Selection in Contextual Stochastic Bandit Problems
NeurIPS 2020
On Approximate Thompson Sampling with Langevin Algorithms
ICML 2020
Practical Nonisotropic Monte Carlo Sampling in High Dimensions via Determinantal Point Processes
AISTATS 2020
Ready Policy One: World Building Through Active Learning
ICML 2020
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian
NeurIPS 2020
Stochastic Flows and Geometric Optimization on the Orthogonal Group
ICML 2020
From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization
NeurIPS 2019
KAMA-NNs: Low-dimensional Rotation Based Neural Networks
AISTATS 2019
Online learning with kernel losses
ICML 2019
Provably Robust Blackbox Optimization for Reinforcement Learning
CoRL 2019
Wasserstein Fair Classification
UAI 2019
Gen-Oja: Simple & Efficient Algorithm for Streaming Generalized Eigenvector Computation
NeurIPS 2018
Geometrically Coupled Monte Carlo Sampling
NeurIPS 2018
Conditions beyond treewidth for tightness of higher-order LP relaxations
AISTATS 2017