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Found 17 papers, 6 papers with code
Provable Benefits of Representational Transfer in Reinforcement Learning
We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used to learn a good policy in a \emph{target task}.
Learning Bellman Complete Representations for Offline Policy Evaluation
We study representation learning for Offline Reinforcement Learning (RL), focusing on the important task of Offline Policy Evaluation (OPE).
Doubly Robust Distributionally Robust Off-Policy Evaluation and Learning
Thanks to a localization technique, LDR$^2$OPE only requires fitting a small number of regressions, just like DR methods for standard OPE.
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query optimization.
NP-ODE: Neural Process Aided Ordinary Differential Equations for Uncertainty Quantification of Finite Element Analysis
Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems.
Scalable and Provably Accurate Algorithms for Differentially Private Distributed Decision Tree Learning
We provide the first utility guarantees for differentially private top-down decision tree learning in both the single machine and distributed settings.
Respond-CAM: Analyzing Deep Models for 3D Imaging Data by Visualizations
The weighted feature maps are combined to produce a heatmap that highlights the important regions in the image for predicting the target concept.
Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography
Cellular Electron Cryo-Tomography (CECT) is a powerful 3D imaging tool for studying the native structure and organization of macromolecules inside single cells.
Partitioned Active Learning for Heterogeneous Systems
Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems.
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signal about the missing confounders.
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing on the objective of Conditional Value at Risk (CVaR) with risk tolerance $\tau$.
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
In online RL, we propose a DistRL algorithm that constructs confidence sets using maximum likelihood estimation.
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Second-order bounds are instance-dependent bounds that scale with the variance of return, which we prove are tighter than the previously known small-loss bounds of distributional RL.
Distributional Reinforcement Learning
Multi-Armed Bandits
+1
Switching the Loss Reduces the Cost in Batch Reinforcement Learning
We propose training fitted Q-iteration with log-loss (FQI-LOG) for batch reinforcement learning (RL).
Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL
We study Risk-Sensitive Reinforcement Learning (RSRL) with the Optimized Certainty Equivalent (OCE) risk, which generalizes Conditional Value-at-risk (CVaR), entropic risk and Markowitz's mean-variance.
Adversarial Defense Teacher for Cross-Domain Object Detection under Poor Visibility Conditions
Existing object detectors encounter challenges in handling domain shifts between training and real-world data, particularly under poor visibility conditions like fog and night.
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes
We characterize the sharp bounds on policy value under this model, that is, the tightest possible bounds given by the transition observations from the original MDP, and we study the estimation of these bounds from such transition observations.
