Search Results for author:
Found 19 papers, 9 papers with code
Hierarchical Average-Reward Linearly-solvable Markov Decision Processes
We introduce a novel approach to hierarchical reinforcement learning for Linearly-solvable Markov Decision Processes (LMDPs) in the infinite-horizon average-reward setting.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Conventional reinforcement learning (RL) methods can successfully solve a wide range of sequential decision problems.
Improving Subgraph-GNNs via Edge-Level Ego-Network Encodings
We present a novel edge-level ego-network encoding for learning on graphs that can boost Message Passing Graph Neural Networks (MP-GNNs) by providing additional node and edge features or extending message-passing formats.
Beyond 1-WL with Local Ego-Network Encodings
Identifying similar network structures is key to capture graph isomorphisms and learn representations that exploit structural information encoded in graph data.
Uncovering the Limits of Text-based Emotion Detection
Identifying emotions from text is crucial for a variety of real world tasks.
Globally Optimal Hierarchical Reinforcement Learning for Linearly-Solvable Markov Decision Processes
In this work we present a novel approach to hierarchical reinforcement learning for linearly-solvable Markov decision processes.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Hierarchical Width-Based Planning and Learning
In classical planning, we show how IW(1) at two levels of abstraction can solve problems of width 2.
Inductive Graph Embeddings through Locality Encodings
Learning embeddings from large-scale networks is an open challenge.
Adaptive Smoothing Path Integral Control
We identify PICE as the infinite smoothing limit of such technique and show that the sample efficiency problems that PICE suffers disappear for finite levels of smoothing.
Input complexity and out-of-distribution detection with likelihood-based generative models
Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.
Ranked #10 on
Anomaly Detection
on Unlabeled CIFAR-10 vs CIFAR-100
Consequential Ranking Algorithms and Long-term Welfare
Can we design ranking models that understand the consequences of their proposed rankings and, more importantly, are able to avoid the undesirable ones?
Deep Policies for Width-Based Planning in Pixel Domains
Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner.
The few-get-richer: a surprising consequence of popularity-based rankings
Ranking algorithms play a crucial role in online platforms ranging from search engines to recommender systems.
Sharing emotions at scale: The Vent dataset
We present the Vent dataset, the largest annotated dataset of text, emotions, and social connections to date.
Social and Information Networks Human-Computer Interaction
Adult content in Social Live Streaming Services: Characterizing deviant users and relationships
Social Live Stream Services (SLSS) exploit a new level of social interaction.
Social and Information Networks
Improving width-based planning with compact policies
The planning step hinges on the Iterated-Width (IW) planner, a state of the art planner that makes explicit use of the state representation to perform structured exploration.
A unified view of entropy-regularized Markov decision processes
We propose a general framework for entropy-regularized average-reward reinforcement learning in Markov decision processes (MDPs).
Fast rates for online learning in Linearly Solvable Markov Decision Processes
We study the problem of online learning in a class of Markov decision processes known as linearly solvable MDPs.
Hierarchical Linearly-Solvable Markov Decision Problems
We present a hierarchical reinforcement learning framework that formulates each task in the hierarchy as a special type of Markov decision process for which the Bellman equation is linear and has analytical solution.
Hierarchical Reinforcement Learning
reinforcement-learning
+2
