Search Results for author:
Found 18 papers, 8 papers with code
Monte Carlo Tree Search in the Presence of Transition Uncertainty
We study and improve MCTS in the context where the environment model is given but imperfect.
Replay Memory as An Empirical MDP: Combining Conservative Estimation with Experience Replay
In this work, we further exploit the information in the replay memory by treating it as an empirical \emph{Replay Memory MDP (RM-MDP)}.
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess
In this work, we adapt a training approach inspired by the original AlphaGo system to play the imperfect information game of Reconnaissance Blind Chess.
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability
In this paper, we study learning in probabilistic domains where the learner may receive incorrect labels but can improve the reliability of labels by repeatedly sampling them.
Cedille: A large autoregressive French language model
Scaling up the size and training of autoregressive language models has enabled novel ways of solving Natural Language Processing tasks using zero-shot and few-shot learning.
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization
Bayesian Active Learning (BAL) is an efficient framework for learning the parameters of a model, in which input stimuli are selected to maximize the mutual information between the observations and the unknown parameters.
A Simple Unified Framework for Anomaly Detection in Deep Reinforcement Learning
In this paper, we propose a simple yet effective anomaly detection framework for deep RL algorithms that simultaneously considers random, adversarial and out-of-distribution~(OOD) state outliers.
Addressing materials' microstructure diversity using transfer learning
We show that a state-of-the-art UDA approach surpasses the na\"ive application of source domain trained models on the target domain (generalization baseline) to a large extent.
A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems
We discuss and compare two different Siamese network architectures for this task: a twin network that compares the two sets resulting after the addition, and a triplet network that models the contribution of each candidate to the existing set.
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking
Drafting, i. e., the selection of a subset of items from a larger candidate set, is a key element of many games and related problems.
A Deep Dive into Conflict Generating Decisions
We also show an important connection between consecutive clauses learned within the same mc decision, where one learned clause triggers the learning of the next one forming a chain of clauses.
Addressing machine learning concept drift reveals declining vaccine sentiment during the COVID-19 pandemic
We show that while vaccine sentiment has declined considerably during the COVID-19 pandemic in 2020, algorithms trained on pre-pandemic data would have largely missed this decline due to concept drift.
Experts and authorities receive disproportionate attention on Twitter during the COVID-19 crisis
Timely access to accurate information is crucial during the COVID-19 pandemic.
Social and Information Networks
COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19.
Learning to Combat Compounding-Error in Model-Based Reinforcement Learning
Despite its potential to improve sample complexity versus model-free approaches, model-based reinforcement learning can fail catastrophically if the model is inaccurate.
Model-based Reinforcement Learning
reinforcement-learning
+1
Maximum Entropy Monte-Carlo Planning
We then extend this approach to general sequential decision making by developing a general MCTS algorithm, Maximum Entropy for Tree Search (MENTS).
Characterization of Glue Variables in CDCL SAT Solving
We first show experimentally, by running the state-of-the-art CDCL SAT solver MapleLCMDist on benchmarks from SAT Competition-2017 and 2018, that branching decisions with glue variables are categorically more inference and conflict efficient than nonglue variables.
Structured Best Arm Identification with Fixed Confidence
We study the problem of identifying the best action among a set of possible options when the value of each action is given by a mapping from a number of noisy micro-observables in the so-called fixed confidence setting.
