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Found 14 papers, 0 papers with code
The Delusional Hedge Algorithm as a Model of Human Learning from Diverse Opinions
Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome.
Corruption-Robust Offline Reinforcement Learning
Surprisingly, in this case, the knowledge of $\epsilon$ is necessary, as we show that being adaptive to unknown $\epsilon$ is impossible. This again contrasts with recent results on corruption-robust online RL and implies that robust offline RL is a strictly harder problem.
Policy Gradient Bayesian Robust Optimization for Imitation Learning
Results suggest that PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse and outperforms state-of-the-art imitation learning algorithms when learning from ambiguous demonstrations by hedging against uncertainty, rather than seeking to uniquely identify the demonstrator's reward function.
Active Learning with Oracle Epiphany
We present a theoretical analysis of active learning with more realistic interactions with human oracles.
Human Memory Search as Initial-Visit Emitting Random Walk
In this paper, we propose the first efficient maximum likelihood estimate (MLE) for INVITE by decomposing the censored output into a series of absorbing random walks.
Optimal Teaching for Limited-Capacity Human Learners
We apply a machine teaching procedure to a cognitive model that is either limited capacity (as humans are) or unlimited capacity (as most machine learning systems are).
Machine Teaching for Bayesian Learners in the Exponential Family
What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner?
Learning Higher-Order Graph Structure with Features by Structure Penalty
In discrete undirected graphical models, the conditional independence of node labels Y is specified by the graph structure.
How Do Humans Teach: On Curriculum Learning and Teaching Dimension
We study the empirical strategies that humans follow as they teach a target concept with a simple 1D threshold to a robot.
Humans Learn Using Manifolds, Reluctantly
When the distribution of unlabeled data in feature space lies along a manifold, the information it provides may be used by a learner to assist classification in a semi-supervised setting.
Transduction with Matrix Completion: Three Birds with One Stone
We pose transductive classification as a matrix completion problem.
Human Rademacher Complexity
We propose to use Rademacher complexity, originally developed in computational learning theory, as a measure of human learning capacity.
Human Active Learning
We investigate a topic at the interface of machine learning and cognitive science.
Unlabeled data: Now it helps, now it doesn't
We show that there are large classes of problems for which SSL can significantly outperform supervised learning, in finite sample regimes and sometimes also in terms of error convergence rates.
