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Found 41 papers, 11 papers with code
Information That Matters: Exploring Information Needs of People Affected by Algorithmic Decisions
Explanations of AI systems rarely address the information needs of people affected by algorithmic decision-making (ADM).
Active Third-Person Imitation Learning
We consider the problem of third-person imitation learning with the additional challenge that the learner must select the perspective from which they observe the expert.
Posterior Consistency for Missing Data in Variational Autoencoders
We consider the problem of learning Variational Autoencoders (VAEs), i. e., a type of deep generative model, from data with missing values.
Large Language Models for In-Context Student Modeling: Synthesizing Student's Behavior in Visual Programming
We instantiate several methods based on LLM-SS framework and evaluate them using an existing benchmark, StudentSyn, for student attempt synthesis in a visual programming domain.
Applying Interdisciplinary Frameworks to Understand Algorithmic Decision-Making
We argue that explanations for "algorithmic decision-making" (ADM) systems can profit by adopting practices that are already used in the learning sciences.
Learning Safety Constraints from Demonstrations with Unknown Rewards
We evaluate CoCoRL in gridworld environments and a driving simulation with multiple constraints.
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes
We introduce a scaffolding framework based on pop quizzes presented as multi-choice programming tasks.
Interactively Learning Preference Constraints in Linear Bandits
We provide an instance-dependent lower bound for constrained linear best-arm identification and show that ACOL's sample complexity matches the lower bound in the worst-case.
Equity and Fairness of Bayesian Knowledge Tracing
We consider the equity and fairness of curricula derived from Knowledge Tracing models.
Option Transfer and SMDP Abstraction with Successor Features
Abstraction plays an important role in the generalisation of knowledge and skills and is key to sample efficient learning.
Contextual HyperNetworks for Novel Feature Adaptation
While deep learning has obtained state-of-the-art results in many applications, the adaptation of neural network architectures to incorporate new output features remains a challenge, as neural networks are commonly trained to produce a fixed output dimension.
Information Directed Reward Learning for Reinforcement Learning
For many reinforcement learning (RL) applications, specifying a reward is difficult.
Reinforcement Learning with Efficient Active Feature Acquisition
Solving real-life sequential decision making problems under partial observability involves an exploration-exploitation problem.
Replication-Robust Payoff-Allocation for Machine Learning Data Markets
In this paper, we systematically study the replication manipulation in submodular games and investigate replication robustness, a metric that quantitatively measures the robustness of solution concepts against replication.
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets.
AMRL: Aggregated Memory For Reinforcement Learning
In many partially observable scenarios, Reinforcement Learning (RL) agents must rely on long-term memory in order to learn an optimal policy.
Educational Question Mining At Scale: Prediction, Analysis and Personalization
Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students.
Resource-Efficient Neural Networks for Embedded Systems
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches.
Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model
In this paper, we address the ice-start problem, i. e., the challenge of deploying machine learning models when only a little or no training data is initially available, and acquiring each feature element of data is associated with costs.
Collaborative Machine Learning Markets with Data-Replication-Robust Payments
We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data.
Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck
We discuss those differences and propose modifications to existing regularization techniques in order to better adapt them to RL.
HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals
In this paper, we focused on improving VAEs for real-valued data that has heterogeneous marginal distributions.
Towards Deployment of Robust AI Agents for Human-Machine Partnerships
In the test phase, the AI agent has to interact with a user of unknown type.
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
In this paper we introduce the ice-start problem, i. e., the challenge of deploying machine learning models when only little or no training data is initially available, and acquiring each feature element of data is associated with costs.
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences.
Efficient and Robust Machine Learning for Real-World Systems
In that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.
Teaching Inverse Reinforcement Learners via Features and Demonstrations
In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i. e., where there is a mismatch between the worldviews of the learner and the expert.
Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning
Posterior sampling for reinforcement learning (PSRL) is an effective method for balancing exploration and exploitation in reinforcement learning.
EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE
Many real-life decision-making situations allow further relevant information to be acquired at a specific cost, for example, in assessing the health status of a patient we may decide to take additional measurements such as diagnostic tests or imaging scans before making a final assessment.
Sum-Product Networks for Sequence Labeling
We consider higher-order linear-chain conditional random fields (HO-LC-CRFs) for sequence modelling, and use sum-product networks (SPNs) for representing higher-order input- and output-dependent factors.
Optical Character Recognition
Optical Character Recognition (OCR)
Variational Inference for Data-Efficient Model Learning in POMDPs
In this paper we propose DELIP, an approach to model learning for POMDPs that utilizes amortized structured variational inference.
Fake News Detection in Social Networks via Crowd Signals
The main objective of our work is to minimize the spread of misinformation by stopping the propagation of fake news in the network.
Social and Information Networks
Learning User Preferences to Incentivize Exploration in the Sharing Economy
We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb.
Guarantees for Greedy Maximization of Non-submodular Functions with Applications
Our guarantees are characterized by a combination of the (generalized) curvature $\alpha$ and the submodularity ratio $\gamma$.
Coordinated Online Learning With Applications to Learning User Preferences
We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners.
Cooperative Graphical Models
We study a rich family of distributions that capture variable interactions significantly more expressive than those representable with low-treewidth or pairwise graphical models, or log-supermodular models.
Variational Inference in Mixed Probabilistic Submodular Models
We consider the problem of variational inference in probabilistic models with both log-submodular and log-supermodular higher-order potentials.
Actively Learning Hemimetrics with Applications to Eliciting User Preferences
We propose an active learning algorithm that substantially reduces this sample complexity by exploiting the structural constraints on the version space of hemimetrics.
Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization
When the underlying submodular function is unknown, users' feedback can provide noisy evaluations of the function that we seek to maximize.
Learning Mixtures of Submodular Functions for Image Collection Summarization
This paper provides, to our knowledge, the first systematic approach for quantifying the problem of image collection summarization, along with a new dataset of image collections and human summaries.
