Teaches diverse students in a classroom setting with minimal examples.
problem Teaching a diverse group of students with varying initial states and learning rates.
method Proves an optimal teaching strategy with O(min{d,N} log(1/eps)) examples, robust to limited knowledge, and studies workload-cost trade-offs.
result Teaching a target concept to the entire classroom using optimal number of examples, validated by experiments.
Study finds flipped classrooms improve student self-concept, enjoyment, but not exam scores.
problem Evaluating the impact of flipped classrooms on higher education outcomes.
method Double/debiased machine learning (DML) approach to analyze student data.
result No significant positive effects on exam scores, passing rates, or knowledge retention.
In school, a teacher plays an important role in various classroom teaching patterns. Likewise to this human learning activity, the learning using privileged information (LUPI) paradigm provides additional information generated by the teacher to 'teach' learning models during the training stage. Therefore, this novel le…
Machine learning assesses group collaboration in classrooms.
problem Assess student group collaboration in large classrooms.
method Deep learning models using Mixup data augmentation and ordinal-cross-entropy loss function.
result Improved assessment of group collaboration quality.
Method teaches students without teachers, estimating true labels from crowdsourcing.
problem Teaching without access to true labels.
method Apply crowdsourcing techniques to estimate true labels and student models for iterative teaching.
result Teaching performance is particularly effective for low-level students.
Study on teaching reinforcement learning with Q-learning, reducing sample complexity.
problem Reducing sample complexity in reinforcement learning.
method Characterized teaching dimension for Q-learning under different teacher control, presented optimal teaching algorithms.
result Minimum number of samples needed for reinforcement learning is characterized.
Study on teaching with imperfect knowledge, showing its impact on optimal teaching sets.
problem Effect of imperfect teacher knowledge on effective teaching.
method Connections to machine teaching problem, optimal teaching sets.
result Teacher's success or failure depends on imperfect knowledge.
Novel framework for teaching complexity in machine teaching models.
problem Understanding and comparing teaching models in batch and sequential settings.
method Developed a novel framework using preference functions to capture teaching complexity.
result Identified preference functions leading to linear teaching complexity in sequential models.
A collaborative machine teaching method that improves learner performance with privacy and efficiency.
problem Improving learner performance with distributed teachers while maintaining privacy and scalability.
method Formulates collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.
result The proposed method delivers significantly more accurate teaching results with high speed compared to non-collaborative MINLP-based super teaching.
Teaches sequential learners with changing inner states to improve future performance.
problem Teaching sequential learners with evolving inner states.
method Introduces an optimal control approach for multi-agent learning.
result Optimal control approach improves future performance of learners.
A new teaching model avoids collusion, optimizing teaching dimensions.
problem Avoiding collusion in teaching models.
method Introducing no-clash teaching model and defining NCTD parameter.
result No-clash teaching is optimal in teaching dimensions.
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications. However, most existing works in crowdsourcing mainly focus on label inference and incentive design. In this paper, we address a different problem of adaptive crowd teaching, which is a …
Paper improves teaching by considering learner's preferences and constraints.
problem Teaching without considering learner's preferences and constraints.
method Design of learner-aware teaching algorithms that account for learner's preferences and constraints.
result Significant performance improvements over learner-agnostic teaching.
Unified framework connects batch and sequential teaching models.
problem Understanding and comparing different teaching models.
method Developed a novel framework using preference functions.
result Equivalence between teaching models and specific preference functions.
We describe theoretical bounds and a practical algorithm for teaching a model by demonstration in a sequential decision making environment. Unlike previous efforts that have optimized learners that watch a teacher demonstrate a static policy, we focus on the teacher as a decision maker who can dynamically choose differ…
Improved student engagement detection using contextual and visual data.
problem Detecting students' behavioral engagement in real-world settings.
method Two-phase approach: contextual logs for active use, appearance information for engagement inference.
result Improved F1-scores from 0.77 to 0.82 with contextual information.
In this paper, we consider the problem of machine teaching, the inverse problem of machine learning. Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligen…
Study on teaching complexity in graphs, proving hardness and tractability.
problem Computing the minimum number of examples per concept for teaching.
method Classical and parameterized complexity analysis, NP-hardness, upper and lower bounds, fixed-parameter tractability.
result Nearly complete understanding of teaching complexity in graphs.
Paper tackles inverse reinforcement learning with feature mismatch, proposing a teaching scheme.
problem Learning near-optimal behavior from an expert's demonstrations when there's a mismatch between the learner and expert's worldviews.
method Introduces teaching risk to measure suboptimality and suggests a teaching scheme to decrease it.
result Standard algorithms based on inverse reinforcement learning can find near-optimal policies with bounds on teaching risk.
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to b…
Educational game on crypto investment helps students grasp macroeconomics.
problem Weak connections between microeconomic decision-making and macroeconomic concepts in classroom games.
method Design and study of an educational game on cryptocurrency investment.
result Engages students in understanding macroeconomics through incentivized individual investment decisions.
Teaches deep learning to statisticians.
problem Statisticians lack expertise in deep learning.
method Developed a program and taught DL to statistics graduate students.
result Provided tips and resources for teaching DL.
Compared to machines, humans are extremely good at classifying images into categories, especially when they possess prior knowledge of the categories at hand. If this prior information is not available, supervision in the form of teaching images is required. To learn categories more quickly, people should see important…
This work proves lower bounds on a greedy teaching set construction algorithm.
problem Characterize the best-case teaching dimension of a concept class.
method A greedy algorithm that iteratively adds points to the teaching set to restrict the concept class the most.
result Lower bounds on the performance of the greedy approach for small k, extending up to k ≤ c*d for small constant c.
In this paper, we make an important step towards the black-box machine teaching by considering the cross-space machine teaching, where the teacher and the learner use different feature representations and the teacher can not fully observe the learner's model. In such scenario, we study how the teacher is still able to …
Optimal control theory improves machine teaching efficiency.
problem Finding the shortest training sequence for a sequential learning algorithm to reach a target model.
method Formulated as a time-optimal control problem, leveraging optimal control theory and computational tools.
result Optimal training sequences can vastly outperform existing heuristics.
Interactive teaching speeds up IRL learning with adaptive demonstrations.
problem Tackles the challenge of accelerating IRL learning with teacher assistance.
method Interactive teaching framework where a teacher adapts demonstrations based on the learner's policy.
result Teaching algorithms converge in the omniscient setting, speeding up learning.
Teaching tool simplifies Monte Carlo simulation for project risk analysis.
problem Difficulty in students performing Monte Carlo Simulation in risk analysis.
method Introducing MCSimulRisk as a teaching tool.
result Students can perform Monte Carlo simulation and apply it to projects of any complexity.
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic i…
Mutual teaching improves graph models with less labeled data.
problem Training graph models with limited labeled data.
method Dual model training with mutual teaching strategy.
result Significant performance improvement with less labeled data.
This work introduces reward teaching for federated multi-armed bandits to guide clients towards global optimality.
problem Existing federated multi-armed bandits designs assume clients will follow the server's protocol, but this is not always feasible.
method Introduces reward teaching where the server adjusts clients' local rewards to encourage global optimality, using phased Teaching-After-Learning (TAL) and Teaching-While-Learning (TWL) algorithms.
result Demonstrates that TAL achieves logarithmic regrets with only logarithmic adjustment costs, and TWL outperforms TAL for UCB1 clients.
Teaches an active learner with contrastive examples to speed up learning.
problem Active learning with a helpful teacher and contrastive examples.
method Designs an adaptive teaching algorithm to provide informative contrastive examples.
result Strong performance guarantees and approximation guarantees for specific learners.
Teaches AI models to learn effectively through dynamic loss functions.
problem Optimizing machine learning models' performance through dynamic loss functions.
method Develops a method for a teacher model to dynamically output loss functions for a student model, enabling gradient-based optimization.
result Significantly improves the performance of various student models in real-world tasks.
Machines, not humans, are the world's dominant knowledge accumulators but humans remain the dominant decision makers. Interpreting and disseminating the knowledge accumulated by machines requires expertise, time, and is prone to failure. The problem of how best to convey accumulated knowledge from computers to humans i…
Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand the minimum set of demonstrations needed to teach a specific sequential decision-making task. We fo…
Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent with the true data distribution. However, for sequential learners which actively choose their queries,…
Self-teaching networks improve deep neural networks' generalization.
problem Improving deep neural networks' generalization capacity.
method Generates soft supervision labels to train lower layers, using an auxiliary loss to mimic the output layer.
result Self-teaching network achieves consistent improvements and outperforms existing methods in speech recognition tasks.
Learning with noisy labels is one of the hottest problems in weakly-supervised learning. Based on memorization effects of deep neural networks, training on small-loss instances becomes very promising for handling noisy labels. This fosters the state-of-the-art approach "Co-teaching" that cross-trains two deep neural ne…
MUTLA dataset analyzes multimodal data for teaching and learning analytics.
problem Lack of a comprehensive multimodal dataset for teaching and learning analytics.
method Presented a large-scale MUTLA dataset with synchronized multimodal data from SAIL.
result Provides insights for predicting student engagement and improving adaptive learning.
We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a …
We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better. We provide sharp super-teaching guarantees on two learners: the maximum likelihood estimator for the mean of a Gaussian, and the large margin classifier in 1D. For general learners, we provide a …
Average teaching complexity for locating target regions among halfspace intersections is Θ(d).
problem Teaching the location of a target region among intersections of halfspaces.
method Novel insights from computational geometry to count convex polytopes and faces.
result Average-case teaching complexity is Θ(d), contrasting with Θ(n) worst-case complexity.
Teaching models for concept learning infer concepts from observations.
problem Teaching models infer concepts from observations.
method Teaching models use MAP- and MLE-learners to infer concepts.
result The teaching dimension of a concept class can be bounded and computed in polynomial time.
Interactive tools help teach economics online.
problem Teaching macroeconomic models online.
method Browser-based simulation toolkits.
result Intuitive approach to comparative statics.
Teaches manifolds from teacher's structured data.
problem Learning a manifold from teacher's structured data.
method Extends existing approaches to learning from randomly sampled data points, considering structured data provided by a teacher.
result Demonstrations can significantly reduce data points needed for manifold learning.
Data Science is currently a popular field of science attracting expertise from very diverse backgrounds. Current learning practices need to acknowledge this and adapt to it. This paper summarises some experiences relating to such learning approaches from teaching a postgraduate Data Science module, and draws some learn…
Deep learning with noisy labels is practically challenging, as the capacity of deep models is so high that they can totally memorize these noisy labels sooner or later during training. Nonetheless, recent studies on the memorization effects of deep neural networks show that they would first memorize training data of cl…
Proposes a new curriculum learning approach to improve deep RL agent generalization.
problem Challenges in training deep RL agents to generalize over unseen situations.
method Two-stage curriculum learning approach: teacher learns high-exploration curriculum, distills priors to generate expert curriculum.
result 50% improvement in average performance over state-of-the-art methods.