Paper proposes JEDI teaching framework for adaptive crowd teaching.
problem Adaptive crowd teaching in crowdsourcing applications.
method Exponentially decayed memory model for teaching and balancing diversity and accuracy.
result JEDI teaching framework outperforms state-of-the-art techniques.
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.
This paper reflects on teaching Data Science to diverse students.
problem Adapting learning practices to diverse backgrounds in Data Science.
method Summarizes experiences from teaching a postgraduate Data Science module.
result Draws lessons relevant to teaching Data Science.
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.
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.
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.
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…
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.
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.
Robots' agility in changing terrain helps financial models adapt to market shifts.
problem Challenges in financial market forecasting due to regime switching.
method Adapts pretrained LLMs using intrinsic market rewards and reinforcement learning.
result Significantly improved accuracy in adapting to market regime shifts.
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.
AC-Teach uses an ensemble of suboptimal teachers to improve exploration in RL.
problem Improving exploration efficiency in long-horizon tasks with sparse rewards.
method Bayesian Actor-Critic with an ensemble of suboptimal teachers.
result AC-Teach improves sample efficiency over baselines on various tasks.
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.
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…
Machine teaching aims to democratize machine learning by making it easier for non-experts to teach machines.
problem Current machine learning systems require specialized knowledge, limiting their creation and use.
method Focus on making teaching machines accessible and easy, emphasizing teacher interaction and design principles.
result By decoupling knowledge about machine learning algorithms from teaching, innovation can accelerate and empower new uses.
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…
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.
Paper tackles black-box machine teaching with cross-space models, proposing an active teacher model.
problem Teaching a learner with different feature representations and without full observation.
method Proposes an active teacher model that queries the learner to estimate its status and guide faster convergence.
result Active teacher model achieves faster convergence rate than traditional passive learning.
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.
Machine teaches IRL with minimal demonstrations.
problem Finding the minimum set of demonstrations for IRL.
method Formalized as a machine teaching problem, reduced to set cover, approximated efficiently.
result Efficient algorithm for determining maximally informative demonstrations.
Teaches categories with visual explanations to improve learning.
problem Challenges of traditional machine teaching methods in providing clear explanations.
method Proposes a teaching framework that provides interpretable explanations as feedback.
result Participants achieve better test set performance with interpretable explanations.
Meta Pseudo Labels boosts image classification accuracy to 90.2%.
problem Improving semi-supervised learning for image classification.
method Adapts a teacher network to generate better pseudo labels through student feedback.
result Achieves a new state-of-the-art top-1 accuracy of 90.2% on ImageNet.
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.
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.
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.
ADVISOR dynamically balances imitation and reinforcement learning to overcome the imitation gap.
problem The gap between imitation learning and reinforcement learning when teaching agents have privileged information.
method Adaptive Insubordination (ADVISOR) dynamically weights imitation and reward-based reinforcement learning losses.
result On-the-fly switching with ADVISOR outperforms pure imitation, pure reinforcement learning, and their combinations.
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.
Improves learning efficiency for active sequential learners.
problem Optimizing training data for sequential learners who actively choose their queries.
method Formulated as a Markov decision process, addressing both teaching and learning from a teacher.
result Planning teaching and learner's model of the teacher improve learning outcomes.
Co-teaching improves deep neural networks' robustness against noisy labels.
problem Training deep neural networks with noisy labels is challenging.
method Simultaneously trains two deep networks, each selecting clean data, and communicates to update each other.
result Empirically superior to state-of-the-art methods in robustness.
A teacher can improve a learner's performance by selecting a smaller, more effective training subset.
problem Improving a learner's performance by selecting a smaller training subset.
method Sharp guarantees for two learners and a mixed-integer nonlinear programming-based algorithm for general learners.
result Empirical experiments show that the algorithm finds good super-teaching sets for regression and classification problems.
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…
New method uses explicit human demonstrations to teach missing features in reward learning.
problem Reward learning methods rely on handcrafted features, limiting their ability to adapt to new or unexplained corrections.
method Introduces human input guiding the robot from states with missing features to states without, teaching the feature explicitly and integrating it into the reward function.
result Decreases sample complexity and improves generalization of the learned reward over deep IRL baseline.
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…
Improved learning to reweight using deep interactions between student and teacher models.
problem Limitation of existing learning to reweight methods in utilizing student model's internal states.
method Proposes an algorithm that uses the student model's internal states to the teacher model, which returns adaptive weights to enhance student model training.
result Significant improvement over previous methods in image classification and neural machine translation experiments.
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.