research
∙
07/30/2023
Evaluating ChatGPT and GPT-4 for Visual Programming
Generative AI and large language models have the potential to drasticall...
share
research
∙
06/29/2023
Generative AI for Programming Education: Benchmarking ChatGPT, GPT-4, and Human Tutors
Generative AI and large language models hold great promise in enhancing ...
research
∙
06/05/2023
Learning Embeddings for Sequential Tasks Using Population of Agents
We present an information-theoretic framework to learn fixed-dimensional...
research
∙
05/27/2023
Synthesizing a Progression of Subtasks for Block-Based Visual Programming Tasks
Block-based visual programming environments play an increasingly importa...
research
∙
05/26/2023
Neural Task Synthesis for Visual Programming
Generative neural models hold great promise in enhancing programming edu...
research
∙
04/25/2023
Proximal Curriculum for Reinforcement Learning Agents
We consider the problem of curriculum design for reinforcement learning ...
research
∙
03/28/2023
Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes
Block-based programming environments are increasingly used to introduce ...
research
∙
01/24/2023
Generating High-Precision Feedback for Programming Syntax Errors using Large Language Models
Large language models (LLMs), such as Codex, hold great promise in enhan...
research
∙
11/18/2022
Provable Defense against Backdoor Policies in Reinforcement Learning
We propose a provable defense mechanism against backdoor policies in rei...
research
∙
06/13/2022
Specifying and Testing k-Safety Properties for Machine-Learning Models
Machine-learning models are becoming increasingly prevalent in our lives...
research
∙
05/04/2022
Equity and Fairness of Bayesian Knowledge Tracing
We consider the equity and fairness of curricula derived from Knowledge ...
research
∙
05/03/2022
From Solution Synthesis to Student Attempt Synthesis for Block-Based Visual Programming Tasks
Block-based visual programming environments are increasingly used to int...
research
∙
04/01/2022
Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes
Actual causality and a closely related concept of responsibility attribu...
research
∙
01/06/2022
Admissible Policy Teaching through Reward Design
We study reward design strategies for incentivizing a reinforcement lear...
research
∙
10/28/2021
Teaching an Active Learner with Contrastive Examples
We study the problem of active learning with the added twist that the le...
research
∙
10/22/2021
Fairness Degrading Adversarial Attacks Against Clustering Algorithms
Clustering algorithms are ubiquitous in modern data science pipelines, a...
research
∙
09/23/2021
Reinforcement Learning Under Algorithmic Triage
Methods to learn under algorithmic triage have predominantly focused on ...
research
∙
07/26/2021
On Blame Attribution for Accountable Multi-Agent Sequential Decision Making
Blame attribution is one of the key aspects of accountable decision maki...
research
∙
07/15/2021
Reinforcement Learning for Education: Opportunities and Challenges
This survey article has grown out of the RL4ED workshop organized by the...
research
∙
06/09/2021
Bayesian Persuasion in Sequential Decision-Making
We study a dynamic model of Bayesian persuasion in sequential decision-m...
research
∙
06/08/2021
Curriculum Design for Teaching via Demonstrations: Theory and Applications
We consider the problem of teaching via demonstrations in sequential dec...
research
∙
06/01/2021
Fair Clustering Using Antidote Data
Clustering algorithms are widely utilized for many modern data science a...
research
∙
05/10/2021
Loss-Aversively Fair Classification
The use of algorithmic (learning-based) decision making in scenarios tha...
research
∙
02/16/2021
Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments
We study black-box reward poisoning attacks against reinforcement learni...
research
∙
02/10/2021
Defense Against Reward Poisoning Attacks in Reinforcement Learning
We study defense strategies against reward poisoning attacks in reinforc...
research
∙
11/21/2020
Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks
We study a security threat to reinforcement learning where an attacker p...
research
∙
10/17/2020
Preference-Based Batch and Sequential Teaching
Algorithmic machine teaching studies the interaction between a teacher a...
research
∙
06/25/2020
Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries
We examine the task of locating a target region among those induced by i...
research
∙
06/23/2020
Environment Shaping in Reinforcement Learning using State Abstraction
One of the central challenges faced by a reinforcement learning (RL) age...
research
∙
06/17/2020
Synthesizing Tasks for Block-based Programming
Block-based visual programming environments play a critical role in intr...
research
∙
06/16/2020
Task-agnostic Exploration in Reinforcement Learning
Efficient exploration is one of the main challenges in reinforcement lea...
research
∙
06/16/2020
The Teaching Dimension of Q-learning
In this paper, we initiate the study of sample complexity of teaching, t...
research
∙
03/28/2020
Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning
We study a security threat to reinforcement learning where an attacker p...
research
∙
03/27/2020
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
In reward-poisoning attacks against reinforcement learning (RL), an atta...
research
∙
03/21/2020
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Machine teaching studies the interaction between a teacher and a student...
research
∙
02/11/2020
Learning to Switch Between Machines and Humans
Reinforcement learning algorithms have been mostly developed and evaluat...
research
∙
10/24/2019
Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models
Algorithmic machine teaching studies the interaction between a teacher a...
research
∙
10/05/2019
Towards Deployment of Robust AI Agents for Human-Machine Partnerships
We study the problem of designing AI agents that can robustly cooperate ...
research
∙
09/01/2019
Can A User Guess What Her Followers Want?
Whenever a social media user decides to share a story, she is typically ...
research
∙
06/02/2019
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Inverse reinforcement learning (IRL) enables an agent to learn complex b...
research
∙
05/28/2019
Interactive Teaching Algorithms for Inverse Reinforcement Learning
We study the problem of inverse reinforcement learning (IRL) with the ad...
research
∙
05/22/2019
Optimal Decision Making Under Strategic Behavior
We are witnessing an increasing use of data-driven predictive models to ...
research
∙
05/16/2019
On the Fairness of Time-Critical Influence Maximization in Social Networks
Influence maximization has found applications in a wide range of real-wo...
research
∙
02/27/2019
Unifying Ensemble Methods for Q-learning via Social Choice Theory
Ensemble methods have been widely applied in Reinforcement Learning (RL)...
research
∙
01/23/2019
Learning to Collaborate in Markov Decision Processes
We consider a two-agent MDP framework where agents repeatedly solve a ta...
research
∙
12/11/2018
Efficient learning of smooth probability functions from Bernoulli tests with guarantees
We study the fundamental problem of learning an unknown, smooth probabil...
research
∙
11/08/2018
Iterative Classroom Teaching
We consider the machine teaching problem in a classroom-like setting whe...
research
∙
10/21/2018
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Learning near-optimal behaviour from an expert's demonstrations typicall...
research
∙
07/02/2018
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Discrimination via algorithmic decision making has received considerable...
research
∙
05/25/2018