Adish Singla
69 publications
13 venues
H Index 22
Affiliation
Max Planck Institute for Software Systems, GermanyLinks
| Name | Venue | Year | citations |
|---|---|---|---|
Policy Teaching via Data Poisoning in Learning from Human Preferences.
|
AISTATS | 2025 | 1 |
| Text-Diffusion Red-Teaming of Large Language Models: Unveiling Harmful Behaviors with Proximity Constraints.
|
AAAI | 2025 | 4 |
| Program Synthesis Benchmark for Visual Programming in XLogoOnline Environment.
|
ACL | 2025 | 0 |
| Corruption Robust Offline Reinforcement Learning with Human Feedback.
|
AISTATS | 2025 | 0 |
| Corruption-Robust Offline Two-Player Zero-Sum Markov Games.
|
AISTATS | 2024 | 3 |
| Informativeness of Reward Functions in Reinforcement Learning.
|
AAMAS | 2024 | 2 |
| Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences.
|
ICML | 2024 | 20 |
| Benchmarking Generative Models on Computational Thinking Tests in Elementary Visual Programming.
|
NIPS/NeurIPS | 2024 | 5 |
| On the Complexity of Teaching a Family of Linear Behavior Cloning Learners.
|
NIPS/NeurIPS | 2024 | 1 |
| Proximal Curriculum with Task Correlations for Deep Reinforcement Learning.
|
IJCAI | 2024 | 7 |
| Hints-In-Browser: Benchmarking Language Models for Programming Feedback Generation.
|
NIPS/NeurIPS | 2024 | 14 |
| Learning Embeddings for Sequential Tasks Using Population of Agents.
|
IJCAI | 2024 | 0 |
| Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks.
|
AAMAS | 2023 | 12 |
| Online Defense Strategies for Reinforcement Learning Against Adaptive Reward Poisoning.
|
AISTATS | 2023 | 6 |
| Online Reinforcement Learning with Uncertain Episode Lengths.
|
AAAI | 2023 | 9 |
| Specifying and Testing k-Safety Properties for Machine-Learning Models.
|
IJCAI | 2023 | 0 |
| Actual Causality and Responsibility Attribution in Decentralized Partially Observable Markov Decision Processes.
|
AIES | 2022 | 15 |
| Envy-free Policy Teaching to Multiple Agents.
|
NIPS/NeurIPS | 2022 | 0 |
| On Batch Teaching with Sample Complexity Bounded by VCD.
|
NIPS/NeurIPS | 2022 | 6 |
| Admissible Policy Teaching through Reward Design.
|
AAAI | 2022 | 16 |
| Provable Defense against Backdoor Policies in Reinforcement Learning.
|
NIPS/NeurIPS | 2022 | 29 |
| Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards.
|
NIPS/NeurIPS | 2022 | 96 |
| Bayesian Persuasion in Sequential Decision-Making.
|
AAAI | 2022 | 0 |
| The Sample Complexity of Teaching by Reinforcement on Q-Learning.
|
AAAI | 2021 | 14 |
| Curriculum Design for Teaching via Demonstrations: Theory and Applications.
|
NIPS/NeurIPS | 2021 | 10 |
| Explicable Reward Design for Reinforcement Learning Agents.
|
NIPS/NeurIPS | 2021 | 53 |
| Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm.
|
NIPS/NeurIPS | 2021 | 3 |
| Teaching an Active Learner with Contrastive Examples.
|
NIPS/NeurIPS | 2021 | 15 |
| On Blame Attribution for Accountable Multi-Agent Sequential Decision Making.
|
NIPS/NeurIPS | 2021 | 14 |
| The Teaching Dimension of Kernel Perceptron.
|
AISTATS | 2021 | 0 |
| Policy Teaching in Reinforcement Learning via Environment Poisoning Attacks.
|
JMLR | 2021 | 0 |
| Towards Deployment of Robust Cooperative AI Agents: An Algorithmic Framework for Learning Adaptive Policies.
|
AAMAS | 2020 | 19 |
| Understanding the Power and Limitations of Teaching with Imperfect Knowledge.
|
IJCAI | 2020 | 50 |
| Adaptive Reward-Poisoning Attacks against Reinforcement Learning.
|
ICML | 2020 | 0 |
| Synthesizing Tasks for Block-based Programming.
|
NIPS/NeurIPS | 2020 | 25 |
| Task-agnostic Exploration in Reinforcement Learning.
|
NIPS/NeurIPS | 2020 | 53 |
| Policy Teaching via Environment Poisoning: Training-time Adversarial Attacks against Reinforcement Learning.
|
ICML | 2020 | 141 |
| Can A User Guess What Her Followers Want?
|
WSDM | 2020 | 0 |
| Learning to Collaborate in Markov Decision Processes.
|
ICML | 2019 | 34 |
| Interactive Teaching Algorithms for Inverse Reinforcement Learning.
|
IJCAI | 2019 | 64 |
| Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints.
|
NIPS/NeurIPS | 2019 | 44 |
| Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models.
|
NIPS/NeurIPS | 2019 | 34 |
| Iterative Classroom Teaching.
|
AAAI | 2019 | 0 |
| Efficient learning of smooth probability functions from Bernoulli tests with guarantees.
|
ICML | 2019 | 0 |
| Loss-Aversively Fair Classification.
|
AIES | 2019 | 0 |
| Teaching Multiple Concepts to a Forgetful Learner.
|
NIPS/NeurIPS | 2019 | 0 |
| Enhancing the Accuracy and Fairness of Human Decision Making.
|
NIPS/NeurIPS | 2018 | 37 |
| Learning to Interact With Learning Agents.
|
AAAI | 2018 | 12 |
| Teaching Inverse Reinforcement Learners via Features and Demonstrations.
|
NIPS/NeurIPS | 2018 | 51 |
| A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual &Group Unfairness via Inequality Indices.
|
KDD | 2018 | 288 |
| Understanding the Role of Adaptivity in Machine Teaching: The Case of Version Space Learners.
|
NIPS/NeurIPS | 2018 | 49 |
| Information Gathering With Peers: Submodular Optimization With Peer-Prediction Constraints.
|
AAAI | 2018 | 0 |
| Learning User Preferences to Incentivize Exploration in the Sharing Economy.
|
AAAI | 2018 | 0 |
| Selecting Sequences of Items via Submodular Maximization.
|
AAAI | 2017 | 57 |
| Actively Learning Hemimetrics with Applications to Eliciting User Preferences.
|
ICML | 2016 | 17 |
| Noisy Submodular Maximization via Adaptive Sampling with Applications to Crowdsourced Image Collection Summarization.
|
AAAI | 2016 | 0 |
| Building Hierarchies of Concepts via Crowdsourcing.
|
IJCAI | 2015 | 40 |
| Incentivizing Users for Balancing Bike Sharing Systems.
|
AAAI | 2015 | 216 |
| Information Gathering in Networks via Active Exploration.
|
IJCAI | 2015 | 15 |
| Stochastic Privacy.
|
AAAI | 2014 | 15 |
| Near-Optimally Teaching the Crowd to Classify.
|
ICML | 2014 | 131 |
| Enhancing personalization via search activity attribution.
|
SIGIR | 2014 | 8 |
| From devices to people: attribution of search activity in multi-user settings.
|
WWW | 2014 | 19 |
| Truthful incentives in crowdsourcing tasks using regret minimization mechanisms.
|
WWW | 2013 | 297 |
| A noise-aware click model for web search.
|
WSDM | 2012 | 23 |
| Studying trailfinding algorithms for enhanced web search.
|
SIGIR | 2010 | 63 |
| Tagging and navigability.
|
WWW | 2010 | 4 |
| Sampling high-quality clicks from noisy click data.
|
WWW | 2010 | 13 |
| Camera brand congruence in the Flickr social graph.
|
WSDM | 2009 | 17 |