Toggle Poster Visibility
Sat Jul 24 04:15 AM -- 04:30 AM (PDT)
Opening Remark
Sat Jul 24 04:30 AM -- 05:00 AM (PDT)
Invited Talk #0
Sat Jul 24 05:00 AM -- 05:30 AM (PDT)
Invited Talk #1
Sat Jul 24 05:30 AM -- 06:00 AM (PDT)
Invited Talk #2
Sat Jul 24 06:10 AM -- 06:40 AM (PDT)
Invited Talk #3
Sat Jul 24 06:40 AM -- 07:10 AM (PDT)
Invited Talk #4
Sat Jul 24 08:20 AM -- 08:50 AM (PDT)
Invited Talk #5
Sat Jul 24 08:50 AM -- 09:20 AM (PDT)
Invited Talk #6
Sat Jul 24 09:30 AM -- 10:00 AM (PDT)
Invited Talk #7
Sat Jul 24 10:00 AM -- 11:00 AM (PDT)
Panel Discussion
Sat Jul 24 11:00 AM -- 11:30 AM (PDT)
Invited Talk #8
Sat Jul 24 11:30 AM -- 11:50 AM (PDT)
Closing Remarks
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
Explicable Policy Search via Preference-Based Learning under Human Biases
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions
Effect of Combination of HBM and Certainty Sampling onWorkload of Semi-Automated Grey Literature Screening
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Shared Interest: Large-Scale Visual Analysis of Model Behavior by Measuring Human-AI Alignment
IADA: Iterative Adversarial Data Augmentation Using Formal Verification and Expert Guidance
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
Machine Teaching with Generative Models for Human Learning
PreferenceNet: Encoding Human Preferences in Auction Design
Less is more: An Empirical Analysis of Model Compression for Dialogue
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models.
Active Learning under Pool Set Distribution Shift and Noisy Data
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Improving Human Decision-Making with Machine Learning
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Differentially Private Active Learning with Latent Space Optimization
Interpretable Video Transformers in Imitation Learning of Human Driving
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Mitigating Sampling Bias and Improving Robustness in Active Learning
Accelerating the Convergence of Human-in-the-Loop Reinforcement Learning with Counterfactual Explanations
Successful Page Load