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(43 events)   Time zone: Conference local time zone  
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The 2021 schedule is still incomplete
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Sat Jul 24 01:15 PM -- 01:30 PM (CEST)
Opening Remark
Shanghang Zhang · Shiji Zhou
Sat Jul 24 01:30 PM -- 02:00 PM (CEST)
Invited Talk #0
Sat Jul 24 02:00 PM -- 02:30 PM (CEST)
Invited Talk #1
Yarin Gal
Sat Jul 24 02:30 PM -- 03:00 PM (CEST)
Invited Talk #2
Hugo Larochelle
Sat Jul 24 03:00 PM -- 03:10 PM (CEST)
Q&A
Sat Jul 24 03:10 PM -- 03:40 PM (CEST)
Invited Talk #3
Vittorio Ferrari
Sat Jul 24 03:40 PM -- 04:10 PM (CEST)
Invited Talk #4
Razvan Pascanu
Sat Jul 24 04:10 PM -- 04:20 PM (CEST)
Q&A
Sat Jul 24 04:20 PM -- 05:20 PM (CEST)
Poster
Shiji Zhou · Nastaran Okati · Wichinpong Sinchaisri · Kim de Bie · Ana Lucic · Mina Khan · Ishaan Shah · JINGHUI LU · Andreas Kirsch · Julius Frost · Ze Gong · Gokul Swamy · Ah Young Kim · Ahmed Baruwa · Ranganath Krishnan
Sat Jul 24 05:20 PM -- 05:50 PM (CEST)
Invited Talk #5
Fei Sha
Sat Jul 24 05:50 PM -- 06:20 PM (CEST)
Invited Talk #6
Ranjay Krishna
Sat Jul 24 06:20 PM -- 06:30 PM (CEST)
Q&A
Sat Jul 24 06:30 PM -- 07:00 PM (CEST)
Invited Talk #7
Kimin Lee
Sat Jul 24 07:00 PM -- 08:00 PM (CEST)
Panel Discussion
Sat Jul 24 08:00 PM -- 08:30 PM (CEST)
Invited Talk #8
Alison Gopnik
Sat Jul 24 08:30 PM -- 08:50 PM (CEST)
Closing Remarks
Mitigating Sampling Bias and Improving Robustness in Active Learning
Ranganath Krishnan · Alok Sinha · Nilesh Ahuja · Mahesh Subedar · Omesh Tickoo · Ravi Iyer
Accelerating the Convergence of Human-in-the-Loop Reinforcement Learning with Counterfactual Explanations
Jakob Karalus · Felix Lindner
Explicable Policy Search via Preference-Based Learning under Human Biases
Ze Gong · Yu Zhang
Explaining Reinforcement Learning Policies through Counterfactual Trajectories
Julius Frost · Olivia Watkins · Eric Weiner · Pieter Abbeel · Trevor Darrell · Bryan Plummer · Kate Saenko
Effect of Combination of HBM and Certainty Sampling onWorkload of Semi-Automated Grey Literature Screening
JINGHUI LU · Brian Mac Namee
Personalizing Pretrained Models
Mina Khan · Advait Rane · Pattie Maes
Interpretable Machine Learning: Moving From Mythos to Diagnostics
Valerie Chen · Jeffrey Li · Joon Kim · Gregory Plumb · Ameet Talwalkar
Less is more: An Empirical Analysis of Model Compression for Dialogue
Ahmed Baruwa
Differentiable Learning Under Triage
Nastaran Okati · Abir De · Manuel Gomez-Rodriguez
IADA: Iterative Adversarial Data Augmentation Using Formal Verification and Expert Guidance
Ruixuan Liu · Changliu Liu
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Yuanfei Wang · Fangwei Zhong · Jing Xu · Yizhou Wang
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
Kim de Bie · Ana Lucic · Hinda Haned
High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm
Lorena Qendro · Alex Campbell · Pietro Lió · Cecilia Mascolo
Interpretable Video Transformers in Imitation Learning of Human Driving
Andrew Dai
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks
Ana Lucic · Maartje ter Hoeve · Gabriele Tolomei · Maarten de Rijke · Fabrizio Silvestri
Shared Interest: Large-Scale Visual Analysis of Model Behavior by Measuring Human-AI Alignment
Angie Boggust · Benjamin Hoover · Arvind Satyanarayan · Hendrik Strobelt
Machine Teaching with Generative Models for Human Learning
Michael Doron · Hussein Mozannar · David Sontag · Juan Caicedo
PreferenceNet: Encoding Human Preferences in Auction Design
Neehar Peri · Michael Curry · Samuel Dooley · John P Dickerson
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models.
Zeyad Emam · Sasha Harrison · Felix Lau · Ah Young Kim
Active Learning under Pool Set Distribution Shift and Noisy Data
Andreas Kirsch · Tom Rainforth · Yarin Gal
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback
Ishaan Shah · David Halpern · Michael L. Littman · Kavosh Asadi
Improving Human Decision-Making with Machine Learning
Hamsa Bastani · Osbert Bastani · Wichinpong Sinchaisri
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos
Haoyu Xiong · Yun-Chun Chen · Homanga Bharadhwaj · Samrath Sinha · Animesh Garg
Differentially Private Active Learning with Latent Space Optimization
Senching Cheung · Xiaoqing Zhu · Herb Wildfeuer · Chongruo Wu · Wai-tian Tan
Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap
Gokul Swamy · Sanjiban Choudhury · James Bagnell · Steven Wu
A Simple Baseline for Batch Active Learning with Stochastic Acquisition Functions
Andreas Kirsch · Sebastian Farquhar · Yarin Gal
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks
Lucie Charlotte Magister · Dmitry Kazhdan · Vikash Singh · Pietro Lió