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Author Information
Sanmi Koyejo (Stanford University)

Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University. Koyejo was previously an Associate Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in developing the principles and practice of trustworthy machine learning, focusing on applications to neuroscience and healthcare. Koyejo completed a Ph.D. in Electrical Engineering at the University of Texas at Austin, advised by Joydeep Ghosh, and postdoctoral research at Stanford University with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards, including a best paper award from the conference on uncertainty in artificial intelligence, a Skip Ellis Early Career Award, a Sloan Fellowship, a Terman faculty fellowship, an NSF CAREER award, a Kavli Fellowship, an IJCAI early career spotlight, and a trainee award from the Organization for Human Brain Mapping. Koyejo spends time at Google as a part of the Brain team, serves on the Neural Information Processing Systems Foundation Board, the Association for Health Learning and Inference Board, and as president of the Black in AI organization.
Samy Bengio (Apple MLR)
Ashia Wilson (MIT)
Kirikowhai Mikaere (Te Kāhui Raraunga Aotearoa)

Ms Kirikowhai Mikaere (Te Arawa - Tūhourangi, Ngāti Whakaue) Ms Mikaere is a leading Māori data and information specialist focused on harnessing information to empower indigenous community development. She is a consultant with over 20 years’ experience advising Ministers, government agencies, tribal, community and private sector organisations with practical statistical analysis and innovative place based data solutions. Ms Mikaere is currently the lead technical advisor to the Aotearoa New Zealand National Iwi (Tribal) Chairs Forum - Data Leadership Group, leads the independent trust Te Kāhui Raraunga and holds governance positions across the private sector and government, including with her tribe (Chair - Tūhourangi Tribal Authority, Trustee – Te Pumautanga o Te Arawa), Māori Health provider Manaaki Ora Trust (Deputy Chair), the New Zealand 2023 Census Programme Board, and is a Ministerial appointed member of the New Zealand Science Board.
Joelle Pineau (McGill University / Facebook)
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2022 : Adapting to Shifts in Latent Confounders via Observed Concepts and Proxies »
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2023 : Layer-Wise Feedback Alignment is Conserved in Deep Neural Networks »
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2023 : FACADE: A Framework for Adversarial Circuit Anomaly Detection and Evaluation »
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2023 : Is Pre-training Truly Better Than Meta-Learning? »
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2023 : Leveraging Side Information for Communication-Efficient Federated Learning »
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2023 : Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting »
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2023 : GPT-Zip: Deep Compression of Finetuned Large Language Models »
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2023 : Beyond Scale: the Diversity Coefficient as a Data Quality Metric Demonstrates LLMs are Pre-trained on Formally Diverse Data »
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2023 : Are Emergent Abilities of Large Language Models a Mirage? »
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2023 : Thomas: Learning to Explore Human Preference via Probabilistic Reward Model »
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2023 : On learning domain general predictors »
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2023 : Fostering Women's Leadership in the Realm of Emerging Trends and Technologies »
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2023 : Panel on Reasoning Capabilities of LLMs »
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2023 : Deceptive Alignment Monitoring »
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2023 : Vignettes on Pairwise-Feedback Mechanisms for Learning with Uncertain Preferences »
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2023 : Joelle Pineau - A culture of open and reproducible research, in the era of large AI generative models »
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2023 : Generalization on the Unseen, Logic Reasoning and Degree Curriculum »
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2023 Workshop: 2nd ICML Workshop on New Frontiers in Adversarial Machine Learning »
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2023 Poster: Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions »
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2023 Poster: Generalization on the Unseen, Logic Reasoning and Degree Curriculum »
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2023 Oral: Generalization on the Unseen, Logic Reasoning and Degree Curriculum »
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2023 Poster: Accelerated Stochastic Optimization Methods under Quasar-convexity »
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2022 Workshop: New Frontiers in Adversarial Machine Learning »
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2022 Poster: Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization »
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2022 Spotlight: Adversarially Robust Models may not Transfer Better: Sufficient Conditions for Domain Transferability from the View of Regularization »
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2021 : Descent method framework in optimization »
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2021 Workshop: ICML 2021 Workshop on Unsupervised Reinforcement Learning »
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2021 Poster: Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability »
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2021 Poster: Optimizing Black-box Metrics with Iterative Example Weighting »
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2021 Spotlight: Optimizing Black-box Metrics with Iterative Example Weighting »
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2021 Poster: OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimation »
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2020 Poster: Interference and Generalization in Temporal Difference Learning »
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2020 Poster: On the consistency of top-k surrogate losses »
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2020 Poster: Invariant Causal Prediction for Block MDPs »
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2020 Poster: Optimization and Analysis of the pAp@k Metric for Recommender Systems »
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2020 Poster: Zeno++: Robust Fully Asynchronous SGD »
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2020 Affinity Workshop: New In ML »
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2019 Workshop: Identifying and Understanding Deep Learning Phenomena »
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2019 Workshop: Generative Modeling and Model-Based Reasoning for Robotics and AI »
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2019 Poster: Partially Linear Additive Gaussian Graphical Models »
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2019 Oral: Partially Linear Additive Gaussian Graphical Models »
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2019 Poster: Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance »
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2019 Oral: Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance »
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2019 Poster: Area Attention »
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2019 Oral: Area Attention »
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2018 Poster: Binary Classification with Karmic, Threshold-Quasi-Concave Metrics »
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2017 : Joelle Pineau: A few modest insights from my lifelong learning »
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