Timezone: »
Basic decisions, such as judging a person as a friend or foe, involve categorizing novel stimuli. Recent work finds that people’s category judgments are guided by a small set of examples that are retrieved from memory at decision time. This limited and stochastic retrieval places limits on human performance for probabilistic classification decisions. In light of this capacity limitation, recent work finds that idealizing training items, such that the saliency of ambiguous cases is reduced, improves human performance on novel test items. One shortcoming of previous work in idealization is that category distributions were idealized in an ad hoc or heuristic fashion. In this contribution, we take a first principles approach to constructing idealized training sets. We apply a machine teaching procedure to a cognitive model that is either limited capacity (as humans are) or unlimited capacity (as most machine learning systems are). As predicted, we find that the machine teacher recommends idealized training sets. We also find that human learners perform best when training recommendations from the machine teacher are based on a limited-capacity model. As predicted, to the extent that the learning model used by the machine teacher conforms to the true nature of human learners, the recommendations of the machine teacher prove effective. Our results provide a normative basis (given capacity constraints) for idealization procedures and offer a novel selection procedure for models of human learning.
Author Information
Kaustubh R Patil (Affective Brain Lab)
Jerry Zhu (University of Wisconsin-Madison)
Łukasz Kopeć (University College London)
Bradley C Love (University College London)
Related Events (a corresponding poster, oral, or spotlight)
-
2014 Spotlight: Optimal Teaching for Limited-Capacity Human Learners »
Thu. Dec 11th 05:00 -- 05:25 PM Room Level 2, room 210
More from the Same Authors
-
2021 : Game Redesign in No-regret Game Playing »
Yuzhe Ma · Young Wu · Jerry Zhu -
2021 : Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments »
Amin Rakhsha · Xuezhou Zhang · Jerry Zhu · Adish Singla -
2021 : Game Redesign in No-regret Game Playing »
Yuzhe Ma · Young Wu · Jerry Zhu -
2021 : Reward Poisoning in Reinforcement Learning: Attacks Against Unknown Learners in Unknown Environments »
Amin Rakhsha · Xuezhou Zhang · Jerry Zhu · Adish Singla -
2023 Poster: Mechanism Design for Collaborative Normal Mean Estimation »
Yiding Chen · Jerry Zhu · Kirthevasan Kandasamy -
2023 Poster: Dream the Impossible: Outlier Imagination with Diffusion Models »
Xuefeng Du · Yiyou Sun · Jerry Zhu · Yixuan Li -
2022 Poster: Provable Defense against Backdoor Policies in Reinforcement Learning »
Shubham Bharti · Xuezhou Zhang · Adish Singla · Jerry Zhu -
2019 Poster: Policy Poisoning in Batch Reinforcement Learning and Control »
Yuzhe Ma · Xuezhou Zhang · Wen Sun · Jerry Zhu -
2019 Poster: Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models »
Farnam Mansouri · Yuxin Chen · Ara Vartanian · Jerry Zhu · Adish Singla -
2019 Poster: A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning »
Xuanqing Liu · Si Si · Jerry Zhu · Yang Li · Cho-Jui Hsieh -
2018 Poster: Adversarial Attacks on Stochastic Bandits »
Kwang-Sung Jun · Lihong Li · Yuzhe Ma · Jerry Zhu -
2017 Workshop: Teaching Machines, Robots, and Humans »
Maya Cakmak · Anna Rafferty · Adish Singla · Jerry Zhu · Sandra Zilles -
2016 : Optimal Teaching for Online Perceptrons »
Xuezhou Zhang · Jerry Zhu -
2016 Workshop: The Future of Interactive Machine Learning »
Kory Mathewson @korymath · Kaushik Subramanian · Mark Ho · Robert Loftin · Joseph L Austerweil · Anna Harutyunyan · Doina Precup · Layla El Asri · Matthew Gombolay · Jerry Zhu · Sonia Chernova · Charles Isbell · Patrick M Pilarski · Weng-Keen Wong · Manuela Veloso · Julie A Shah · Matthew Taylor · Brenna Argall · Michael Littman -
2016 Poster: Active Learning with Oracle Epiphany »
Tzu-Kuo Huang · Lihong Li · Ara Vartanian · Saleema Amershi · Jerry Zhu -
2015 Poster: Human Memory Search as Initial-Visit Emitting Random Walk »
Kwang-Sung Jun · Jerry Zhu · Timothy T Rogers · Zhuoran Yang · Ming Yuan -
2013 Poster: Machine Teaching for Bayesian Learners in the Exponential Family »
Jerry Zhu -
2011 Poster: How Do Humans Teach: On Curriculum Learning and Teaching Dimension »
Faisal Khan · Jerry Zhu · Bilge Mutlu -
2011 Poster: Learning Higher-Order Graph Structure with Features by Structure Penalty »
Shilin Ding · Grace Wahba · Jerry Zhu -
2010 Oral: Humans Learn Using Manifolds, Reluctantly »
Bryan R Gibson · Jerry Zhu · Timothy T Rogers · Chuck Kalish · Joseph Harrison -
2010 Poster: Humans Learn Using Manifolds, Reluctantly »
Bryan R Gibson · Jerry Zhu · Timothy T Rogers · Chuck Kalish · Joseph Harrison -
2010 Poster: Transduction with Matrix Completion: Three Birds with One Stone »
Andrew B Goldberg · Jerry Zhu · Benjamin Recht · Junming Sui · Rob Nowak -
2010 Session: Spotlights Session 1 »
Jerry Zhu -
2009 Poster: Human Rademacher Complexity »
Jerry Zhu · Timothy T Rogers · Bryan R Gibson -
2008 Workshop: Machine learning meets human learning »
Nathaniel D Daw · Tom Griffiths · Josh Tenenbaum · Jerry Zhu -
2008 Poster: Human Active Learning »
Jerry Zhu · Rui M Castro · Timothy T Rogers · Rob Nowak · Ruichen Qian · Chuck Kalish -
2008 Poster: Unlabeled data: Now it helps, now it doesn't »
Aarti Singh · Rob Nowak · Jerry Zhu -
2008 Oral: Unlabeled data: Now it helps, now it doesn't »
Aarti Singh · Rob Nowak · Jerry Zhu