December 2022 - talks - lists.cs.princeton.edu

Paul Krueger will present his FPO "Metacognition: toward a computational framework for improving our minds" on Wednesday, January 18, 2023 at 2:00 PM in CS 402.
by Louis Riehl 22 Dec '22

22 Dec '22

Paul Krueger will present his FPO "Metacognition: toward a computational framework for improving our minds" on Wednesday, January 18, 2023 at 2:00 PM in CS 402. Location: CS 402 The members of Paul's committee are as follows: Examiners: Tom Griffiths (Adviser), Jonathan Cohen, Ryan Adams Readers: Karthik Narasimhan, Nathaniel Daw A copy of his thesis will be available two weeks before the FPO upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Reinforcement learning has been used successfully to account for human decision-making and to produce intelligent behavior in artificial agents. However, people's internal cognitive processes generally remain obscure with these models. The same formalism used to account for external actions can also be applied to cognitive processes themselves. In particular, using the recently developed framework of resource rationality we can describe how people make efficient use of limited cognitive resources. Using this approach, we apply tools from machine learning to derive a normative account of how people ought to think efficiently. As a proof of concept, I will show how this framework can be used to automatically derive decision-making heuristics. I will present results from a classic multi-alternative risky choice task that allows us to trace people's cognitive processes. Using our method, we both re-discovered previously known heuristics, and discovered heuristics that had been overlooked. We found that people did indeed use the same heuristics that our method predicted, and furthermore adapted their heuristics to the structure of the decision environment in a rational way. Our approach bridges classic rationality with research on heuristics and biases by providing a general framework that can be used to apply rationality to any decision-making process while accounting for heuristics, thus providing a more realistic normative standard. In the second part of the talk, I will discuss a new framework for how model-based and model-free reinforcement learning systems can interact cooperatively, and the potential to account for human decision-making with this approach. In particular, this cooperation may be used to mediate the tension between goals and habits. I will close by discussing ongoing work to develop tools to solve meta-MDPs, the formalism used for our resource-rational approach.

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Rachit Dubey will present his FPO "The successes and failures of human drives" on Friday, January 13, 2023 at 10:00 AM in CS 402.
by CS Grad Department 13 Dec '22

13 Dec '22

Rachit Dubey will present his FPO "The successes and failures of human drives" on Friday, January 13, 2023 at 10:00 AM in CS 402. Location: CS 402 The members of Rachit’s committee are as follows: Examiners: Tom Griffiths (Adviser), Ryan Adams, Jonathan Cohen Readers: Tania Lombrozo, Karthik Narasimhan A copy of his thesis is available upon request. Please email gradinfo(a)cs.princeton.edu if you would like a copy of the thesis. Everyone is invited to attend his talk. Abstract follows below: Even in the absence of external rewards, we have internal motives that drive us to acquire information, pursue tasks, learn new things, etc. What is it that drives us? Under what conditions do these drives become maladaptive? In this dissertation, I employ computational modeling, behavioral experiments, and agent-based simulations to help develop a more complete picture of our intrinsic drives and motivations. In Chapter 2, I present a rational account of curiosity that unifies previous distinct theories in a single framework and explains a wide range of findings about human curiosity. Based on the insights from this framework, in Chapter 3, I present a behavioral intervention that can pique people’s curiosity for everyday scientific topics. Chapter 4 develops a computational model of Aha! moments and provides an explanation for why Aha! moments feel so rewarding. In Chapter 5, using the computational framework of reinforcement learning and the idea of reward design, I study the human drive to keep wanting more. I show that even though this seemingly maladaptive drive leads to unhappiness and overconsumption, it nevertheless plays an important role in promoting adaptive behavior and might be a deeply rooted bias of the human mind. Finally, in Chapter 6, I present an intervention that targets the wealthy and uses non-material social incentives to reduce their water consumption levels. Taken together, this work makes progress towards understanding the origins, strengths, and shortcomings of human drives, and illuminates the psychological forces that shape human behavior and suggests new ways to guide them.

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TODAY at 10am: Tyler Park Dissertation Defense (301 Computer Science Bldg)
by Nicki Mahler 07 Dec '22

07 Dec '22

The Lewis-Sigler Institute for Integrative Genomics ~ Graduate Program in Quantitative and Computational Biology Final Public Oral Tyler Park Integrating multi-modal biological data uncovers vulnerabilities and complex interactions in cancer Wednesday, December 7, 2022 10:00 AM Computer science building, room 301 Raphael Lab A copy of the dissertation is available to QCB for inspection electronically; email [ mailto:jgiraldi@princeton.edu | jgiraldi(a)princeton.edu ] with your request.

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Yuxuan Zhang's MSE thesis talk "Defending Against Adversarial Attacks with Camera Image Pipelines" will take place December 6, 2022 at 5pm in CS 402
by Nicki Mahler 05 Dec '22

05 Dec '22

Yuxuan Zhang's MSE thesis talk "Defending Against Adversarial Attacks with Camera Image Pipelines" will take place December 6, 2022 at 5pm in CS 402. The members of his committee are Felix Heide (adviser) and Olga Russakovsky (reader). All are welcome to attend. Abstract: Existing neural networks for computer vision tasks are vulnerable to adversarial attacks: adding imperceptible perturbations to the input images can fool these models into making a false prediction on an image that was correctly predicted without the perturbation. Various defense methods have proposed image-to-image mapping methods, either including these perturbations in the training process or removing them in a preprocessing step. In doing so, existing methods often ignore that the natural RGB images in today’s datasets are not captured but, in fact, recovered from RAW color filter array captures that are subject to various degradations in the capture. In this work, we exploit this RAW data distribution as an empirical prior for adversarial defense. Specifically, we propose a model-agnostic adversarial defensive method, which maps the input RGB images to Bayer RAW space and back to output RGB using a learned camera image signal processing (ISP) pipeline to eliminate potential adversarial patterns. The proposed method acts as an off-the-shelf preprocessing module and, unlike model-specific adversarial training methods, does not require adversarial images to train. As a result, the method generalizes to unseen tasks without additional retraining. Experiments on large-scale datasets, e.g., ImageNet, COCO, for different vision tasks, e.g., classification, semantic segmentation, object detection, validate that the method significantly outperforms existing methods across task domains

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