- talks - lists.cs.princeton.edu

Howard Chen will present his General Exam "Can Rationalization Improve Robustness?" on Wednesday, April 20, 2022 at 3:00 PM via Zoom.
by Louis W. Riehl 19 Apr '22

19 Apr '22

Howard Chen will present his General Exam "Can Rationalization Improve Robustness?" on Wednesday, April 20, 2022 at 3:00 PM via Zoom. Zoom link: https://princeton.zoom.us/j/94177862135 Committee Members: Danqi Chen (advisor), Karthik Narasimhan (advisor), Jia Deng Abstract: A growing line of work has investigated the development of neural NLP models that can produce rationales--subsets of input that can explain their model predictions. In this work, we ask whether such rationale models can also provide robustness to adversarial attacks in addition to their interpretable nature. Since these models need to first generate rationales (``rationalizer'') before making predictions (``predictor''), they have the potential to ignore noise or adversarially added text by simply masking it out of the generated rationale. To this end, we systematically generate various types of `AddText' attacks for both token and sentence-level rationalization tasks, and perform an extensive empirical evaluation of state-of-the-art rationale models across five different tasks. Our experiments reveal that the rationale models show the promise to improve robustness, while they struggle in certain scenarios--when the rationalizer is sensitive to positional bias or lexical choices of attack text. Further, leveraging human rationale as supervision does not always translate to better performance. Our study is a first step towards exploring the interplay between interpretability and robustness in the rationalize-then-predict framework. Reading List: https://docs.google.com/document/d/15UBbTlvJSIz3Go7ZOx6A_y_QnD8aujdEpDK82m4… Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Qinshi Wang will present his Pre FPO "Formally verifiable data plane programming" on Wednesday, April 20, 2022 in CS 401 at 4pm
by Nicki Mahler 19 Apr '22

19 Apr '22

Qinshi Wang will present his Pre FPO "Formally verifiable data plane programming" on Wednesday, April 20, 2022 in CS 401 at 4pm His committee is as follows: Andrew Appel (advisor) David Walker (reader) Nate Foster, Cornell (reader) Jen Rexford (examiner) Zachary Kincaid (examiner) All are welcome to attend. Title: Formally verifiable data plane programming Older-generation network devices were not end-user programmable. Software-defined network has been successful in separating the control plane and the data plane and providing more programmability for the control plane. But the data plane still lacks programmability. In recent years, programmable data plane devices and the P4 programming language, which programs these devices, have been developed. Because of P4's special language features for high-speed packet processing, and because extra consideration is needed to write P4 programs that fit into limited hardware resources, programming in P4 is hard and error-prone. Tools have been developed to check safety properties, but those are far from guaranteeing correctness. Meanwhile, P4 compilers and hardware may also have bugs. And when the network fails, it is hard to identify the cause between the P4 program, the compiler, and the hardware. We suggest that the solution is to adopt formal verification to P4 to make all the tools in the toolchain provably correct. Toward this goal, we first define an intermediate representation called P4light, which can encode the full P4 language after processing by the front end, and define the abstract syntax tree (AST) of P4light. We then define the mechanized operational semantics for P4light in the Coq proof assistant, according to the P4 specification and reference implementations. In the semantics, we establish a phase distinction between the instantiation phase (static resource allocation) and the execution phase. This phase distinction clarifies the explanation of the language, simplifies the operational semantics, and avoids tracking complicated objects when analyzing programs' behavior. Still, proving programs correct directly with respect to the semantics is too complicated to be tractable. So we build a verifier that helps users prove correctness of P4 programs. The verifier consists of an expressive program logic proved sound with respect to the semantics and a proof automation system that applies the program logic rules to prove programs correct. The verifier is powerful enough to prove the behavior of a Bloom filter on a sequence of packets with moderate human effort.

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SEAS talk: Databricks: An entrepreneur’s journey from Princeton to founder TODAY at 4:30pm
by Emily C. Lawrence 19 Apr '22

19 Apr '22

SEAS talk Speakers: Patrick Wendell, Databricks & Jen Rexford, Princeton University Date: Tuesday, April 19, 2022 Time: 4:30pm EST Location: Webinar Event page: [ https://engineering.princeton.edu/events/databricks-entrepreneurs-journey-p… | https://engineering.princeton.edu/events/databricks-entrepreneurs-journey-p… ] Patrick Wendell ’11 , co-founder and vice president of engineering at Databricks, will join Jen Rexford ’91 , chair of Princeton’s Department of Computer Science, in a conversation about his entrepreneurial journey from computer science major at Princeton to co-founder of a pioneering company used by businesses around the world to manage and analyze their data. Wendell is a co-founder of Databricks, a machine learning and data infrastructure company recently valued at $38 billion. At Databricks Patrick manages several engineering efforts in the areas of machine learning and data science. He holds an M.S. in Computer Science at UC Berkeley, where his research focused on low latency scheduling for large scale analytics workloads. He holds a B.S.E in Computer Science from Princeton University. Rexford is the Gordon Y.S. Wu Professor in Engineering and chair and professor of Computer Science at Princeton. She received her Ph.D. from the University of Michigan and earned her B.S.E. in electrical engineering from Princeton in 1991. She worked at AT&T Labs before joining the Princeton faculty in 2005. Rexford is a leader in computer networking, focusing on methods to improve and expand digital communications. This event will take place at 4:30 pm Eastern Time / 1:30 pm Pacific Time on Tuesday, April 19. No advanced registration required.

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Christina Shatford will present her MSE talk "Simultaneous Pattern and Detector Optimization for Visual Tags" on Thursday, April 21, 2022 at 3:00pm via Zoom
by Nicki Mahler 18 Apr '22

18 Apr '22

Christina Shatford will present her MSE talk "Simultaneous Pattern and Detector Optimization for Visual Tags" on Thursday, April 21, 2022 at 3:00pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/91816794623 | https://princeton.zoom.us/j/91816794623 ] Committee members: Rusinkiewicz (Adviser) and Ryan Adams (Reader) Abstract: Accurate and robust object localization has been a long studied problem in computer vision. In this talk, we will propose a new approach for localizing visual tags in images. Instead of optimizing a detector to locate a tag with a static pattern, this approach involves the simultaneous optimization of both the detector and the pattern of the visual tag. We will show how this joint learning leads to increased localization accuracy when compared to only training the detector and using a selection of standard non-optimized tags, such as QR codes. All are welcome to attend.

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Huihan Li will present her MSE Talk "Re-evaluating Conversational Question Answering" on Thursday, April 21st, 2022 at 3:30 PM via Zoom.
by Louis W. Riehl 14 Apr '22

14 Apr '22

Huihan Li will present her MSE Talk "Re-evaluating Conversational Question Answering" on Thursday, April 21st, 2022 at 3:30 PM via Zoom. Zoom Link: [ https://princeton.zoom.us/j/92763452837 | https://princeton.zoom.us/j/92763452837 ] Committee Members: Danqi Chen (advisor), Karthik Narasimhan (reader) All are welcome to attend. Re-evaluating Conversational Question Answering Conversational question answering aims to provide natural-language answers to users in information-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human-machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers. We find that the distribution of human-machine conversations differs drastically from that of human-human conversations, and there is a disagreement between human and gold-history evaluation in terms of model ranking. We further investigate how to improve automatic evaluations, and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments. Finally, we demonstrate that training conversational question answering systems using their own predictions benefits the models under the new automatic evaluation protocol. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Juan Duque will present his MSE talk, "Transformer Representations for Efficient Reinforcement Learning" on Thursday, April 21st at 3:00 PM (EDT) via Zoom.
by Louis W. Riehl 14 Apr '22

14 Apr '22

Juan Duque will present his MSE talk, "Transformer Representations for Efficient Reinforcement Learning" on Thursday, April 21st at 3:00 PM (EDT) via Zoom. Zoom Link: [ https://princeton.zoom.us/j/93980023607 | https://princeton.zoom.us/j/93980023607 ] Committee Members: Karthik Narasimhan (adviser) and Elad Hazan (reader) All are welcome to attend. Transformer Representations for Efficient Reinforcement Learning Reinforcement Learning algorithms are often trained from scratch at high computational costs. In this talk I will present a self-supervised learning framework that uses the Transformer architecture to generate useful representations for Reinforcement Learning. Our formulation uses objectives borrowed from Natural Language Processing and Imitation Learning to pre-train a Tranformer model from offline trajectories that is then fine-tuned on Atari games. We demonstrate how this setup allows for quick online convergence in under 100 thousand episodes. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Ishita Chaturvedi will present her General Exam "GPU Out-Of-Order Processing" on Wednesday, April 20, 2022 at 1:00 PM in Friend 202 and via zoom.
by Louis W. Riehl 14 Apr '22

14 Apr '22

Ishita Chaturvedi will present her General Exam "GPU Out-Of-Order Processing" on Wednesday, April 20, 2022 at 1:00 PM in Friend 202 and via zoom. Zoom link: [ https://princeton.zoom.us/my/ishita.c | https://princeton.zoom.us/my/ishita.c ] Committee Members: David August (advisor), David Wentzlaff, Tyler Sorensen Abstract: GPUs have become ubiquitous today for high-performance computing. While GPUs leverage massive multithreaded parallelism to obtain speedup, they still suffer from having multiple stall cycles. While some stall reasons can be fixed others must be tolerated. We introduce the Def-Ex GPU infrastructure which improves GPU performance using Out-of-Order instruction execution. We focus on the Deferred Execution Buffer which defers the execution of stalling instructions to issue them later. This gives an opportunity for the next instructions in the pipeline to execute, thus reducing the stall cycles. We see a maximum speedup of 26% and a geomean speedup of 14% on the Rodinia benchmark suite with the Deferred Execution Buffer. Reading List: [ https://docs.google.com/document/d/1LxXAI99yrbRsGa6kc56JCsnmBwQ8nzs3B2E9pTF… | https://docs.google.com/document/d/1LxXAI99yrbRsGa6kc56JCsnmBwQ8nzs3B2E9pTF… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Huihan Li will present her MSE talk "Re-evaluating Conversational Question Answering" on April 21, 3:30pm via Zoom
by Nicki Mahler 14 Apr '22

14 Apr '22

Huihan Li will present her MSE talk "Re-evaluating Conversational Question Answering" on April 21, 3:30pm via Zoom. Zoom link: [ https://princeton.zoom.us/j/92763452837 | https://princeton.zoom.us/j/92763452837 ] Huihan's committee is as follows: Danqi Chen (adviser) and Karthik Narasimhan (reader) All are welcome to attend. Abstract: Conversational question answering aims to provide natural-language answers to users ininformation-seeking conversations. Existing conversational QA benchmarks compare models with pre-collected human-human conversations, using ground-truth answers provided in conversational history. It remains unclear whether we can rely on this static evaluation for model development and whether current systems can well generalize to real-world human- machine conversations. In this work, we conduct the first large-scale human evaluation of state-of-the-art conversational QA systems, where human evaluators converse with models and judge the correctness of their answers. We find that the distribution of human-machine conversations differs drastically from that of human-human conversations, and there is a disagreement between human and gold-history evaluation in terms of model ranking. We further investigate how to improve automatic evaluations, and propose a question rewriting mechanism based on predicted history, which better correlates with human judgments. Finally, we demonstrate that training conversational question answering systems using their own predictions benefits the models under the new automatic evaluation protocol.

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Mengzhou Xia will present her General Exam "Structured Pruning Learns Compact and Accurate Models" on Tuesday, April 19, 2022 at 1:00 PM in CS 302 and via Zoom.
by Louis W. Riehl 12 Apr '22

12 Apr '22

Mengzhou Xia will present her General Exam "Structured Pruning Learns Compact and Accurate Models" on Tuesday, April 19, 2022 at 1:00 PM in CS 302 and via Zoom. Zoom link: [ https://princeton.zoom.us/j/6256370935 | https://princeton.zoom.us/j/6256370935 ] Committee Members: Danqi Chen (advisor), Karthik Narasimhan, Kai Li Abstract: The growing size of neural language models has led to increased attention in model compression. The two predominant approaches are pruning, which gradually removes weights from a pre-trained model, and distillation, which trains a smaller compact model to match a larger one. Pruning methods can significantly reduce the model size but hardly achieve large speedups as distillation. However, distillation methods require large amounts of unlabeled data and are expensive to train. In this work, we propose a task-specific structured pruning method CoFi (Coarse- and Fine-grained Pruning), which delivers highly parallelizable subnetworks and matches the distillation methods in both accuracy and latency, without resorting to any unlabeled data. Our key insight is to jointly prune coarse-grained (e.g., layers) and fine-grained (e.g., heads and hidden units) modules, which controls the pruning decision of each parameter with masks of different granularity. We also devise a layerwise distillation strategy to transfer knowledge from unpruned to pruned models during optimization. Our experiments on GLUE and SQuAD datasets show that CoFi yields models with over 10x speedups with a small accuracy drop, showing its effectiveness and efficiency compared to previous pruning and distillation approaches. Reading List: [ https://docs.google.com/document/d/1cJzfOnk7OEn1ssGHZlpheGDRCZ9I9bO4Lcdk3EF… | https://docs.google.com/document/d/1cJzfOnk7OEn1ssGHZlpheGDRCZ9I9bO4Lcdk3EF… ] Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so. Louis Riehl Graduate Administrator Computer Science Department, CS213 Princeton University (609) 258-8014

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Samyak Gupta will present his General Exam on April 15th, 2022 at 1pm via Zoom.
by Nicki Mahler 12 Apr '22

12 Apr '22

Samyak Gupta will present his General Exam on April 15th, 2022 at 1pm via Zoom. Zoom link: [ https://princeton.zoom.us/my/samyakg | https://princeton.zoom.us/my/samyakg ] The members of his committee are as follows: Kai Li (adviser), Danqi Chen, and Jon Cohen (Neuroscience) Link to Abstract and Reading list: [ https://docs.google.com/document/d/1t6BU6TflwycaOe7XECeUk7Tln19Aui9JqVOLhxk… | https://docs.google.com/document/d/1t6BU6TflwycaOe7XECeUk7Tln19Aui9JqVOLhxk… ]

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