Welcome to AISys reading group!

The paper reading group meets weekly during the Fall, Spring, and Summer semesters to discuss papers in artificial intelligence, machine learning, systems, and software engineering. Participation is open to all, guests are always welcome; if you are interested in receiving invitations contact the organizer.

Each week we will discuss a different paper. The paper to discuss is announced about one week in advance by the organizer. All participants are expected to read the paper before the meeting. It is recommended to take notes about insights, questions, and other points potentially worth discussing.

The goals of the reading group are:

• Critical reflection on scientific work
• Practice reading and argumentation strategies
• Exposure to a broad range of research topics
• Practice leading group discussions

We follow these simple rules:

• Please read the paper before the meeting; set a 1-2h reminder on your schedule
• Engage in the discussions (ask questions, answer questions, discuss insights, discuss writing style)
• Volunteer for moderating paper
• A 2-min feedback form after each session

Guideline for selecting papers:

• Make sure you know the interests of the group, look at previous papers, and select a paper that lies in the interests of the group
• Make sure the paper is solid with a very high quality preferably published on top venues but could be a preprint from a top researcher which is not published yet; or classic papers in AI/ML(examples).
• Do not select your own paper or by an author from the group. We only read papers from others.
• If you are not sure about the quality or relevance of a paper, please feel free to contact the organizer.
• Preferably select a paper in your area and you are confident that you can understand a good part of the paper and can discuss it in depth.
• You do not necessarily need to prepare slides for your presentation, I would suggest walking through the paper, but if you prefer to prepare slides, that is also fine.

Topics of Interests:

• Causal AI: e.g., Causal Representation Learning, Causal Inference, Structure Learning
• Optimization: e.g., Convex, non-Convex, multi-objective, blackbox, active learning
• Systems: Computer Architecture, Distributed Systems, Serverless, Systems for ML, ML for Systems, AI/ML Hardware, Autonomous Systems
• Robotics: Robot Learning
• Cognitive Psychology: Child Learning, anything related to AI/ML
• AI Systems: Fairness, Trusts, Bias
• Classical AI: Search, Planning
• Reinforcement Learning
• Deep Learning: Theory, Architecture
• Theory of ML
• Security of ML: Adversarial ML
• Software Engineering

Good Resources:

• ArxivSanity: a great resource for finding the most relevant papers published on Arxiv.
• Arxiv Vanity: Arxiv Vanity renders Arxiv PDFs in a mobile-friendly HTML format.
• Depth First Learning: Typically tackling papers that require more background knowledge, DFL is a great resource for very high-quality explanations of ML research concepts.
• Distill.pub: Typically tackling papers that require more background knowledge, DFL is a great resource for very high-quality blogs and explanations of ML research concepts.
• Berkeley AI Research Blog: A great resource for high-quality blogs written by Berkeley ML researchers.
• r/MachineLearning: Reddit community for discussions on ML.

The discussion is limited to 60-90 min. The discussion is lead by a moderator, who may also set a focus for the discussion. The moderator will kick off the meeting by giving a summary of the paper and raising a few points for discussion. The moderator should try to incorporate all participants into the discussion. The moderator role rotates through all participants. The moderator is encouraged to help with the selection of a paper that week.

**Time and location: Friday 11:00 AM (EST)

• Organizer: Pooyan Jamshidi ([email protected])
• Contact person: Iftakhar ([email protected])

Subscribe for announcements:

• mailing list: [email protected] by sending an email to the organizer.

Slack for the reading group (join to communicate with the group members):

• Join the Slack

Agenda

February 17, 2023

'A TRANSFERABLE APPROACH FOR PARTITIONING MACHINE LEARNING MODELS ON MULTI-CHIP-MODULES'
Moderator: Rasool; email: [email protected]

February 10, 2023

'Active Bayesian Causal Inference'
Moderator: Sonam; email: [email protected]

January 27, 2023

'H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness'
Moderator: Shahriar; email: [email protected]

January 06, 2023

'FlakeFlagger: Predicting Flakiness Without Rerunning Tests'
Moderator: Iftakhar; email: [email protected]

December 16, 2022

'Active Feature Acquisition with Generative Surrogate Models'
Moderator: Hung-Tien; email: [email protected]

December 2, 2022

'A layered framework for root cause diagnosis of microservices'
Moderator: Iftakhar; email: [email protected]

November 18, 2022

'Improving Adversarial Robustness via Promoting Ensemble Diversity'
Moderator: Mehdi; email: [email protected]

November 11, 2022

'Deep Residual Learning for Image Recognition'
Moderator: Fatemeh; email: [email protected]

November 4, 2022

'Active Bayesian Causal Inference'
Moderator: Morteza; email: [email protected]

October 21, 2022

'Morphling: Fast, Near-Optimal Auto-Configuration for Cloud-Native Model Serving'
Moderator: Saeid; email: [email protected]

October 14, 2022

'Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning'
Moderator: Morteza; email: [email protected]

October 7, 2022

'Can Humans Be out of the Loop?'
Moderator: Sonam; email: [email protected]

September 23, 2022

'Kraken : Adaptive Container Provisioning forDeploying Dynamic DAGs in Serverless Platforms'
Moderator: Iftakhar; email: [email protected]

September 16, 2022

'MULTI-OBJECTIVE OPTIMIZATION BY LEARNING SPACE PARTITIONS'
Moderator: Shahriar; email: [email protected]

September 9, 2022

'Winning the Lottery Ahead of Time: Efficient Early Network Pruning'
Moderator: Nasrin; email: [email protected]

September 2, 2022

'Training Behavior of Sparse Neural Network Topologies'
Moderator: Nasrin; email: [email protected]

July 15, 2022

'Lifelong Dynamic Optimization for Self-Adaptive Systems: Fact or Fiction?'
Moderator: Shahriar; email: [email protected]

July 8, 2022

'Concave Utility Reinforcement Learning with Convex Constraints'
Moderator: Mridul Agarwal; email: [email protected]

July 1, 2022

'DUAL LOTTERY TICKET HYPOTHESIS'
Moderator: Nasrin; email: [email protected]

June 24, 2022

'Robust Contrastive Learning Using Negative Samples with Diminished Semantics'
Moderator: Fatemeh; email: [email protected]

June 17, 2022

'Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian Noise'
Moderator: Sonam; email: [email protected]

June 10, 2022

'Uniform Consistency in Causal Inference'
Moderator: Marco; email: [email protected]

May 27, 2022

'Pareto Curves for Probabilistic Model Checking'
Moderator: Sonam; email: [email protected]

May 6, 2022

'INFaaS: Automated Model-less Inference Serving'
Moderator: Saeid; email: [email protected]

Apr 15, 2022

'How to Design AI for Social Good: Seven Essential Factors'
Moderator: Elizabet; email: [email protected]

Apr 8, 2022

'Causal Reinforcement Learning using Observational and Interventional Data'
Moderator: Sonam; email: [email protected]

Apr 1, 2022

'A channel hopping based defense method against primary user emulation attack in cognitive radio networks'
Moderator: Arash; email: [email protected]

Mar 25, 2022

'Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks'
Moderator: Abhinav; email: [email protected]

Mar 18, 2022

'Multiple Randomization Designs'
Moderator: Morteza; email: [email protected]

Mar 4, 2022

'Dynamic Causal Bayesian Optimization'
Moderator: Shahriar; email: [email protected]

Feb 25, 2022

'Causal Markov Decision Processes: Learning Good Interventions Efficiently'
Moderator: Sonam; email: [email protected]

Feb 18, 2022

Brainstorming and Discussion
Moderator: Pooyan; email: [email protected]

Feb 11, 2022

'Rethinking Co-design of Neural Architectures and Hardware Accelerators '
Moderator: Elaheh; email: [email protected]

Feb 4, 2022

'Enchanted Determinism: Power without Responsibility in Artificial Intelligence '
Moderator: Gartley; email: [email protected]

Jan 28, 2022

'Causal Navigation by Continuous-time Neural Networks '
Moderator: Morteza; email: [email protected]

Jan 21, 2022

'Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty '
Moderator: Fatemeh; email: [email protected]

Jan 14, 2022

'Neurips 2021 discussion: All to read: *** Key Papers *** Awards: https://blog.neurips.cc/2021/11/30/announcing-the-neurips-2021-award-recipients/ A summary post-conference:'
Moderators: Biplav and Pooyan; emails: [email protected], [email protected]

Jan 7, 2022

'Explainable Reinforcement Learning through a Causal Lens'
Moderator: Jianhai; email: [email protected]

Dec 24, 2021

'Contrastive Learning with Adversarial Examples'
Moderator: Fatemeh; email: [email protected]

Dec 17, 2021

'TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning'
Moderator: Nasrin; email: [email protected]

Dec 3, 2021

'Chameleon: Adaptive code optimization for expedited deep neural network compilation'
Moderator: Hossein; email: [email protected]

Nov 19, 2021

'Generalizable Machine Learning in Neuroscience Using Graph Neural Networks'
Moderator: Chrisogonas; email: [email protected]

Nov 5, 2021

'Where and What? Examining Interpretable Disentangled Representations'
Moderator: Zahra; email: [email protected]

Oct 29, 2021

'Argus: Debugging Performance Issues in Modern Desktop Applications with Annotated Causal Tracing'
Moderator: Shahriar; email: [email protected]

Oct 22, 2021

'Neural Bootstrapper'
Moderator: Minsuk Shin; email: [email protected]

Oct 15, 2021

'Accelerating Reinforcement Learning with Learned Skill Priors'
Moderator: Jianhai; email: [email protected]

Oct 8, 2021

'A Simple Framework for Contrastive Learning of Visual Representations'
Moderator: Fatemeh; email: [email protected]

Oct 1, 2021

'Desiderata for Representation Learning: A Causal Perspective'
Moderator: Mohsen Rezapour; email: [email protected]

Sept 24, 2021

'Desiderata for Representation Learning: A Causal Perspective'
Moderator: Mohsen Rezapour; email: [email protected]

Sept 10, 2021

'Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models'
Moderator: Kimia; email: [email protected]

Aug 27, 2021

'Vision-based estimation of driving energy for planetary rovers using deep learning and terram'
Shoya Higa , Yumi Iwashita , Kyohei Otsu , Masahiro Ono, Olivier Lamarre, Annie Didier, and Mark Hoffmann
Moderator: Abir; email: [email protected]

Aug 20, 2021

'Neural Network Attributions: A Causal Perspective'
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar, Vineeth N Balasubramanian
Moderator: Mahdi; email: [email protected]

Aug 13, 2021

'Pretrained Transformers as Universal Computation Engines'
Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch
Moderator: Kallol & Modar; email: [email protected]

Aug 6, 2021

'The Causal-Neural Connection: Expressiveness, Learnability, and Inference'
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
Moderator: Mohammad, Pooyan, Mahdi; email: [email protected]

July 30, 2021

'Decision Transformer: Reinforcement Learning via Sequence Modeling'
Moderator: Bharat; email: [email protected]

July 23, 2021

'The Causal-Neural Connection: Expressiveness, Learnability, and Inference'
Kevin Xia, Kai-Zhan Lee, Yoshua Bengio, Elias Bareinboim
*Moderator: Mohammad, Pooyan, Mahdi; email: [email protected]

July 16, 2021

'DEEP LEARNING FOR SYMBOLIC MATHEMATICS'
Moderator: Kimia; email: [email protected]

July 9, 2021

'Adversarial Robustness through the Lens of Causality'
Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, and Kun Zhang
Moderator: Fatemeh; email: [email protected]

July 2, 2021

How to read a paper
Moderator: Pooyan; email: [email protected]

June 25, 2021

'Relational Reinforcement Learning'
Moderator: Forest; email: [email protected]

June 18, 2021

'Discussion regarding the group reading'
'Recording Video'
Moderator: Pooyan; email: [email protected]

June 11, 2021

'Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks'
'slides'
Patrick Lewis, Ethan Perez,Aleksandra Piktus, et al.
Moderator: Manas Gaur; email: [email protected]

June 4, 2021

'Towards causal generative scene models via competition of experts'
Julius von Kügelgen, Ivan Ustyuzhaninov, Peter Gehler, Matthias Bethge, Bernhard Schölkopf
Moderator: Hamed; email: [email protected]

May 28, 2021

'Representing Partial Programs with Blended Abstract Semantics'
Maxwell Nye, Yewen Pu, Matthew Bowers, Jacob Andreas, Joshua B. Tenenbaum, Armando Solar-Lezama
Moderator: Jianhai; email: [email protected]

May 21, 2021

'CAUSAL DISCOVERY WITH REINFORCEMENT LEARNING'
Shengyu Zhu, Ignavier Ng, Zhitang Chen
Moderator: Musfiq; email: [email protected]

May 14, 2021

'Learning Independent Causal Mechanisms'
Giambattista Parascandolo, Niki Kilbertus, Mateo Rojas-Carulla, Bernhard Scholkopf
Moderator: Pooyan; email: [email protected]

May 7, 2021

'Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence'
Atoosa Kasirzadeh
Moderator: Marilyn; email: [email protected]

April 30, 2021

'Representation Learning via Invariant Causal Mechanisms; 2020'
Moderator: Pooyan; email: [email protected]

April 23, 2021

"Total Nitrogen Estimation in Agricultural Soils via Aerial Multispectral Imaging and LIBS"
Moderator: Abir; email: [email protected]

April 16, 2021

"Heart rate variability: An index of brain processing in vegetative state? An artificial intelligence, data mining study"
Moderator: Koppel; email: [email protected]

April 9, 2021

"Causal inference by using invariant prediction: identification and confidence intervals"
Moderator: Mohammad Ali Javidian; email: [email protected]

April 2, 2021

"Markov Chain Monte Carlo and Variational Inference: Bridging the Gap"
Tim Salimans, Algoritmica, Diederik P. Kingma, and Max Welling
Moderator: Kimia; email: [email protected]

March 26, 2021

##Cancelled

March 19, 2021

“Improving Adversarial Robustness Requires Revisiting Misclassified Examples”
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu
Moderator: Ying; email: [email protected]

March 12, 2021

"Approximating discrete probability distributions with dependence trees"
C.K.Chow, C.N.Liu
Moderator: Fatemeh; email: [email protected]

March 5, 2021

"On the Relationship between Sum-Product Networks and Bayesian Networks"
Han Zhao, Mazen Melibari,Pascal Poupart
Moderator: Shahriar; email: [email protected]

February 26, 2021

"Deep Reinforcement Learning with Dynamic Optimism"
Ted Moskovitz, Jack Parker-Holder, Aldo Pacchiano, Michael Arbel
Moderator: Qi Zhang; email: [email protected]

February 19, 2021

"Learning the Parameters of Bayesian Networks from Uncertain Data"
Segev Wasserkrug, Radu Marinescu, Sergey Zeltyn, Evgeny Shindin, Yishai Feldman
Moderator: Marco; email: [email protected]

February 12, 2021

"Drug discovery with explainable artificial intelligence"
José Jiménez-Luna, Francesca Grisoni, and Gisbert Schneider
Moderator: Forest; email: [email protected]

February 5, 2021

"Entropic Causal Inference"
Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi
Moderator: Mohammad Ali Javidian

January 29, 2021

"How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods"
Jeya Vikranth Jeyakumar, Joseph Noor, Yu-Hsi Cheng, Luis Garcia, Mani Srivastava
Moderator: Biplav Srivastava

January 22, 2021

"Reconsidering Generative Objectives For Counterfactual Reasoning"
Danni Lu, Chenyang Tao, Junya Chen, Fan Li, Feng Guo, Lawrence Carin
Moderator: Shuge Lei

January 15, 2021

"Underspecification Presents Challenges for Credibility in Modern Machine Learning"
Alexander D'Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, D. Sculley
Moderator: Bharat

January 13, 2021

"Neural Adaptive Video Streaming with Pensieve"
Hongzi Mao, Ravi Netravali, Mohammad Alizadeh
Moderator: Nawras

January 8, 2021

"Theoretically Principled Trade-off between Robustness and Accuracy"
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, Michael I. Jordan
Talk
Moderator: Ying

January 6, 2021

"Value-Decomposition Networks For Cooperative Multi-Agent Learning"
Peter Sunehag, Guy Lever, Audrunas Gruslys, Wojciech Marian Czarnecki, Vinicius Zambaldi, Max Jaderberg, Marc Lanctot, Nicolas Sonnerat, Joel Z. Leibo, Karl Tuyls, Thore Graepel
Moderator: Nawras

Jan. 1, 2021

Happy New Year! :fireworks:

December 25, 2020

Merry Christmas! :santa: :christmas_tree:

December 18, 2020

"Learning Differentiable Programs with Admissible Neural Heuristics"
Ameesh Shah, Eric Zhan, Jennifer J. Sun, Abhinav Verma, Yisong Yue, Swarat Chaudhuri
Moderator: Forest

December 11, 2020

"On Sampled Metrics for Item Recommendation"
Walid Krichene, Steffen Rendle
Moderator: Shuge

December 4, 2020

"Deep Compositional Captioning: Describing Novel Object Categories without Paired Training Data"
Lisa Anne Hendricks, Subhashini Venugopalan, Marcus Rohrbach, Raymond Mooney, Kate Saenko, and Trevor Darrell
Moderator: Kimia

November 27, 2020

Happy Thanksgiving! :turkey:

November 20, 2020

"LAMBDANETWORKS: MODELING LONG-RANGE INTERACTIONS WITHOUT ATTENTION" (Continue)
code
additional materials: material 1, material 2
Paper under double-blind review (Under review as a conference paper at ICLR 2021)
Moderator: Bharat

November 13, 2020

"LAMBDANETWORKS: MODELING LONG-RANGE INTERACTIONS WITHOUT ATTENTION"
code
additional materials: material 1, material 2
Paper under double-blind review (Under review as a conference paper at ICLR 2021)
Moderator: Bharat

November 6, 2020

"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting"
Xingjian Shi, Zhourong Chen, Hao Wang, and Dit-Yan Yeung
Moderator: Shuge

October 30, 2020

"Law and Adversarial Machine Learning"
Ram Shankar Siva Kumar, David R. O'Brien, Kendra Albert, Salome Vilojen.
Moderator: Marilyn

October 23, 2020

"YOLACT: Real-time Instance Segmentation"
Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee.
Moderator: Rabab

October 16, 2020

"Causal Bayesian Optimization"
Virginia Aglietti, Xiaoyu Lu, Andrei Paleyes, Javier González.
Moderator: Shahriar

October 9, 2020

"Symbolic pregression: Discovering Physical Laws from Raw Distorted Video."
Silviu-Marian Udrescu, Max Tegmark
Moderator: Kimia

October 2, 2020

"Deep Flow-Guided Video Inpainting"
Rui Xu, Xiaoxiao Li, Bolei Zhou, Chen Change Loy.
Moderator: Shuge

September 25, 2020

"Towards Robust Neural Networks via Random Self-ensemble"
Xuanqing Liu, Minhao Cheng, Huan Zhang, and Cho-Jui Hsieh.
Moderator: Ying

September 18 2020

"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 4)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan

September 11 2020

"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 3)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan

September 4 2020

"Perspective Plane Program Induction from a Single Image"
Yikai Li, Jiayuan Mao, Xiuming Zhang, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu.
Moderator: Bharat

August 28 2020

"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 2)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan

August 21 2020

The Computational Limits of Deep Learning
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, Gabriel F. Manso.
Moderator: Biplav

August 14 2020

"DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning" (part 1)
Kevin Ellis, Catherine Wong, Maxwell Nye, Mathias Sable-Meyer, Luc Cary, Lucas Morales, Luke Hewitt, Armando Solar-Lezama, Joshua B. Tenenbaum.
Moderator: Pooyan

August 7 2020

"Improving Adversarial Robustness via Promoting Ensemble Diversity"
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, Jun Zhu.
Moderator: Ying

July 31 2020

"Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider"
Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini.
Moderator: Shahriar

July 24 2020

"Spatially Transformed Adversarial Examples"
Chaowei Xiao, Jun-Yan Zhu, Bo Li, Warren He, Mingyan Liu, Dawn Song.
Moderator: Fatemeh

July 17 2020

"A DEEP LEARNING APPROACH FOR CANCER DETECTION AND RELEVANT GENE IDENTIFICATION"
PADIDEH DANAEE, REZA GHAEIN.
Moderator: MAHMUDUL

July 10 2020

"Human-level concept learning through probabilistic program induction"
Brenden M. Lake, Ruslan Salakhutdinov, Joshua B. Tenenbaum
Moderator: Jianhai

July 03 2020

"Taskonomy: Disentangling Task Transfer Learning"
Amir Zamir, Alexander Sax, William Shen, Leonidas Guibas, Jitendra Malik, Silvio Savarese
Project Page: http://taskonomy.stanford.edu/
Moderator: Bharat

June 26 2020

"Scalable Neural Bootstrapper"
Minsuk Shin, Hyungjoo Cho, Sungbin Lim
Moderator: Minsuk

June 19 2020

“Concept Learning in Deep Reinforcement Learning”
Diego Gomez, Nicanor Quijano, Luis Felipe Giraldo
Moderator: Nawras

June 12 2020

"Shortcut Learning in Deep Neural Networks"
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, Felix A. Wichmann
Moderator: Ying

June 5 2020

"Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions"
Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Moderator: Mohammad

May 29 2020

"Transfer in Deep Reinforcement Learning Using Successor Features and Generalized Policy Improvement"
Andre Barreto, Diana Borsa, John Quan, Tom Schaul, David Silver, Matteo Hessel, Daniel Mankowitz, Augustin Zidek, Remi Munos
Moderator: Jianhai

May 22 2020

“Sympson’s Paradox in Covid-19 Case Fatality Rates: A Mediation Analysis of Age-Related Causal Effects”
Julius von Kügelgen, Luigi Gresele, Bernhard Schölkopf
Moderator: Marco

May 15 2020

"Counterfactuals and Their Applications" - chapter 4 of the book, “Causal Inference in Statistics: A Primer”
Judea Pearl, Madelyn Glymour and Nicholas Jewel
Moderator: Mohammad

May 8 2020

“Successor Features for Transfer in Reinforcement Learning”
André Barreto, Will Dabney, Rémi Munos, Jonathan J. Hunt, Tom Schaul, Hado van Hasselt, David Silver
Moderator: Pooyan

May 1 2020

"Deep Learning: Chapter 16.3 onwards" MIT press, 2016.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Mohammad

April 24 2020

"Deep Learning: Chapter 16.1 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Mohammad

April 17 2020

“On Learning Invariant Representations for Domain Adaptation”(Continue)
Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon
Moderator: Jianhai

April 10 2020

“On Learning Invariant Representations for Domain Adaptation”
Han Zhao, Remi Tachet des Combes, Kun Zhang, Geoffrey J. Gordon
Moderator: Jianhai

April 3 2020

“Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations”
Florian Tramèr, Jens Behrmann, Nicholas Carlini, Nicolas Papernot, Jörn-Henrik Jacobsen
Moderator: Ying

March 27 2020

"The Three Pillars of Machine Programming"
Justin Gottschlich, Armando Solar-Lezama, Nesime Tatbul, et al
Moderator: Pooyan

March 20 2020

“Invariant Risk Minimization”(Continue)
Martin Arjovsky, L´eon Bottou, Ishaan Gulrajani, David Lopez-Paz
Moderator: Mohammad

March 13 2020

“Invariant Risk Minimization”
Martin Arjovsky, L´eon Bottou, Ishaan Gulrajani, David Lopez-Paz
Moderator: Mohammad

March 6 2020

“A Programmable Approach to Model Compression”
Vinu Joseph, Saurav Muralidharan, Animesh Garg, Michael Garland, Ganesh Gopalakrishnan
Moderator: Shahriar

February 14 2020

"Deep Learning: Chapter 12, section 1.4 to section 1.6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai

February 7 2020

"Deep Learning: Chapter 12, section 1.1 to section 1.3" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Bharat

January 31 2020

"Deep Learning: Chapter 11.5 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Bharat

January 24 2020

"Deep Learning: Chapter 11 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar

January 17 2020

"Deep Learning: Chapter 9.10 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying

January 10 2020

"Deep Learning: Chapter 9.6 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai

November 22 2019

"Deep Learning: Chapter 9.4 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar

November 22 2019

"Deep Learning: Chapter 9 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Bharat

November 8 2019

"Deep Learning: Section 8.7 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar

November 1 2019

"Deep Learning: Section 8.6 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai

Oct 11 2019

"Deep Learning: Section 8.4 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar

Oct 4 2019

"Deep Learning: Section 8.3 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ahmed

Aug 19 2019

"Deep Learning: Section 7.9 onwards" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Chris

Aug 12 2019

"Deep Learning: Section 7.6 onwards (Semi-supervised, multi-task learning, early stopping)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Dr. Jamshidi

Aug 5 2019

"Deep Learning: chapter 7.2 - 7.5" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar

Jul 29 2019

"Deep Learning: chapter 7" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ahmed

Jul 22 2019

"Deep Learning: chapter 6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying

Jul 15 2019

"Deep Learning: chapter 6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying

Jul 08 2019

"Deep Learning: chapter 6" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Ying

Jul 01 2019

"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Joshua

Jun 24 2019

"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Nawras

Jun 17 2019

"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Chrisogonas

Jun 10 2019

"Deep Learning: chapter 5 (ML basics)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Chrisogonas

Jun 3 2019

"Deep Learning: chapter 3 (Probability and Information Theory)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Shahriar

May 27 2019

"Deep Learning: chapter 2 (Linear Algebra)" MIT press, 2016
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville
Moderator: Jianhai

May 20 2019

"Learning Bayesian networks: chapter 8 (Sections 8.1-8.2)"
Neapolitan, Richard E
Moderator: Marco

May 13 2019

"Modeling concept drift: A probabilistic graphical model based approach"
In International Symposium on Intelligent Data Analysis, pp. 72-83. Springer, Cham, 2015
Borchani, Hanen, Ana M. Martínez, Andrés R. Masegosa, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón et al
Moderator: Emad

May 06 2019

"Noisy Blackbox Optimization using Multi-fidelity Queries: A Tree Search Approach"
In The 22nd International Conference on Artificial Intelligence and Statistics (AIStats), pp. 2096-2105. 2019
Sen, Rajat, Kirthevasan Kandasamy, and Sanjay Shakkottai
Moderator: Jianhai

Apr 15 2019

"Hybrid reward architecture for reinforcement learning"
In Advances in Neural Information Processing Systems, pp. 5392-5402. 2017
Van Seijen, Harm, Mehdi Fatemi, Joshua Romoff, Romain Laroche, Tavian Barnes, and Jeffrey Tsang
Moderator: Yuxiang

Mar 25 2019

"Evaluating the Search Phase of Neural Architecture Search"
Sciuto, Christian, Kaicheng Yu, Martin Jaggi, Claudiu Musat, and Mathieu Salzmann
Moderator: Rui

Mar 18 2019

"Guiding deep learning system testing using surprise adequacy"
Proceedings of the 41th International Conference on Software Engineering (ICSE), 2019
Kim, Jinhan, Robert Feldt, and Shin Yoo
Moderator: Ying

Mar 11 2019

"The seven tools of causal inference, with reflections on machine learning"
Communications of the ACM 62, no. 3 (2019): 54-60
Pearl, Judea
Moderator: Marco Valtorta

Mar 4 2019

"Designing neural network architectures using reinforcement learning"
International Conference on Learning Representations (ICLR), 2017
Baker, Bowen, Otkrist Gupta, Nikhil Naik, and Ramesh Raskar
Moderator: Jianhai Su

Feb 25 2019

"Dynamic gpgpu power management using adaptive model predictive control"
In 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA), pp. 613-624. IEEE, 2017
Majumdar, Abhinandan, Leonardo Piga, Indrani Paul, Joseph L. Greathouse, Wei Huang, and David H. Albonesi
Moderator: Shahriar

Feb 11 2019

"The Art of Getting Deep Neural Networks in Shape"
ACM Transactions on Architecture and Code Optimization (TACO) 15, no. 4 (2019): 62
Mammadli, Rahim, Felix Wolf, and Ali Jannesari
Moderator: RUI XIN

Feb 4 2019

"PerfLearner: learning from bug reports to understand and generate performance test frames"
In Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering, pp. 17-28. ACM, 2018
Han, Xue, Tingting Yu, and David Lo
Moderator: Yang Ren

Jan 27 2019

"Predictive Test Selection"
Machalica, Mateusz, Alex Samylkin, Meredith Porth, and Satish Chandra
Moderator: Alireza

Jan 21 2019

"Curriculum learning of Bayesian network structures"
In Asian Conference on Machine Learning, pp. 269-284. 2016
Zhao, Yanpeng, Yetian Chen, Kewei Tu, and Jin Tian
Moderator: Mohammad Ali Javidian

Jan 14 2019

"Performance Analysis of Cloud Applications"
In 15th USENIX Symposium on Networked Systems Design and Implementation (NSDI 18). USENIX Association, 2018
Ardelean, Dan, Amer Diwan, and Chandra Erdman
Moderator: Jianhai Su

Jan 7 2019

"Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents"
Journal of Artificial Intelligence Research 61 (2018): 523-562
Machado, Marlos C., Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling
Moderator: Yuxiang Sun

Dec 17 2018

"Massively Parallel Hyperparameter Tuning"
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar
Moderator: Shahriar

Dec 3 2018

"Transportability of causal and statistical relations: A formal approach"
In Proceedings of the 25th AAAI Conference on Artificial Intelligence, 2011
Pearl, Judea, and Elias Bareinboim
Moderator: Mohammad Ali Javidian

Nov 26 2018

Human-level intelligence or animal-like abilities?\ Commun. ACM 61, 10 (September 2018), 56-67
Adnan Darwiche
Moderator: Marco Valtorta

Nov 19 2018

"Understanding and Auto-Adjusting Performance-Sensitive Configurations"
Shu Wang, Chi Li, Henry Hoffmann, Shan Lu, William Sentosa, Achmad Imam Kistijantoro
Moderator: YANG REN

Nov 12 2018

"Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics"
In NSDI, pp. 363-378. 2016
Venkataraman, Shivaram, Zongheng Yang, Michael J. Franklin, Benjamin Recht, and Ion Stoica
Moderator: Edward Tsien

Nov 5 2018

"Deeparchitect: Automatically designing and training deep architectures"
Negrinho, Renato, and Geoff Gordon
Moderator: Rui Xin

October 29 2018

"Deepxplore: Automated whitebox testing of deep learning systems"
In Proceedings of the 26th Symposium on Operating Systems Principles (SOSP), pp. 1-18. ACM, 2017
Pei, Kexin, Yinzhi Cao, Junfeng Yang, and Suman Jana
Moderator: Hussein Almulla

October 22 2018

"Autonomous skill-centric testing using deep learning"
In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 95-102. IEEE, 2017
Hangl, Simon, Sebastian Stabinger, and Justus Piater
Moderator: Jason O'Kane

October 15 2018

"Human-level control through deep reinforcement learning"
Nature 518, no. 7540 (2015): 529
Mnih, Volodymyr, Koray Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves et al
Moderator: Yuxiang Sun

October 8 2018

"Translating code comments to procedure specifications"
In Proceedings of the 27th ACM SIGSOFT International Symposium on Software Testing and Analysis, pp. 242-253. ACM, 2018
Blasi, Arianna, Alberto Goffi, Konstantin Kuznetsov, Alessandra Gorla, Michael D. Ernst, Mauro Pezzè, and Sergio Delgado Castellanos
Moderator: Gregory Gay

October 1 2018

"The care and feeding of wild-caught mutants"
In Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering, pp. 511-522. ACM, 2017
Brown, David Bingham, Michael Vaughn, Ben Liblit, and Thomas Reps
Moderator: Alireza Salahirad

September 24 2018

"CALOREE: learning control for predictable latency and low energy"
In Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, pp. 184-198. ACM, 2018
Mishra, Nikita, Connor Imes, John D. Lafferty, and Henry Hoffmann
Moderator: Shahriar

September 10 2018

"What bugs live in the cloud? a study of 3000+ issues in cloud systems"
In Proceedings of the ACM Symposium on Cloud Computing, pp. 1-14. ACM, 2014
Gunawi, Haryadi S., Mingzhe Hao, Tanakorn Leesatapornwongsa, Tiratat Patana-anake, Thanh Do, Jeffry Adityatama et al
Moderator: Yang