Machine Learning Lunch Meetings
At the weekly Machine Learning Lunch Meetings, faculty members from Computer Science, Statistics, ECE, and other departments discuss their latest groundbreaking research in machine learning. This is an opportunity to network with faculty and fellow researchers, and to learn about the cutting-edge research being conducted in our university.
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You can stay updated with the events hosted by MLLM by signing up to our mailing list at https://lists.cs.wisc.edu/mailman/listinfo/mllm.
2024 Spring
Thursdays 1-2pm in Computer Sciences 1221 unless noted otherwise. Co-organized by Jeremy McMahan
2/29 Grigoris Chrysos (ECE) Polynomial nets: Are activation functions required for learning in all deep networks?
3/7 Kirthi Kandasamy (CS) Data without Borders: Game-theoretic Challenges in Democratizing Data
3/14 cancelled due to speaker illness
3/21 (room: Researchers’ Link, WID 2nd floor) Kangwook Lee (ECE) Dual Operating Modes of In-Context Learning
4/4 Rob Nowak (ECE) What Kinds of Functions do Neural Networks Learn? Theory and Practical Applications
4/11 Kris Sankaran (Statistics) Transparent Synthetic Data Generation
4/18 Jessi Cisewski-Kehe (Statistics) Can ML help me find an exoplanet in my data?
4/25 (room: Biochemistry 1125) Yongyi Guo (Statistics) Balancing personalization and pooling: Decision-making and statistical inference with limited time horizons
5/2 Fred Sala (CS) Data and Compute-Efficient Foundation Model Adaptation
5/9 Junjie Hu (BMI)
2023 Fall
Thursdays 12-1pm in Computer Sciences 1325. Co-organized by Jeremy McMahan
12/7 Sharon Li (CS) Steering Large Language Models by Human Preferences
11/30 Qiaomin Xie (ISyE) Recent Advances in Average-Reward Restless Bandits
11/16 Kris Sankaran (STAT) Visualization in Deep Learning -- Theme and Variations
11/9 Dimitris Papailiopoulos (ECE) Can we teach addition to a small language model?
11/2 Yiqiao Zhong (STAT) A Geometric Journey into the world of large language models
10/26 Josiah Hanna (CS) On-policy Reinforcement Learning without On-policy Sampling
10/19 Yingyu Liang (CS) Provable Guarantees for Neural Networks via Gradient Feature Learning
10/12 Fred Sala (CS) How to Improve Your Zero-Shot Models, For Free
10/5 Yong Jae Lee (CS) Large Multimodal (Vision-Language) Models for Image Generation and Understanding
9/28 Young Wu + Jerry Zhu (CS) Two enemies are better than one
2023 Spring
5/2 Junjie Hu (BMI) Towards A Better Understanding of Language Modeling and Reasoning
4/25 Mark Craven (BMI) Machine learning to Uncover Host-Virus Interactions
4/18 Rob Nowak (ECE) Active Machine Learning: Combining Human and Artificial Intelligence for Improved Learning Efficiency and Accuracy
4/11 Mohit Gupta (CS) Computer Vision, One Photon at a Time
3/28 Yingyu Liang (CS) Towards better understanding of deep learning: a perspective from data and algorithms
3/21 Yudong Chen (CS) Best of three worlds? Bias and extrapolation in constant-stepsize stochastic approximation
3/7 Dimitris Papailiopoulos (ECE) Looped Transformers as Programmable Computers
2/28 Josiah Hanna (CS) Scaling Off-Policy Evaluation to High-Dimensional State-Spaces Via State Abstraction
2/21 Kirthi Kandasamy (CS) Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
2/14 Yong Jae Lee (CS) Large Multimodal (Vision-Language) Models for Image Generation and Understanding
2/7 Sharon Li (CS) (Data) Shift Happens, and How Should We Handle Them?
1/31 Jerry Zhu (CS) Inverse Nash Equilibrium
2022 Fall
12/20 Fred Sala (CS) Weak Supervision for All Seasons
12/13 Ramya Vinayak (ECE) Learning from Diverse Data
11/22 Kangwook Lee (ECE) LIFT: Language-Interfaced FineTuning for Non-Language Machine Learning Tasks