Maryam Fazel, Professor, University of Washington Abstract: Policy Optimization methods enjoy wide practical use in reinforcement learning (RL) for applications ranging from robotic manipulation to game-playing, partly because they are easy to implement and allow for richly parameterized policies. Yet their theoretical properties, from optimality to statistical complexity, are still not fully understood. To help […]
Aaron Roth, Professor, University of Pennsylvania Abstract: Machine learning provides us with an amazing set of tools to make predictions, but how much should we trust particular predictions? To answer this, we need a way of estimating the confidence we should have in particular predictions of black-box models. Standard tools for doing this give guarantees […]
Dimitris Papailiopoulos, Associate Professor, University of Wisconsin–Madison Abstract: Large neural networks can be pruned to a small fraction of their original size, with little loss in accuracy, by following a time-consuming “train, prune, re-train” approach. Frankle & Carbin in 2019 conjectured that we can avoid this by training lottery tickets, i.e., special sparse subnetworks found at […]
Murat Kocaoglu, Assistant Professor, Purdue University Abstract: Cause-effect relations are crucial for several fields, from medicine to policy design as they inform us of the outcomes of our actions a priori. However, causal knowledge is hard to curate for complex systems that might be changing frequently. Causal discovery algorithms allow us to extract causal knowledge from […]
Dr. Ruchir Puri, Chief Scientist, IBM Research, IBM Fellow, Vice-President IBM Corporate Technology Abstract: Software has become woven into every aspect of our society, and it will be fair to say that “Software has eaten the world.” More recently, advances in AI are starting to transform every aspect of our society as well. These two […]
Pengtao Xie, Assistant Professor, UC San Diego Abstract: Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active recalling, to name a few. These learning skills and methodologies enable humans to learn new topics more effectively and efficiently. We are […]
Francis Bach, NRIA, ENS, and PSL Paris Abstract: The representation of non-negative functions as sums of squares has become an important tool in many modeling and optimization tasks. Traditionally applied to polynomial functions, it requires rich tools from algebraic geometry that led to many developments in the last twenty years. In this talk, I will […]
Mert Pilanci, Stanford University Abstract: Since deep neural network training problems are inherently non-convex, their recent dramatic success largely relies on non-convex optimization heuristics and experimental findings. Despite significant advancements, the non-convex nature of neural network training poses two central challenges: first, understanding the underlying mechanisms that contribute to model performance, and second, achieving efficient […]
Ramya Korlakai Vinayak, Assistant Professor, University of Wisconsin–Madison Abstract: Machine learning (ML) algorithms are becoming ubiquitous in various application domains such as public health, genomics, psychology, and social sciences. In these domains, data is often obtained from populations that are diverse, e.g., varying demographics, phenotypes, preferences etc. Many ML algorithms focus on learning model parameters […]
The TILOS Ethics and Early Career Committee invites you to an upcoming round table discussion on AI Ethics. This will take place virtually through Zoom on Friday, June 2, 2023 at 9am Pacific / 11am Central / Noon Eastern. Please join Dr. Nisheeth Vishnoi from Yale, Dr. David Danks from UC San Diego, and Dr. […]
