Developing Robots that Autonomously Learn and Plan in the Real World
Explore autonomous robot learning and planning, covering challenges, techniques, and future directions in creating versatile AI agents capable of solving complex real-world tasks.
Generalizable Robotic Agents Through Large-Scale Simulation
Explore how large-scale simulation training produces proficient robotic agents capable of real-world navigation and manipulation using only RGB sensors, guided by language instructions.
Becoming Bionic: Advancements in Smart Prosthetic Limbs
Explore cutting-edge research in prosthetics and smart limb control. Discover open problems and current solutions in neuroprosthetics, reinforcement learning, and human-robot interaction for improved functionality and usability.
Efficient Action Representation for Robot Learning
Explore efficient action representation techniques for enhancing robot learning, focusing on helpful abstractions and theoretical aspects of reinforcement learning.
Robotic Dexterous Manipulation - Advances in Learning and Teleoperation
Explore advancements in robotic manipulation, combining classical and learned methods. Discover projects on extrinsic contact learning, visuo-tactile SLAM, and in-hand object rotation using vision and touch.
Building Adaptable Generalist Robots
Explore cutting-edge approaches for building adaptable generalist robots, focusing on in-context learning, unsupervised reinforcement learning, and leveraging foundation models for improved generalization and task versatility.
Robotics as an Enabler and Inhibitor of Sustainable Development Goals
Explore robotics' dual role in achieving UN Sustainable Development Goals. Analyze potential to enable 46% of targets while inhibiting 19%, focusing on economic, social, and environmental impacts.
Learning Structured World Models From and For Physical Interactions
Explore structured world models for physical interactions in robotics. Learn novel representations and structural priors for effective model-based planning and complex manipulation tasks.
RoboCat - A Self-Improving Generalist for Robotic Manipulation
Explore RoboCat, a multi-embodiment, multi-task generalist agent for robotic manipulation. Learn about its ability to generalize across tasks and robots, and its potential for autonomous improvement.
Data-Driven Fine Manipulation: Pushing Boundaries in Robotic Precision
Explore data-driven solutions for fine robotic manipulation, enabling low-cost hardware to perform intricate tasks with superhuman precision using imitation and reinforcement learning techniques.
Reinforcement and Mean-Field Games in Algorithmic Trading - Sebastian Jaimungal
Explore reinforcement learning and mean-field games in algorithmic trading, covering deep Q-learning, reinforced deep Kalman filters, and heterogeneous agent interactions with differing beliefs in financial markets.
Preserving Patient Safety as AI Transforms Clinical Care - Curt Langlotz, Stanford University
Explore AI's impact on healthcare, focusing on patient safety, data privacy, and interpretability. Learn about new machine learning methods supporting caregivers and enhancing clinical decision-making.
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
Explore cutting-edge data science topics with leading experts, including deep learning, reinforcement learning, robustness, fairness, and ethics, through keynote talks and panel discussions.
Reinforcement Learning
Explore reinforcement learning's foundations, applications, and advanced concepts, including Q-learning, exploration-exploitation trade-offs, and instance-optimality in policy evaluation.
On Modeling Investors in Simulated Markets
Explore multi-agent market simulations, from market replay to advanced AI-driven models. Learn about trading agent design, market impact analysis, and applications in financial research.
Beyond Monitoring - The Rise of Data Observability - Shane Murray
Explore data observability as a solution to data downtime, ensuring data trust at scale. Learn its importance in modern DataOps and gain insights for implementing a robust data quality strategy.
Applied Reinforcement Learning for Online Ads and Recommender Systems
Explore how Reinforcement Learning revolutionizes online advertising, from personalized content delivery to stochastic bandit algorithms, covering business impacts and technical challenges in real-world implementation.
From AI to GX: The Quantum Leap in Algorithmic Evolution
Explore the evolution from AI to Generative Algorithms (GX) and its impact on algorithm design, creativity, and innovation in technology and society with insights from industry expert Jepson Taylor.
AI and Advanced Analytics in Retail
Explore key ML, DL, RL, and Graph Analytics use cases in Retail, with a demo of Microsoft Azure AI platform's MLOps capabilities in a Retail environment.
A Framework for Differentiable Discovery of Graph Algorithms
Explore graph algorithm discovery using deep learning and tree decomposition. Learn effective techniques for NP-complete problems with interpretable results and expanded search space.
Neural Network Verification as Piecewise Linear Optimization
Neural network verification as piecewise linear optimization: exploring robust methods to prove model reliability against attacks, using LP and MIP formulations for efficient problem-solving in various network types.