Stephanie Rosenthal, Reid Simmons
Carnegie Mellon University, Learning and Explaining the Differences Between Novice and Expert Data Analysts
Tom M. Mitchell, Brad Myers
Carnegie Mellon University, Machine Learning from Human Instruction: Every Person a Programmer
Agostino Capponi
Columbia University, Robo-Advising as a Symbiotic Human- Machine System
Yolanda Gil, Deborah Khider
University of Southern California, Automated Machine Learning for Time Series Analysis
Ion Stoica
University of California, Berkeley, A Unified Library for Distributed Reinforcement Learning
Shimon Whiteson
University of Oxford, Cooperative Multi-Agent Reinforcement Learning
Anisoara Calinescu, Doyne Farmer, Michael Wooldridge
University of Oxford, Risk Management of Investment Strategies: An Agent-based Approach
Sarit Kraus
Bar-Ilan University, Israel, Agents supporting large-scale environments of teams of people and computer systems
Sergey Levine
University of California, Berkeley, Scalable Language-Guided Deep Reinforcement Learning
Ofra Amir, David Sarne, Finale Doshi-Velez
Bar-Ilan University, Israel, Summarizing Agent Behavior to People
Adnan Darwiche
University of California, Los Angeles, Explaining Decisions of Machine Learning Systems
Suman Jana, Jeannette M. Wing, Junfeng Yang
Columbia University, Efficient Formal Safety Analysis of Neural Networks
Xiang Ren
University of Southern California, Interpretable Knowledge Reasoning and Extraction for Equity Investment
Daniela Rus
Massachusetts Institute of Technology, Compression Algorithms for Resilient AI
Ning Chen, Hang Su, Jun Zhu
Tsinghua University, Interpretable machine learning by incorporating human knowledge; Adversarial attack and defense on image classification; Adversarial attacks and defense on graph data
Ameet Talwalkar
Carnegie Mellon University, Illuminating Black-Box Models in Machine Learning
Ryan P. Adams
Princeton University, “Optimizer, explain thyself!”: Making Bayesian Optimization Interpretable
Subbarao Kambhampati
Arizona State University, Human-Aware AI Assistants for Interactive Decision Support in Finance
Xia “Ben” Hu
Texas A&M University, Post-hoc Prediction Interpretation of Deep Learning in Finance Applications
Daniel Hsu
Columbia University, Prediction semantics and interpretations that are grounded in real data
Xu Chu
Georgia Institute of Technology, Contextual Model Interpretation
Erik Demaine
Massachusetts Institute of Technology, Energy-Efficient Algorithms for AI and Data Science
Rafail Ostrovsky
University of California Los Angeles, SIENA: Securing AI Computing Environment for AWS
John Lafferty
Massachusetts Institute of Technology, Energy-Efficient Algorithms for AI and Data Science
Kay Giesecke
Stanford University, Exploring Use Cases for JP Morgan ROAR in Academic Teaching and Research
Avigdor Gal
Technion Israel Institute of Technology, Teaching the Machine to Solve Matching Problems
Vipul Goyal
Carnegie Mellon University, Secure Multi-Party Computation for Privacy Preserving Data Mining
Ariel D. Procaccia
Carnegie Mellon University, Voting-Based Methods for Explainable AI
Zhiwei Steven Wu
University of Minnesota, Preventing Unfair Discrimination in Interactive Learning
Nathan Kallus
Cornell University, Robustness and Fairness in Policy Learning from Observational Data
Joydeep Biswas
University of Massachusetts Amherst, A Scalable Approach to Correcting Failures of AI Systems in the Real World
Furong Huang, Louiqa Raschid, Alberto Rossi
University of Maryland, Methods to Identify Communities and Trading Behavior Over Financial Data Streams
Nikolas Kantas, Panos Parpas, Grigorios A. Pavliotis
Imperial College London, Dynamics, Control and Uncertainty Quantification for Stable Machine Learning Algorithms