Search Results for author:
Found 14 papers, 7 papers with code
Progressive Safeguards for Safe and Model-Agnostic Reinforcement Learning
Our framework is inspired by how parents safeguard their children across a progression of increasingly riskier tasks, imparting a sense of safety that is carried over from task to task.
Maia-2: A Unified Model for Human-AI Alignment in Chess
Previous work in modeling human decision-making in chess uses completely independent models to capture human style at different skill levels, meaning they lack coherence in their ability to adapt to the full spectrum of human improvement and are ultimately limited in their effectiveness as AI partners and teaching tools.
Designing Skill-Compatible AI: Methodologies and Frameworks in Chess
Traditional chess engines designed to output near-optimal moves prove to be inadequate partners when paired with engines of various lower skill levels in this domain, as they are not designed to consider the presence of other agents.
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
Video games have served as useful benchmarks for the decision making community, but going beyond Atari games towards training agents in modern games has been prohibitively expensive for the vast majority of the research community.
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
Variance in predictions across different trained models is a significant, under-explored source of error in fair binary classification.
Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess
We present a transformer-based approach to behavioral stylometry in the context of chess, where one attempts to identify the player who played a set of games.
Mimetic Models: Ethical Implications of AI that Acts Like You
An emerging theme in artificial intelligence research is the creation of models to simulate the decisions and behavior of specific people, in domains including game-playing, text generation, and artistic expression.
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.
Sayer: Using Implicit Feedback to Optimize System Policies
We develop a methodology, called Sayer, that leverages implicit feedback to evaluate and train new system policies.
Learning Models of Individual Behavior in Chess
AI systems that can capture human-like behavior are becoming increasingly useful in situations where humans may want to learn from these systems, collaborate with them, or engage with them as partners for an extended duration.
Aligning Superhuman AI with Human Behavior: Chess as a Model System
We develop and introduce Maia, a customized version of Alpha-Zero trained on human chess games, that predicts human moves at a much higher accuracy than existing engines, and can achieve maximum accuracy when predicting decisions made by players at a specific skill level in a tuneable way.
MLSys: The New Frontier of Machine Learning Systems
Machine learning (ML) techniques are enjoying rapidly increasing adoption.
Poor Video Streaming Performance Explained (and Fixed)
We show that the root cause of the problem lies in the data plane, and that even a perfect control plane (ABR) algorithm is not enough to guarantee video flows their fair share of network bandwidth.
Networking and Internet Architecture
Making Contextual Decisions with Low Technical Debt
The Decision Service enables all aspects of contextual bandit learning using four system abstractions which connect together in a loop: explore (the decision space), log, learn, and deploy.
