Search Results for author:
Found 15 papers, 11 papers with code
Evaluating Frontier Models for Dangerous Capabilities
To understand the risks posed by a new AI system, we must understand what it can and cannot do.
Grandmaster-Level Chess Without Search
Unlike traditional chess engines that rely on complex heuristics, explicit search, or a combination of both, we train a 270M parameter transformer model with supervised learning on a dataset of 10 million chess games.
Learning Universal Predictors
Meta-learning has emerged as a powerful approach to train neural networks to learn new tasks quickly from limited data.
Distributional Bellman Operators over Mean Embeddings
We propose a novel algorithmic framework for distributional reinforcement learning, based on learning finite-dimensional mean embeddings of return distributions.
Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles.
Language Modeling Is Compression
We show that large language models are powerful general-purpose predictors and that the compression viewpoint provides novel insights into scaling laws, tokenization, and in-context learning.
Randomized Positional Encodings Boost Length Generalization of Transformers
Transformers have impressive generalization capabilities on tasks with a fixed context length.
Memory-Based Meta-Learning on Non-Stationary Distributions
Memory-based meta-learning is a technique for approximating Bayes-optimal predictors.
Beyond Bayes-optimality: meta-learning what you know you don't know
This is in contrast to risk-sensitive agents, which additionally exploit the higher-order moments of the return, and ambiguity-sensitive agents, which act differently when recognizing situations in which they lack knowledge.
Neural Networks and the Chomsky Hierarchy
Reliable generalization lies at the heart of safe ML and AI.
Latent Space Smoothing for Individually Fair Representations
This enables us to learn individually fair representations that map similar individuals close together by using adversarial training to minimize the distance between their representations.
Fair Normalizing Flows
Fair representation learning is an attractive approach that promises fairness of downstream predictors by encoding sensitive data.
Robustness Certification for Point Cloud Models
In this work, we address this challenge and introduce 3DCertify, the first verifier able to certify the robustness of point cloud models.
Efficient Certification of Spatial Robustness
Recent work has exposed the vulnerability of computer vision models to vector field attacks.
Learning Certified Individually Fair Representations
That is, our method enables the data producer to learn and certify a representation where for a data point all similar individuals are at $\ell_\infty$-distance at most $\epsilon$, thus allowing data consumers to certify individual fairness by proving $\epsilon$-robustness of their classifier.
