24 citations · 60 across the 10 of their papers we have counts for
10 papers
Inference for Probabilistic Dependency Graphs
Oliver E. Richardson, Joseph Y. Halpern, Christopher De Sa
Probabilistic dependency graphs (PDGs) are a flexible class of probabilistic graphical models, subsuming Bayesian Networks and Factor Graphs. They can also capture inconsistent bel…
QuIP: 2-Bit Quantization of Large Language Models With Guarantees
Jerry Chee, Yaohui Cai, Volodymyr Kuleshov +1
This work studies post-training parameter quantization in large language models (LLMs). We introduce quantization with incoherence processing (QuIP), a new method based on the insi…
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
Yingheng Wang, Yair Schiff, Aaron Gokaslan +4
While diffusion models excel at generating high-quality samples, their latent variables typically lack semantic meaning and are not suitable for representation learning. Here, we p…
TART: A plug-and-play Transformer module for task-agnostic reasoning
Kush Bhatia, Avanika Narayan, Christopher De Sa +1
Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, traditional…
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent
Satyen Kale, Jason D. Lee, Chris De Sa +2
Stochastic Gradient Descent (SGD) has been the method of choice for learning large-scale non-convex models. While a general analysis of when SGD works has been elusive, there has b…
MCTensor: A High-Precision Deep Learning Library with Multi-Component Floating-Point
Tao Yu, Wentao Guo, Jianan Canal Li +2
In this paper, we introduce MCTensor, a library based on PyTorch for providing general-purpose and high-precision arithmetic for DL training. MCTensor is used in the same way as Py…
Low-Precision Stochastic Gradient Langevin Dynamics
Ruqi Zhang, Andrew Gordon Wilson, Christopher De Sa
While low-precision optimization has been widely used to accelerate deep learning, low-precision sampling remains largely unexplored. As a consequence, sampling is simply infeasibl…
Non-Determinism and the Lawlessness of Machine Learning Code
A. Feder Cooper, Jonathan Frankle, Christopher De Sa
Legal literature on machine learning (ML) tends to focus on harms, and thus tends to reason about individual model outcomes and summary error rates. This focus has masked important…
Random Laplacian Features for Learning with Hyperbolic Space
Tao Yu, Christopher De Sa
Due to its geometric properties, hyperbolic space can support high-fidelity embeddings of tree- and graph-structured data, upon which various hyperbolic networks have been develope…
Model Selection's Disparate Impact in Real-World Deep Learning Applications
Jessica Zosa Forde, A. Feder Cooper, Kweku Kwegyir-Aggrey +2
Algorithmic fairness has emphasized the role of biased data in automated decision outcomes. Recently, there has been a shift in attention to sources of bias that implicate fairness…