← Authors

Christopher M De Sa

24 papers · 2015–2023 · 1 conference · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (44) 🧭 Keyword Pioneer πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (8) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) 🌈 Renaissance Researcher (7) 🏠 Conference Loyalist (24) πŸ† Keyword Champion πŸ“ˆ Trend Setter πŸ’Ž Century Club (24) πŸ”₯ Unstoppable (5) ⚑ Prolific Year (5) πŸ—ƒοΈ Keyword Collector (101)

Conferences

NIPS (24)

Top co-authors

Christopher Re (6) Tao Yu (4) Ser Nam Lim (3) Yucheng Lu (3) Aaron Lou (3) Isay Katsman (3) A. Feder Cooper (3) Ce Zhang (2) Jerry Chee (2) Qingxuan Jiang (2)

Keywords

stochastic gradient descent (6) markov chain monte carlo (4) convergence analysis (4) model compression (3) non-convex optimization (3) hyperbolic embedding (3) generative model (3) gibbs sampling (3) hierarchical datum (2) manifold learning (2) random fourier feature (2) gradient balancing (2) representation learning (2) hyperbolic space (2) convergence rate (2) random reshuffling (2) graphical model (2) mixing time (2) convergence guarantee (2) large language model (2)

Papers

QuIP: 2-Bit Quantization of Large Language Models With Guarantees NIPS 2023
Riemannian Residual Neural Networks NIPS 2023
Coordinating Distributed Example Orders for Provably Accelerated Training NIPS 2023
Coneheads: Hierarchy Aware Attention NIPS 2023
TART: A plug-and-play Transformer module for task-agnostic reasoning NIPS 2023
GraB: Finding Provably Better Data Permutations than Random Reshuffling NIPS 2022
Model Preserving Compression for Neural Networks NIPS 2022
From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent NIPS 2022
Understanding Hyperdimensional Computing for Parallel Single-Pass Learning NIPS 2022
Hyperparameter Optimization Is Deceiving Us, and How to Stop It NIPS 2021
Equivariant Manifold Flows NIPS 2021
Representing Hyperbolic Space Accurately using Multi-Component Floats NIPS 2021
Random Reshuffling is Not Always Better NIPS 2020
Asymptotically Optimal Exact Minibatch Metropolis-Hastings NIPS 2020
Neural Manifold Ordinary Differential Equations NIPS 2020
Channel Gating Neural Networks NIPS 2019
Dimension-Free Bounds for Low-Precision Training NIPS 2019
Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models NIPS 2019
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees NIPS 2019
Gaussian Quadrature for Kernel Features NIPS 2017
Data Programming: Creating Large Training Sets, Quickly NIPS 2016
Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much NIPS 2016
Rapidly Mixing Gibbs Sampling for a Class of Factor Graphs Using Hierarchy Width NIPS 2015
Taming the Wild: A Unified Analysis of Hogwild-Style Algorithms NIPS 2015