Christopher M De Sa
24 papers · 2015–2023 · 1 conference · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (44) π Academic Marathon (8) π Renaissance Researcher (7) π§ Keyword Pioneer
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(15)
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Renaissance Researcher
(7)
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Conference Loyalist
(24)
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Keyword Champion
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Trend Setter
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Century Club
(24)
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Unstoppable
(5)
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Prolific Year
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Keyword Collector
(101)
Conferences
NIPS (24)
Top co-authors
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