Christopher De Sa
30 papers · 2015–2025 · 10 conferences · across top CS/AI conferences
Achievements
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🌍 Conference Polyglot (10) 🌉 Interdisciplinary Bridge 🗺️ Taxonomy Completionist (13) 🧭 Keyword Pioneer 🏃 Academic Marathon (10)
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(9)
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(10)
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(2)
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Century Club
(30)
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Prolific Year
(5)
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Keyword Collector
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Unstoppable
(11)
Conferences
ICML (12)
ICLR (7)
AISTATS (3)
NIPS (2)
AAAI (1)
CVPR (1)
EMNLP (1)
IJCAI (1)
JMLR (1)
UAI (1)
Top co-authors
Research topics
Keywords
model compression
(5)
stochastic gradient descent
(4)
distributed learning
(3)
non-convex optimization
(3)
markov chain monte carlo
(3)
stochastic optimization
(3)
decentralized learning
(2)
low-precision training
(2)
bayesian sampling
(2)
distributed training
(2)
variance reduction
(2)
mixing time
(2)
gibbs sampling
(2)
gossip algorithm
(2)
bias correction
(2)
iteration complexity
(2)
representation learning
(2)
ensemble learning
(1)
variational inference
(1)
neural network pruning
(1)
Papers
Zeroth-Order Fine-Tuning of LLMs with Transferable Static Sparsity
ICLR 2025
Compute-Optimal LLMs Provably Generalize Better with Scale
ICLR 2025
Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification
AAAI 2024
Shadow Cones: A Generalized Framework for Partial Order Embeddings
ICLR 2024
QuIP$#$: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks
ICML 2024
Searching for Efficient Linear Layers over a Continuous Space of Structured Matrices
NIPS 2024
QTIP: Quantization with Trellises and Incoherence Processing
NIPS 2024
Inference for probabilistic dependency graphs
UAI 2023
Random Laplacian Features for Learning with Hyperbolic Space
ICLR 2023
Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam
ICLR 2023
STEP: Learning N:M Structured Sparsity Masks from Scratch with Precondition
ICML 2023
CocktailSGD: Fine-tuning Foundation Models over 500Mbps Networks
ICML 2023
InfoDiffusion: Representation Learning Using Information Maximizing Diffusion Models
ICML 2023
Decentralized Learning: Theoretical Optimality and Practical Improvements
JMLR 2023
Low-Precision Stochastic Gradient Langevin Dynamics
ICML 2022
A General Analysis of Example-Selection for Stochastic Gradient Descent
ICLR 2022
How Low Can We Go: Trading Memory for Error in Low-Precision Training
ICLR 2022
Meta-Learning Divergences for Variational Inference
AISTATS 2021
Low-Precision Reinforcement Learning: Running Soft Actor-Critic in Half Precision
ICML 2021
Optimal Complexity in Decentralized Training
ICML 2021
Variance Reduced Training with Stratified Sampling for Forecasting Models
ICML 2021
‘Tecnologica cosa’: Modeling Storyteller Personalities in Boccaccio’s ‘Decameron’
EMNLP 2021
Differentiating through the Fréchet Mean
ICML 2020
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
AISTATS 2020
Moniqua: Modulo Quantized Communication in Decentralized SGD
ICML 2020
Building Efficient Deep Neural Networks With Unitary Group Convolutions
CVPR 2019
Accelerated Stochastic Power Iteration
AISTATS 2018
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
IJCAI 2017
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
ICML 2016
Global Convergence of Stochastic Gradient Descent for Some Non-convex Matrix Problems
ICML 2015