Shashank Rajput
15 papers · 2019–2024 · 6 conferences · across top CS/AI conferences
Achievements
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🐣 Hot Topic Early Bird 🏃 Academic Marathon (5) 🧭 Keyword Pioneer 🌍 Conference Polyglot (6) 🐝 Cross-Pollinator (12)
🌍
Conference Polyglot
(6)
🏃
Academic Marathon
(5)
🧭
Keyword Pioneer
🤝
Dynamic Duo
(12)
🗃️
Keyword Collector
(71)
❓
The Questioner
(2)
⚡
Prolific Year
(5)
💎
Century Club
(15)
🔥
Unstoppable
(6)
Conferences
NIPS (6)
ICML (4)
ICLR (2)
AISTATS (1)
COLT (1)
EMNLP (1)
Top co-authors
Keywords
relu network
(2)
neural network pruning
(2)
image classification
(1)
lottery ticket hypothesis
(1)
attention mechanism
(1)
stochastic gradient descent
(1)
transfer learning
(1)
in-context learning
(1)
collaborative filtering
(1)
model robustness
(1)
adversarial learning
(1)
adversarial machine learning
(1)
multilingual nlp
(1)
data augmentation
(1)
learning theory
(1)
batch normalization
(1)
cross-lingual transfer
(1)
prompt engineering
(1)
multimodal learning
(1)
federated learning
(1)
Papers
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
ICML 2024
The Expressive Power of Tuning Only the Normalization Layers
COLT 2023
Recommender Systems with Generative Retrieval
NIPS 2023
Looped Transformers as Programmable Computers
ICML 2023
LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks
NIPS 2022
Finding Nearly Everything within Random Binary Networks
AISTATS 2022
Utilizing Language-Image Pretraining for Efficient and Robust Bilingual Word Alignment
EMNLP 2022
Permutation-Based SGD: Is Random Optimal?
ICLR 2022
Minibatch vs Local SGD with Shuffling: Tight Convergence Bounds and Beyond
ICLR 2022
An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks
NIPS 2021
Attack of the Tails: Yes, You Really Can Backdoor Federated Learning
NIPS 2020
Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient
NIPS 2020
Closing the convergence gap of SGD without replacement
ICML 2020
Does Data Augmentation Lead to Positive Margin?
ICML 2019
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation
NIPS 2019