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Hi there šŸ‘‹

I am Thanh, a ML scientist. I am building toward data-efficient, runtime-efficient and robust AI by studying three foundational pillars of modern machine learning ā€“ provable statistical efficiency, computational efficiency, and robustness. My current focus includes:

  • Reinforcement Learning
  • Learning under Distributional Shifts
  • Robust Adversarial Learning
  • Probabilistic Deep Learning
  • Representation Learning

Iā€™m always actively open to research collaborations and chat! More info at my homepage.

thanhnt's Projects

drbqo icon drbqo

Code for our AISTATS 2020 paper "Distributionally Robust Bayesian Quadrature Optimization"

ml-deadlines icon ml-deadlines

:alarm_clock: Top-tier ML conference deadline countdowns

mmdrl icon mmdrl

Official repo for our AAAI'21 paper, https://arxiv.org/abs/2007.12354

neural-offline-rl icon neural-offline-rl

The official code of our top-25%-noble paper at ICLR'23: "Provably Efficient Neural Offline Reinforcement Learning via Perturbed Rewards"

offline_neural_bandits icon offline_neural_bandits

An official JAX-based code for our NeuraLCB paper, "Offline Neural Contextual Bandits: Pessimism, Optimization and Generalization", ICLR 2022.

pyrl icon pyrl

Simple implementation of Reinforcement Learning algorithms

whats-cooking icon whats-cooking

Scripts for my model in Kaggle's competition: What's Cooking

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