243 citations · 443 across the 32 of their papers we have counts for
13 papers · 1 filter
Sample-Efficient Learning of POMDPs with Multiple Observations In Hindsight
Jiacheng Guo, Minshuo Chen, Huan Wang +3
This paper studies the sample-efficiency of learning in Partially Observable Markov Decision Processes (POMDPs), a challenging problem in reinforcement learning that is known to be…
Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selection
Yu Bai, Fan Chen, Huan Wang +2
Neural sequence models based on the transformer architecture have demonstrated remarkable \emph{in-context learning} (ICL) abilities, where they can perform new tasks when prompted…
On the Unlikelihood of D-Separation
Itai Feigenbaum, Huan Wang, Shelby Heinecke +4
Causal discovery aims to recover a causal graph from data generated by it; constraint based methods do so by searching for a d-separating conditioning set of nodes in the graph via…
Improved Online Conformal Prediction via Strongly Adaptive Online Learning
Aadyot Bhatnagar, Huan Wang, Caiming Xiong +1
We study the problem of uncertainty quantification via prediction sets, in an online setting where the data distribution may vary arbitrarily over time. Recent work develops online…
Lower Bounds for Learning in Revealing POMDPs
Fan Chen, Huan Wang, Caiming Xiong +2
This paper studies the fundamental limits of reinforcement learning (RL) in the challenging \emph{partially observable} setting. While it is well-established that learning in Parti…
Salesforce CausalAI Library: A Fast and Scalable Framework for Causal Analysis of Time Series and Tabular Data
Devansh Arpit, Matthew Fernandez, Itai Feigenbaum +11
We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and…
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
Runyu Zhang, Qinghua Liu, Huan Wang +3
This paper studies policy optimization algorithms for multi-agent reinforcement learning. We begin by proposing an algorithm framework for two-player zero-sum Markov Games in the f…
Dual Lottery Ticket Hypothesis
Yue Bai, Huan Wang, Zhiqiang Tao +2
Fully exploiting the learning capacity of neural networks requires overparameterized dense networks. On the other side, directly training sparse neural networks typically results i…
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi +5
Program synthesis strives to generate a computer program as a solution to a given problem specification, expressed with input-output examples or natural language descriptions. The…
Efficient and Differentiable Conformal Prediction with General Function Classes
Yu Bai, Song Mei, Huan Wang +2
Quantifying the data uncertainty in learning tasks is often done by learning a prediction interval or prediction set of the label given the input. Two commonly desired properties f…
Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games
Yu Bai, Chi Jin, Huan Wang +1
Real world applications such as economics and policy making often involve solving multi-agent games with two unique features: (1) The agents are inherently asymmetric and partition…
Don't Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification
Yu Bai, Song Mei, Huan Wang +1
Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident. It…
How Important is the Train-Validation Split in Meta-Learning?
Yu Bai, Minshuo Chen, Pan Zhou +5
Meta-learning aims to perform fast adaptation on a new task through learning a "prior" from multiple existing tasks. A common practice in meta-learning is to perform a train-valida…