18 citations · 19 across the 14 of their papers we have counts for
14 papers
QuEPT: Quantized Elastic Precision Transformers with One-Shot Calibration for Multi-Bit Switching
Ke Xu, Yixin Wang, Zhongcheng Li +3
Elastic precision quantization enables multi-bit deployment via a single optimization pass, fitting diverse quantization scenarios.Yet, the high storage and optimization costs asso…
Causal Representation Meets Stochastic Modeling under Generic Geometry
Jiaxu Ren, Yixin Wang, Biwei Huang
Learning meaningful causal representations from observations has emerged as a crucial task for facilitating machine learning applications and driving scientific discoveries in fiel…
Meta-probabilistic Modeling
Kevin Zhang, Yixin Wang
Probabilistic graphical models (PGMs) are widely used to discover latent structure in data, but their success hinges on selecting an appropriate model design. In practice, model sp…
Counterfactual Voting Adjustment for Quality Assessment and Fairer Voting in Online Platforms with Helpfulness Evaluation
Chang Liu, Yixin Wang, Moontae Lee
Efficient access to high-quality information is vital for online platforms. To promote more useful information, users not only create new content but also evaluate existing content…
Bayesian Invariance Modeling of Multi-Environment Data
Luhuan Wu, Mingzhang Yin, Yixin Wang +2
Invariant prediction [Peters et al., 2016] analyzes feature/outcome data from multiple environments to identify invariant features - those with a stable predictive relationship to…
Let Me Grok for You: Accelerating Grokking via Embedding Transfer from a Weaker Model
Zhiwei Xu, Zhiyu Ni, Yixin Wang +1
''Grokking'' is a phenomenon where a neural network first memorizes training data and generalizes poorly, but then suddenly transitions to near-perfect generalization after prolong…
Doubly robust identification of treatment effects from multiple environments
Piersilvio De Bartolomeis, Julia Kostin, Javier Abad +2
Practical and ethical constraints often require the use of observational data for causal inference, particularly in medicine and social sciences. Yet, observational datasets are pr…
Explanation Design in Strategic Learning: Sufficient Explanations that Induce Non-harmful Responses
Kiet Q. H. Vo, Siu Lun Chau, Masahiro Kato +2
We study explanation design in algorithmic decision making with strategic agents, individuals who may modify their inputs in response to explanations of a decision maker's (DM's) p…
Posterior Mean Matching: Generative Modeling through Online Bayesian Inference
Sebastian Salazar, Michal Kucer, Yixin Wang +2
This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model…
Permutative Preference Alignment from Listwise Ranking of Human Judgments
Yang Zhao, Yixin Wang, Mingzhang Yin
Aligning Large Language Models (LLMs) with human preferences is crucial in ensuring desirable and controllable model behaviors. Current methods, such as Reinforcement Learning from…
LLMs Are Prone to Fallacies in Causal Inference
Nitish Joshi, Abulhair Saparov, Yixin Wang +1
Recent work shows that causal facts can be effectively extracted from LLMs through prompting, facilitating the creation of causal graphs for causal inference tasks. However, it is…
Addressing Discretization-Induced Bias in Demographic Prediction
Evan Dong, Aaron Schein, Yixin Wang +1
Racial and other demographic imputation is necessary for many applications, especially in auditing disparities and outreach targeting in political campaigns. The canonical approach…
On the Challenges and Opportunities in Generative AI
Laura Manduchi, Clara Meister, Kushagra Pandey +23
The field of deep generative modeling has grown rapidly in the last few years. With the availability of massive amounts of training data coupled with advances in scalable unsupervi…
Multi-Domain Causal Representation Learning via Weak Distributional Invariances
Kartik Ahuja, Amin Mansouri, Yixin Wang
Causal representation learning has emerged as the center of action in causal machine learning research. In particular, multi-domain datasets present a natural opportunity for showc…