no code implementations • ICML 2020 • Liu Leqi, Justin Khim, Adarsh Prasad, Pradeep Ravikumar
In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning.
no code implementations • ICML 2020 • Liu Leqi, Justin Khim, Adarsh Prasad, Pradeep Ravikumar
In this work, we study a novel notion of L-Risk based on the classical idea of rank-weighted learning.
1 code implementation • 17 Oct 2024 • Hui Yuan, Yifan Zeng, Yue Wu, Huazheng Wang, Mengdi Wang, Liu Leqi
In this paper, we identify a common pitfall of margin-based methods -- the under-specification of ideal LM behavior on preferred and dispreferred responses individually, which leads to two unintended consequences as the margin increases: (1) The probability of dispreferred (e. g., unsafe) responses may increase, resulting in potential safety alignment failures.
no code implementations • 20 Sep 2024 • Vibhhu Sharma, Shantanu Gupta, Nil-Jana Akpinar, Zachary C. Lipton, Liu Leqi
In this paper, we view recommender system auditing from a causal lens and provide a general recipe for defining auditing metrics.
no code implementations • 18 Apr 2024 • Sarah Dean, Evan Dong, Meena Jagadeesan, Liu Leqi
As AI systems enter into a growing number of societal domains, these systems increasingly shape and are shaped by user preferences, opinions, and behaviors.
no code implementations • 13 Mar 2024 • Jingling Li, Zeyu Tang, Xiaoyu Liu, Peter Spirtes, Kun Zhang, Liu Leqi, Yang Liu
Large language models (LLMs) can easily generate biased and discriminative responses.
1 code implementation • 6 Feb 2024 • Xinyu Li, Ruiyang Zhou, Zachary C. Lipton, Liu Leqi
While Reinforcement Learning from Human Feedback (RLHF) is a commonly used framework for aligning LLMs with human preferences, vanilla RLHF assumes that all human preferences share the same distribution, preventing fine-tuned LLMs from generating personalized content when user preferences are diverse.
1 code implementation • 16 Apr 2023 • Liu Leqi, Giulio Zhou, Fatma Kılınç-Karzan, Zachary C. Lipton, Alan L. Montgomery
While answering these questions, we provide a flexible experimental framework for understanding human preference dynamics and testing MABs algorithms with human users.
no code implementations • 21 Sep 2022 • Audrey Huang, Liu Leqi, Zachary Chase Lipton, Kamyar Azizzadenesheli
To mitigate these problems, we incorporate model-based estimation to develop the first doubly robust (DR) estimator for the CDF of returns in MDPs.
no code implementations • 27 Jun 2022 • Liu Leqi, Audrey Huang, Zachary C. Lipton, Kamyar Azizzadenesheli
Standard uniform convergence results bound the generalization gap of the expected loss over a hypothesis class.
no code implementations • 22 Apr 2022 • Charvi Rastogi, Liu Leqi, Kenneth Holstein, Hoda Heidari
To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity.
no code implementations • 25 Mar 2022 • Omer Ben-Porat, Lee Cohen, Liu Leqi, Zachary C. Lipton, Yishay Mansour
We first address the case where all users share the same type, demonstrating that a recent UCB-based algorithm is optimal.
no code implementations • 19 Jan 2022 • Yueyi Jiang, Yunfan Jiang, Liu Leqi, Piotr Winkielman
To examine how different forms of loneliness and coping strategies manifest in loneliness self-disclosure, we built a dataset, FIG-Loneliness (FIne-Grained Loneliness) by using Reddit posts in two young adult-focused forums and two loneliness related forums consisting of a diverse age group.
no code implementations • 12 Oct 2021 • Biwei Huang, Chaochao Lu, Liu Leqi, José Miguel Hernández-Lobato, Clark Glymour, Bernhard Schölkopf, Kun Zhang
Perceived signals in real-world scenarios are usually high-dimensional and noisy, and finding and using their representation that contains essential and sufficient information required by downstream decision-making tasks will help improve computational efficiency and generalization ability in the tasks.
no code implementations • NeurIPS 2021 • Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli
Even when unable to run experiments, practitioners can evaluate prospective policies, using previously logged data.
no code implementations • 4 Mar 2021 • Audrey Huang, Liu Leqi, Zachary C. Lipton, Kamyar Azizzadenesheli
Because optimizing the coherent risk is difficult in Markov decision processes, recent work tends to focus on the Markov coherent risk (MCR), a time-consistent surrogate.
no code implementations • 2 Mar 2021 • Liu Leqi, Edward H. Kennedy
In this work, we propose a new median optimal treatment regime that instead treats individuals whose conditional median is higher under treatment.
no code implementations • NeurIPS 2021 • Liu Leqi, Fatma Kilinc-Karzan, Zachary C. Lipton, Alan L. Montgomery
Psychological research shows that enjoyment of many goods is subject to satiation, with short-term satisfaction declining after repeated exposures to the same item.
no code implementations • NeurIPS 2019 • Fan Yang, Liu Leqi, Yifan Wu, Zachary C. Lipton, Pradeep Ravikumar, William W. Cohen, Tom Mitchell
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems.
2 code implementations • 2 Dec 2019 • David I. Inouye, Liu Leqi, Joon Sik Kim, Bryon Aragam, Pradeep Ravikumar
To address these drawbacks, we formalize a method for automating the selection of interesting PDPs and extend PDPs beyond showing single features to show the model response along arbitrary directions, for example in raw feature space or a latent space arising from some generative model.
no code implementations • NeurIPS 2019 • Liu Leqi, Adarsh Prasad, Pradeep K. Ravikumar
The statistical decision theoretic foundations of modern machine learning have largely focused on the minimization of the expectation of some loss function for a given task.
no code implementations • NeurIPS 2018 • Chen Dan, Liu Leqi, Bryon Aragam, Pradeep K. Ravikumar, Eric P. Xing
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions.
no code implementations • NeurIPS 2018 • Chen Dan, Liu Leqi, Bryon Aragam, Pradeep Ravikumar, Eric P. Xing
We study the sample complexity of semi-supervised learning (SSL) and introduce new assumptions based on the mismatch between a mixture model learned from unlabeled data and the true mixture model induced by the (unknown) class conditional distributions.