no code implementations • 7 May 2024 • Hanlin Zhu, Baihe Huang, Shaolun Zhang, Michael Jordan, Jiantao Jiao, Yuandong Tian, Stuart Russell
Auto-regressive large language models (LLMs) show impressive capacities to solve many complex reasoning tasks while struggling with some simple logical reasoning tasks such as inverse search: when trained on ''A is B'', LLM fails to directly conclude ''B is A'' during inference, which is known as the ''reversal curse'' (Berglund et al., 2023).
no code implementations • 20 Mar 2024 • Charles Lu, Baihe Huang, Sai Praneeth Karimireddy, Praneeth Vepakomma, Michael Jordan, Ramesh Raskar
Acquiring high-quality training data is essential for current machine learning models.
1 code implementation • 7 Mar 2024 • Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E. Gonzalez, Ion Stoica
To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences.
no code implementations • 21 Nov 2023 • Eugene Berta, Francis Bach, Michael Jordan
IR acts as an adaptive binning procedure, which allows achieving a calibration error of zero, but leaves open the issue of the effect on performance.
1 code implementation • 14 Jun 2023 • Mariel Werner, Lie He, Michael Jordan, Martin Jaggi, Sai Praneeth Karimireddy
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning.
1 code implementation • 1 Jun 2023 • Banghua Zhu, Mingyu Ding, Philip Jacobson, Ming Wu, Wei Zhan, Michael Jordan, Jiantao Jiao
Self-training is an important technique for solving semi-supervised learning problems.
no code implementations • CVPR 2023 • Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen, Jian Zhao, Yukun Huang, Deli Zhao, Jingren Zhou, Michael Jordan, Zheng-Jun Zha
Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others.
no code implementations • 13 Jun 2022 • Ruili Feng, Kecheng Zheng, Yukun Huang, Deli Zhao, Michael Jordan, Zheng-Jun Zha
By virtue of our numerical tools, we provide the first empirical analysis of the per-layer behavior of network rank in practical settings, i. e., ResNets, deep MLPs, and Transformers on ImageNet.
no code implementations • 29 Sep 2021 • Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael Jordan
To our best knowledge, we establish the first provably efficient RL algorithms for solving SNE in general-sum Markov games with leader-controlled state transitions.
no code implementations • NeurIPS 2020 • Zhuoran Yang, Chi Jin, Zhaoran Wang, Mengdi Wang, Michael Jordan
Reinforcement learning (RL) algorithms combined with modern function approximators such as kernel functions and deep neural networks have achieved significant empirical successes in large-scale application problems with a massive number of states.
no code implementations • pproximateinference AABI Symposium 2021 • Ghassen Jerfel, Serena Lutong Wang, Clara Fannjiang, Katherine A Heller, Yian Ma, Michael Jordan
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference.
2 code implementations • International Conference on Machine Learning 2019 • Kaichao You, Ximei Wang, Mingsheng Long, Michael Jordan
Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain.
1 code implementation • ICML 2018 • Aaditya Ramdas, Tijana Zrnic, Martin Wainwright, Michael Jordan
However, unlike older methods, SAFFRON's threshold sequence is based on a novel estimate of the alpha fraction that it allocates to true null hypotheses.
no code implementations • 15 Dec 2017 • Ion Stoica, Dawn Song, Raluca Ada Popa, David Patterson, Michael W. Mahoney, Randy Katz, Anthony D. Joseph, Michael Jordan, Joseph M. Hellerstein, Joseph E. Gonzalez, Ken Goldberg, Ali Ghodsi, David Culler, Pieter Abbeel
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies such as digital advertising and intelligent infrastructures, AI (Artificial Intelligence) has moved from research labs to production.
no code implementations • 13 Oct 2017 • Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing.
2 code implementations • 7 Sep 2017 • Romain Lopez, Jeffrey Regier, Michael Cole, Michael Jordan, Nir Yosef
We also extend our framework to account for batch effects and other confounding factors, and propose a Bayesian hypothesis test for differential expression that outperforms DESeq2.
no code implementations • 21 Sep 2016 • Nicholas Boyd, Trevor Hastie, Stephen Boyd, Benjamin Recht, Michael Jordan
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range.
no code implementations • NeurIPS 2016 • Chi Jin, Yuchen Zhang, Sivaraman Balakrishnan, Martin J. Wainwright, Michael Jordan
Our first main result shows that the population likelihood function has bad local maxima even in the special case of equally-weighted mixtures of well-separated and spherical Gaussians.
no code implementations • 23 Jun 2016 • Ryan Giordano, Tamara Broderick, Rachael Meager, Jonathan Huggins, Michael Jordan
Bayesian hierarchical models are increasing popular in economics.
1 code implementation • NeurIPS 2015 • Ryan Giordano, Tamara Broderick, Michael Jordan
We call our method linear response variational Bayes (LRVB).
17 code implementations • 8 Jun 2015 • John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel
Policy gradient methods are an appealing approach in reinforcement learning because they directly optimize the cumulative reward and can straightforwardly be used with nonlinear function approximators such as neural networks.
no code implementations • 27 Jun 2012 • John Paisley, David Blei, Michael Jordan
This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution.
no code implementations • 27 Jun 2012 • Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time.
no code implementations • 6 Sep 2011 • Purnamrita Sarkar, Deepayan Chakrabarti, Michael Jordan
We propose a nonparametric approach to link prediction in large-scale dynamic networks.
1 code implementation • 1 Aug 2009 • Percy Liang, Michael Jordan, Dan Klein
A central problem in grounded language acquisition is learning the correspondences between a rich world state and a stream of text which references that world state.