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Found 24 papers, 9 papers with code
Data Acquisition via Experimental Design for Decentralized Data Markets
Acquiring high-quality training data is essential for current machine learning models.
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference
To address this issue, we introduce Chatbot Arena, an open platform for evaluating LLMs based on human preferences.
Classifier Calibration with ROC-Regularized Isotonic Regression
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.
Provably Personalized and Robust Federated Learning
Identifying clients with similar objectives and learning a model-per-cluster is an intuitive and interpretable approach to personalization in federated learning.
Doubly Robust Self-Training
Self-training is an important technique for solving semi-supervised learning problems.
Neural Dependencies Emerging from Learning Massive Categories
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.
Rank Diminishing in Deep Neural Networks
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.
Can Reinforcement Learning Efficiently Find Stackelberg-Nash Equilibria in General-Sum Markov Games?
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.
Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations
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.
Variational Refinement for Importance SamplingUsing the Forward Kullback-Leibler Divergence
Variational Inference (VI) is a popular alternative to asymptotically exact sampling in Bayesian inference.
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
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.
SAFFRON: an adaptive algorithm for online control of the false discovery rate
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.
A Berkeley View of Systems Challenges for AI
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.
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes
We propose a probabilistic model for interpreting gene expression levels that are observed through single-cell RNA sequencing.
A deep generative model for gene expression profiles from single-cell RNA sequencing
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.
Saturating Splines and Feature Selection
We extend the adaptive regression spline model by incorporating saturation, the natural requirement that a function extend as a constant outside a certain range.
Local Maxima in the Likelihood of Gaussian Mixture Models: Structural Results and Algorithmic Consequences
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.
Fast robustness quantification with variational Bayes
Bayesian hierarchical models are increasing popular in economics.
Linear Response Methods for Accurate Covariance Estimates from Mean Field Variational Bayes
We call our method linear response variational Bayes (LRVB).
High-Dimensional Continuous Control Using Generalized Advantage Estimation
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.
Nonparametric Link Prediction in Dynamic Networks
We propose a non-parametric link prediction algorithm for a sequence of graph snapshots over time.
Variational Bayesian Inference with Stochastic Search
This requires the ability to integrate a sum of terms in the log joint likelihood using this factorized distribution.
Nonparametric Link Prediction in Large Scale Dynamic Networks
We propose a nonparametric approach to link prediction in large-scale dynamic networks.
Learning Semantic Correspondences with Less Supervision
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.
