GenAI4ELab/papercli-papers huggingface dataset:
Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs
Authors: Kaichao You; Yong Liu; Ziyang Zhang; Jianmin Wang; Michael I. Jordan; Mingsheng Long
Venue: JMLR 2022 (main)

Abstract: Model hubs with many pre-trained models (PTMs) have become a cornerstone of deep learning. Although built at a high cost, they remain under-exploited---practitioners usually pick one PTM from the provided model hub by popularity and then fine-tune the PTM to solve the target task. This naÃ¯ve but common practice poses two obstacles to full exploitation of pre-trained model hubs: first, the PTM selection by popularity has no optimality guarantee, and second, only one PTM is used while the remaining PTMs are ignored. An alternative might be to consider all possible combinations of PTMs and extensively fine-tune each combination, but this would not only be prohibitive computationally but may also lead to statistical over-fitting. In this paper, we propose a new paradigm for exploiting model hubs that is intermediate between these extremes.  The paradigm is characterized by two aspects: (1) We use an evidence maximization procedure to estimate the maximum value of label evidence given features extracted by pre-trained models.  This procedure can rank all the PTMs in a model hub for various types of PTMs and tasks before fine-tuning. (2) The best ranked PTM can either be fine-tuned and deployed if we have no preference for the model's architecture or the target PTM can be tuned by the top $K$ ranked PTMs via a Bayesian procedure that we propose. This procedure, which we refer to as B-Tuning, not only improves upon specialized methods designed for tuning homogeneous PTMs, but also applies to the challenging problem of tuning heterogeneous PTMs where it yields a new level of benchmark performance.
A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review
Authors: Michael Pearce; Elena A. Erosheva
Venue: JMLR 2022 (main)

Abstract: Rankings and scores are two common data types used by judges to express preferences and/or perceptions of quality in a collection of objects. Numerous models exist to study data of each type separately, but no unified statistical model captures both data types simultaneously without first performing data conversion. We propose the Mallows-Binomial model to close this gap, which combines a Mallows $\phi$ ranking model with Binomial score models through shared parameters that quantify object quality, a consensus ranking, and the level of consensus among judges. We propose an efficient tree-search algorithm to calculate the exact MLE of model parameters, study statistical properties of the model both analytically and through simulation, and apply our model to real data from an instance of grant panel review that collected both scores and partial rankings. Furthermore, we demonstrate how model outputs can be used to rank objects with confidence. The proposed model is shown to sensibly combine information from both scores and rankings to quantify object quality and measure consensus with appropriate levels of statistical uncertainty.
Efficient Inference for Dynamic Flexible Interactions of Neural Populations
Authors: Feng Zhou; Quyu Kong; Zhijie Deng; Jichao Kan; Yixuan Zhang; Cheng Feng; Jun Zhu
Venue: JMLR 2022 (main)

Abstract: Hawkes process provides an effective statistical framework for analyzing the interactions of neural spiking activities. Although utilized in many real applications, the classic Hawkes process is incapable of modeling inhibitory interactions among neural population. Instead, the nonlinear Hawkes process allows for modeling a more flexible influence pattern with excitatory or inhibitory interactions. This work proposes a flexible nonlinear Hawkes process variant based on sigmoid nonlinearity. To ease inference, three sets of auxiliary latent variables (Polya-Gamma variables, latent marked Poisson processes and sparsity variables) are augmented to make functional connection weights appear in a Gaussian form, which enables simple iterative algorithms with analytical updates. As a result, the efficient Gibbs sampler, expectation-maximization algorithm and mean-field approximation are derived to estimate the interactions among neural populations. Furthermore, to reconcile with time-varying neural systems, the proposed time-invariant model is extended to a dynamic version by introducing a Markov state process. Similarly, three analytical iterative inference algorithms: Gibbs sampler, EM algorithm and mean-field approximation are derived. We compare the accuracy and efficiency of these inference algorithms on synthetic data, and further experiment on real neural recordings to demonstrate that the developed models achieve superior performance over the state-of-the-art competitors.
Multi-Agent Multi-Armed Bandits with Limited Communication
Authors: Mridul Agarwal; Vaneet Aggarwal; Kamyar Azizzadenesheli
Venue: JMLR 2022 (main)

Abstract: We consider the problem where $N$ agents collaboratively interact with an instance of a stochastic $K$ arm bandit problem for $K \gg N$. The agents aim to simultaneously minimize the cumulative regret over all the agents for a total of $T$ time steps, the number of communication rounds, and the number of bits in each communication round.  We present Limited Communication Collaboration - Upper Confidence Bound (LCC-UCB), a doubling-epoch based algorithm where each agent communicates only after the end of the epoch and shares the index of the best arm it knows. With our algorithm, LCC-UCB, each agent enjoys a regret of $\tilde{O}\left(\sqrt{({K/N}+ N)T}\right)$, communicates for $O(\log T)$ steps and broadcasts $O(\log K)$ bits in each communication step. We extend the work to sparse graphs with maximum degree $K_G$ and diameter $D$ to propose LCC-UCB-GRAPH which enjoys a regret bound of $\tilde{O}\left(D\sqrt{(K/N+ K_G)DT}\right)$. Finally, we empirically show that the LCC-UCB and the LCC-UCB-GRAPH algorithms perform well and outperform strategies that communicate through a central node.
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Authors: Lars H. B. Olsen; Ingrid K. Glad; Martin Jullum; Kjersti Aas
Venue: JMLR 2022 (main)

Abstract: Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the dependencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing methods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.
When is the Convergence Time of Langevin Algorithms Dimension Independent? A Composite Optimization Viewpoint
Authors: Yoav Freund; Yi-An Ma; Tong Zhang
Venue: JMLR 2022 (main)

Abstract: There has been a surge of works bridging MCMC sampling and optimization, with a specific focus on translating non-asymptotic convergence guarantees for optimization problems into the analysis of Langevin algorithms in MCMC sampling. A conspicuous distinction between the convergence analysis of Langevin sampling and that of optimization is that all known convergence rates for Langevin algorithms depend on the dimensionality of the problem, whereas the convergence rates for optimization are dimension-free for convex problems. Whether a dimension independent convergence rate can be achieved by the Langevin algorithm is thus a long-standing open problem. This paper provides an affirmative answer to this problem for the case of either Lipschitz or smooth convex functions with normal priors. By viewing Langevin algorithm as composite optimization, we develop a new analysis technique that leads to dimension independent convergence rates for such problems.
Learning Operators with Coupled Attention
Authors: Georgios Kissas; Jacob H. Seidman; Leonardo Ferreira Guilhoto; Victor M. Preciado; George J. Pappas; Paris Perdikaris
Venue: JMLR 2022 (main)

Abstract: Supervised operator learning is an emerging machine learning paradigm with applications to modeling the evolution of spatio-temporal dynamical systems and approximating general black-box relationships between functional data. We propose a novel operator learning method, LOCA (Learning Operators with Coupled Attention), motivated from the recent success of the attention mechanism. In our architecture, the input functions are mapped to a finite set of features which are then averaged with attention weights that depend on the output query locations. By coupling these attention weights together with an integral transform, LOCA is able to explicitly learn correlations in the target output functions, enabling us to approximate nonlinear operators even when the number of output function measurements in the training set is very small. Our formulation is accompanied by rigorous approximation theoretic guarantees on the universal expressiveness of the proposed model. Empirically, we evaluate the performance of LOCA on several operator learning scenarios involving systems governed by ordinary and partial differential equations, as well as a black-box climate prediction problem. Through these scenarios we demonstrate state of the art accuracy, robustness with respect to noisy input data, and a consistently small spread of errors over testing data sets, even for out-of-distribution prediction tasks.
Kernel Partial Correlation Coefficient --- a Measure of Conditional Dependence
Authors: Zhen Huang; Nabarun Deb; Bodhisattva Sen
Venue: JMLR 2022 (main)

Abstract: We propose and study a class of simple, nonparametric, yet interpretable measures of conditional dependence, which we call kernel partial correlation (KPC) coefficient, between two random variables $Y$ and $Z$ given a third variable $X$, all taking values in general topological spaces. The population KPC captures the strength of conditional dependence and it is 0 if and only if $Y$ is conditionally independent of $Z$ given $X$, and 1 if and only if $Y$ is a measurable function of $Z$ and $X$. We describe two consistent methods of estimating KPC. Our first method is based on the general framework of geometric graphs, including $K$-nearest neighbor graphs and minimum spanning trees. A sub-class of these estimators can be computed in near linear time and converges at a rate that adapts automatically to the intrinsic dimensionality of the underlying distributions. The second strategy involves direct estimation of conditional mean embeddings in the RKHS framework. Using these empirical measures we develop a fully model-free variable selection algorithm, and formally prove the consistency of the procedure under suitable sparsity assumptions. Extensive simulation and real-data examples illustrate the superior performance of our methods compared to existing procedures.
Smooth Robust Tensor Completion for Background/Foreground Separation with Missing Pixels: Novel Algorithm with Convergence Guarantee
Authors: Bo Shen; Weijun Xie; Zhenyu (James) Kong
Venue: JMLR 2022 (main)

Abstract: Robust PCA (RPCA) and its tensor extension, namely, Robust Tensor PCA (RTPCA), provide an effective framework for background/foreground separation by decomposing the data into low-rank and sparse components, which contain the background and the foreground (moving objects), respectively. However, in real-world applications, the presence of missing pixels is a very common and challenging issue due to errors in the acquisition process or manufacturer defects. RPCA and RTPCA are not able to recover the background and foreground simultaneously with missing pixels. This study aims to address the problem of background/foreground separation with missing pixels by combining video recovery and background/foreground separation into a single framework. To achieve this goal, a smooth robust tensor completion (SRTC) model is proposed to recover the data and decompose it into the static background and smooth foreground, respectively. An efficient algorithm based on tensor proximal alternating minimization (tenPAM) is implemented to solve the proposed model with a global convergence guarantee under very mild conditions. Extensive experiments on actual data demonstrate that the proposed method significantly outperforms the state-of-the-art approaches for background/foreground separation with missing pixels.
Learning Green's functions associated with time-dependent partial differential equations
Authors: Nicolas BoullÃ©; Seick Kim; Tianyi Shi; Alex Townsend
Venue: JMLR 2022 (main)

Abstract: Neural operators are a popular technique in scientific machine learning to learn a mathematical model of the behavior of unknown physical systems from data. Neural operators are especially useful to learn solution operators associated with partial differential equations (PDEs) from pairs of forcing functions and solutions when numerical solvers are not available or the underlying physics is poorly understood. In this work, we attempt to provide theoretical foundations to understand the amount of training data needed to learn time-dependent PDEs. Given input-output pairs from a parabolic PDE in any spatial dimension $n\geq 1$, we derive the first theoretically rigorous scheme for learning the associated solution operator, which takes the form of a convolution with a Green's function $G$. Until now, rigorously learning Green's functions associated with time-dependent PDEs has been a major challenge in the field of scientific machine learning because $G$ may not be square-integrable when $n>1$, and time-dependent PDEs have transient dynamics. By combining the hierarchical low-rank structure of $G$ together with randomized numerical linear algebra, we construct an approximant to $G$ that achieves a relative error of $\smash{\mathcal{O}(\Gamma_\epsilon^{-1/2}\epsilon)}$ in the $L^1$-norm with high probability by using at most $\smash{\mathcal{O}(\epsilon^{-\frac{n+2}{2}}\log(1/\epsilon))}$ input-output training pairs, where $\Gamma_\epsilon$ is a measure of the quality of the training dataset for learning $G$, and $\epsilon>0$ is sufficiently small.
Structural Agnostic Modeling: Adversarial Learning of Causal Graphs
Authors: Diviyan Kalainathan; Olivier Goudet; Isabelle Guyon; David Lopez-Paz; MichÃ¨le Sebag
Venue: JMLR 2022 (main)

Abstract: A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries, SAM aims to find the underlying causal structure from observational data. The approach is based on a game between different players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the generated data against the original data. A learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the  optimization of the graph structure and parameters through stochastic gradient descent. SAM is extensively experimentally validated on synthetic and real data.
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Authors: Alireza Fallah; Mert GÃ¼rbÃ¼zbalaban; Asuman Ozdaglar; Umut ÅimÅekli; Lingjiong Zhu
Venue: JMLR 2022 (main)

Abstract: We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the objective function is distributed over several computational units, lying on a fixed but arbitrary connected communication graph, subject to local communication constraints where noisy estimates of the gradients are available. We develop a framework which allows to choose the stepsize and the momentum parameters of these algorithms in a way to optimize performance by systematically trading off the bias, variance and dependence to network effects. When gradients do not contain noise, we also prove that D-ASG can achieve acceleration, in the sense that it requires $\mathcal{O}(\sqrt{\kappa} \log(1/\varepsilon))$ gradient evaluations and $\mathcal{O}(\sqrt{\kappa} \log(1/\varepsilon))$ communications to converge to the same fixed point with the non-accelerated variant where $\kappa$ is the condition number and $\varepsilon$ is the target accuracy.  For quadratic functions, we also provide finer performance bounds that are tight with respect to bias and variance terms. Finally, we study a multistage version of D-ASG with parameters carefully varied over stages to ensure exact convergence to the optimal solution. It achieves optimal and accelerated $\mathcal{O}(-k/\sqrt{\kappa})$ linear decay in the bias term as well as optimal $\mathcal{O}(\sigma^2/k)$ in the variance term. We illustrate through numerical experiments that our approach results in accelerated practical algorithms that are robust to gradient noise and that can outperform existing methods.
Behavior Priors for Efficient Reinforcement Learning
Authors: Dhruva Tirumala; Alexandre Galashov; Hyeonwoo Noh; Leonard Hasenclever; Razvan Pascanu; Jonathan Schwarz; Guillaume Desjardins; Wojciech Marian Czarnecki; Arun Ahuja; Yee Whye Teh; Nicolas Heess
Venue: JMLR 2022 (main)

Abstract: As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effective solution strategies becomes increasingly important. In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts. For example the day-to day behavior of humans comprises distinctive locomotion and manipulation patterns that recur across many different situations and goals. We discuss how such behavior patterns can be captured using probabilistic trajectory models and how these can be integrated effectively into reinforcement learning schemes, e.g. to facilitate multi-task and transfer learning. We then extend these ideas to latent variable models and consider a formulation to learn hierarchical priors that capture different aspects of the behavior in reusable modules. We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives, thereby offering an alternative perspective on existing ideas. We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains, videos of which can be found at the following url: https://sites.google.com/view/behavior-priors.
Variance Reduced EXTRA and DIGing and Their Optimal Acceleration for Strongly Convex Decentralized Optimization
Authors: Huan Li; Zhouchen Lin; Yongchun Fang
Venue: JMLR 2022 (main)

Abstract: We study stochastic decentralized optimization for the problem of training machine learning models with large-scale distributed data. We extend the widely used EXTRA and DIGing methods with variance reduction (VR), and propose two methods: VR-EXTRA and VR-DIGing. The proposed VR-EXTRA requires the time of $O((\kappa_s+n)\log\frac{1}{\epsilon})$ stochastic gradient evaluations and $O((\kappa_b+\kappa_c)\log\frac{1}{\epsilon})$ communication rounds to reach precision $\epsilon$, which are the best complexities among the non-accelerated gradient-type methods, where $\kappa_s$ and $\kappa_b$ are the stochastic condition number and batch condition number for strongly convex and smooth problems, respectively, $\kappa_c$ is the condition number of the communication network, and $n$ is the sample size on each distributed node. The proposed VR-DIGing has a little higher communication cost of $O((\kappa_b+\kappa_c^2)\log\frac{1}{\epsilon})$. Our stochastic gradient computation complexities are the same as the ones of single-machine VR methods, such as SAG, SAGA, and SVRG, and our communication complexities keep the same as those of EXTRA and DIGing, respectively. To further speed up the convergence, we also propose the accelerated VR-EXTRA and VR-DIGing with both the optimal $O((\sqrt{n\kappa_s}+n)\log\frac{1}{\epsilon})$ stochastic gradient computation complexity and $O(\sqrt{\kappa_b\kappa_c}\log\frac{1}{\epsilon})$ communication complexity. Our stochastic gradient computation complexity is also the same as the ones of single-machine accelerated VR methods, such as Katyusha, and our communication complexity keeps the same as those of accelerated full batch decentralized methods, such as MSDA. To the best of our knowledge, our accelerated methods are the first to achieve both the optimal stochastic gradient computation complexity and communication complexity in the class of gradient-type methods.
On Acceleration for Convex Composite Minimization with Noise-Corrupted Gradients and Approximate Proximal Mapping
Authors: Qiang Zhou; Sinno Jialin Pan
Venue: JMLR 2022 (main)

Abstract: The accelerated proximal methods (APM) have become one of the most important optimization tools for large-scale convex composite minimization problems, due to their wide range of applications and the optimal convergence rate in first-order algorithms. However, most existing theoretical results of APM are obtained by assuming that the gradient oracle is exact and the proximal mapping must be exactly solved, which may not hold in practice. This work presents a theoretical study of APM by allowing to use inexact gradient oracle and approximate proximal mapping. Specifically, we analyze inexact APM by improving the approximate duality gap technique (ADGT) which was originally designed for convergence analysis for first-order methods with both exact gradient oracle and proximal mapping. Our approach has several advantages: 1) we provide a unified convergence analysis that allows both inexact gradient oracle and approximate proximal mapping; 2) our proof is generic that naturally recovers the convergence rates of both accelerated and non-accelerated proximal methods, on top of which the advantages and the disadvantages of acceleration can be easily derived; 3) we derive the same convergence bound as previous methods in terms of inexact gradient oracle, but a tighter convergence bound in terms of approximate proximal mapping.