GenAI4ELab/papercli-papers huggingface dataset:
Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradients
Authors: Sachin Garg; Albert S. Berahas; MichaÅ DereziÅski
Venue: JMLR 2025 (main)

Abstract: We show that, for finite-sum minimization problems, incorporating partial second-order information of the objective function can dramatically improve the robustness to mini-batch size of variance-reduced stochastic gradient methods, making them more scalable while retaining their benefits over traditional Newton-type approaches. We demonstrate this phenomenon on a prototypical stochastic second-order algorithm, called Mini-Batch Stochastic Variance-Reduced Newton ($\texttt{Mb-SVRN}$), which combines variance-reduced gradient estimates with access to an approximate Hessian oracle. In particular, we show that when the data size $n$ is sufficiently large, i.e., $n\gg \alpha^2\kappa$, where $\kappa$ is the condition number and $\alpha$ is the Hessian approximation factor, then $\texttt{Mb-SVRN}$ achieves a fast linear convergence rate that is independent of the gradient mini-batch size $b$, as long $b$ is in the range between $1$ and $b_{\max}=O(n/(\alpha \log n))$. Only after increasing the mini-batch size past this critical point $b_{\max}$, the method begins to transition into a standard Newton-type algorithm which is much more sensitive to the Hessian approximation quality. We verify this empirically on benchmark tasks showing that, after tuning the step size, the convergence rate of $\texttt{Mb-SVRN}$ remains fast for a wide range of mini-batch sizes, and the dependence of the phase transition point $b_{\max}$ on the Hessian approximation factor $\alpha$ agrees with our theory.
FusionBench: A Unified Library and Comprehensive Benchmark for Deep Model Fusion
Authors: Anke Tang; Li Shen; Yong Luo; Enneng Yang; Han Hu; Lefei Zhang; Bo Du; Dacheng Tao
Venue: JMLR 2025 (main)

Abstract: Deep model fusion is an emerging technique that unifies the predictions or parameters of several deep neural networks into a single better-performing model in a cost-effective and data-efficient manner. Although a variety of deep model fusion techniques have been introduced, their evaluations tend to be inconsistent and often inadequate to validate their effectiveness and robustness. We present FusionBench, the first benchmark and a unified library designed specifically for deep model fusion. Our benchmark consists of multiple tasks, each with different settings of models and datasets. This variety allows us to compare fusion methods across different scenarios and model scales. Additionally, FusionBench serves as a unified library for easy implementation and testing of new fusion techniques. FusionBench is open source and actively maintained, with community contributions encouraged.
Certified Machine Unlearning Under High Dimensional Regime
Authors: Haolin Zou; Arnab Auddy; Yongchan Kwon; Kamiar Rahnama Rad; Arian Maleki
Venue: JMLR 2025 (main)

Abstract: Machine unlearning focuses on the computationally efficient removal of specific training data from trained models, ensuring that the influence of forgotten data is effectively eliminated without the need for full retraining. Despite advances in low-dimensional settings, where the number of parameters \( p \) is much smaller than the sample size \( n \), extending similar theoretical guarantees to high-dimensional regimes remains challenging.
We study an unlearning algorithm that starts from the original model parameters and performs a theory-guided sequence of Newton steps. After this update, carefully scaled isotropic Laplacian noise is added to the estimate to ensure that any (potential) residual influence of the deletion set is completely removed.
We show that when both \( n, p \to \infty \) with a fixed ratio \( n/p \), significant theoretical and computational obstacles arise due to the interplay between the complexity of the model and the finite signal-to-noise ratio. Finally, we show that, unlike in low-dimensional settings where one Newton step suffices, in high-dimensional problems at least two Newton steps are required to effectively unlearn a fixed number of data points, and even more steps are required when the deletion set scales with $n$. We provide numerical experiments to support the theoretical claims of the paper.
Guiding Safe Exploration with Weakest Preconditions
Authors: Greg Anderson; Swarat Chaudhuri; Isil Dillig
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In reinforcement learning for safety-critical settings, it is often desirable for the agent to obey safety constraints at all points in time, including during training. We present a novel neurosymbolic approach called SPICE to solve this safe exploration problem. SPICE uses an online shielding layer based on symbolic weakest preconditions to achieve a more precise safety analysis than existing tools without unduly impacting the training process. We evaluate the approach on a suite of continuous control benchmarks and show that it can achieve comparable performance to existing safe learning techniques while incurring fewer safety violations. Additionally, we present theoretical results showing that SPICE converges to the optimal safe policy under reasonable assumptions.
An Adaptive Entropy-Regularization Framework for Multi-Agent Reinforcement Learning
Authors: Woojun Kim; Youngchul Sung
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: In this paper, we propose an adaptive entropy-regularization framework (ADER) for multi-agent reinforcement learning (RL) to learn the adequate amount of exploration for each agent based on the degree of required exploration. In order to handle instability arising from updating multiple entropy temperature parameters for multiple agents, we disentangle the soft value function into two types: one for pure reward and the other for entropy. By applying multi-agent value factorization to the disentangled value function of pure reward, we obtain a relevant metric to assess the necessary degree of exploration for each agent. Based on this metric, we propose the ADER algorithm based on maximum entropy RL, which controls the necessary level of exploration across agents over time by learning the proper target entropy for each agent. Experimental results show that the proposed scheme significantly outperforms current state-of-the-art multi-agent RL algorithms.
AutoSparse: Towards Automated Sparse Training
Authors: Abhisek Kundu; Naveen Mellempudi; Dharma Teja Vooturi; Bharat Kaul; Pradeep Dubey
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Sparse training is emerging as a promising avenue for reducing the computational cost of training neural networks. Several recent studies have proposed pruning methods using learnable thresholds to efficiently explore the non-uniform distribution of sparsity inherent within the models. In this paper, we propose Gradient Annealing (GA), a gradient driven approach where gradients to pruned out weights are scaled down in a non-linear manner. GA eliminates the need for additional sparsity-inducing regularization by providing an elegant trade-off between sparsity and accuracy. We integrated GA with the latest learnable threshold based pruning methods to create an automated sparse training algorithm called AutoSparse. Our algorithm achieves state-of-the-art accuracy with 80% sparsity for ResNet50 and 75% sparsity for  MobileNetV1 on Imagenet-1K. AutoSparse also results in 7× reduction in inference FLOPS and > 2× reduction in training FLOPS for ResNet50 on ImageNet at 80% sparsity. Finally, GA generalizes well to fixed-budget (Top-K, 80%) sparse training methods, improving the accuracy of ResNet50 on Imagenet-1K, to outperform TopKAST+PP by 0.3%.
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions
Authors: Sitan Chen; Sinho Chewi; Jerry Li; Yuanzhi Li; Adil Salim; Anru Zhang
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: We provide theoretical convergence guarantees for score-based generative models (SGMs) such as denoising diffusion probabilistic models (DDPMs), which constitute the backbone of large-scale real-world generative models such as DALL$\cdot$E 2. Our main result is that, assuming accurate score estimates, such SGMs can efficiently sample from essentially any realistic data distribution. In contrast to prior works, our results (1) hold for an $L^2$-accurate score estimate (rather than $L^\infty$-accurate); (2) do not require restrictive functional inequality conditions that preclude substantial non-log-concavity; (3) scale polynomially in all relevant problem parameters; and (4) match state-of-the-art complexity guarantees for discretization of the Langevin diffusion, provided that the score error is sufficiently small. We view this as strong theoretical justification for the empirical success of SGMs. We also examine SGMs based on the critically damped Langevin diffusion (CLD). Contrary to conventional wisdom, we provide evidence that the use of the CLD does *not* reduce the complexity of SGMs.
RoCourseNet: Distributionally Robust Training of a Prediction Aware Recourse Model
Authors: Hangzhi Guo; Feiran Jia; Jinghui Chen; Anna Squicciarini; Amulya Yadav
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Counterfactual (CF) explanations for machine learning (ML) models are preferred by end-users, as they explain the predictions of ML models by providing a recourse (or contrastive) case to individuals who are adversely impacted by predicted outcomes. Existing CF explanation methods generate recourses under the assumption that the underlying target ML model remains stationary over time. However, due to commonly occurring distributional shifts in training data, ML models constantly get updated in practice, which might render previously generated recourses invalid and diminish end-users trust in our algorithmic framework. To address this problem, we propose RoCourseNet, a training framework that jointly optimizes for predictions and recourses that are robust to future data shifts. We have three main contributions: (i) We propose a novel \emph{virtual data shift (VDS)} algorithm to find worst-case shifted ML models by explicitly considering the worst-case data shift in the training dataset. (ii) We leverage adversarial training to solve a novel tri-level optimization problem inside RoCourseNet, which simultaneously generates predictions and corresponding robust recourses. (iii) Finally, we evaluate RoCourseNet's performance on three real-world datasets and show that RoCourseNet outperforms state-of-the-art baselines by $\sim$10\% in generating robust CF explanations.
Irregularity Reflection Neural Network for Time Series Forecasting
Authors: YoungJun Choo; Adrian Matias Chung Baek; Namhun Kim; Han-Gyun Woo
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Time series forecasting is a long-standing challenge in a variety of industries, and deep learning stands as the mainstream paradigm for handling this forecasting problem. With recent success, representations of time series components (e.g., trend and seasonality) are also considered in the learning process of the models. However, the residual remains under explored due to difficulty in formulating its inherent complexity. In this study, we propose a novel Irregularity Reflection Neural Network (IRN) that reflect the residual for the time series forecasting. First, we redefine the residual as the irregularity and express it as a sum of individual, short regular waves considering the Fourier series in a micro perspective. Second, we design a module, based on the convolutional architectures to mimic the variables of the derived irregularity representation, named Irregularity Representation Block (IRB). IRN comprises IRB on top of a forecasting model to learn the irregularity representation of time series. Extensive experiments on multiple real-world datasets demonstrate that IRN outperforms the state-of-the-art benchmarks in time series forecasting tasks.
Sample-efficient multi-objective molecular optimization with GFlowNets
Authors: Yiheng Zhu; Jialu Wu; Chaowen Hu; Jiahuan Yan; Chang-Yu Hsieh; Tingjun Hou; Jian Wu
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as an expensive black-box optimization problem over the discrete chemical space. Computational methods have achieved initial success but still struggle with simultaneously optimizing multiple competing properties in a sample-efficient manner. In this work, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. Inspired by reinforcement learning, we further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. Through synthetic experiments, we illustrate that HN-GFN has adequate capacity to generalize over preferences. Extensive experiments show that our framework outperforms the best baselines by a large margin in terms of hypervolume in various real-world MOBO settings.
MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting
Authors: Huiqiang Wang; Jian Peng; Feihu Huang; Jince Wang; Junhui Chen; Yifei Xiao
Venue: ICLR 2023 (ICLR 2023 notable top 5%)

Abstract: Recently, Transformer-based methods have achieved surprising performance in the field of long-term series forecasting, but the attention mechanism for computing global correlations entails high complexity. And they do not allow for targeted modeling of local features as CNN structures do. To solve the above problems, we propose to combine local features and global correlations to capture the overall view of time series (e.g., fluctuations, trends). To fully exploit the underlying information in the time series, a multi-scale branch structure is adopted to model different potential patterns separately. Each pattern is extracted with down-sampled convolution and isometric convolution for local features and global correlations, respectively. In addition to being more effective, our proposed method, termed as Multi-scale Isometric Convolution Network (MICN), is more efficient with linear complexity about the sequence length with suitable convolution kernels. Our experiments on six benchmark datasets show that compared with state-of-the-art methods, MICN yields 17.2% and 21.6% relative improvements for multivariate and univariate time series, respectively.
PLOT: Prompt Learning with Optimal Transport for Vision-Language Models
Authors: Guangyi Chen; Weiran Yao; Xiangchen Song; Xinyue Li; Yongming Rao; Kun Zhang
Venue: ICLR 2023 (ICLR 2023 notable top 25%)

Abstract: With the increasing attention to large vision-language models such as CLIP, there has been a significant amount of effort dedicated to building efficient prompts. Unlike conventional methods of only learning one single prompt, we propose to learn multiple comprehensive prompts to describe diverse characteristics of categories such as intrinsic attributes or extrinsic contexts. However, directly matching each prompt to the same visual feature is problematic, as it pushes the prompts to converge to one point. To solve this problem, we propose to apply optimal transport to match the vision and text modalities. Specifically, we first model images and the categories with visual and textual feature sets. Then, we apply a two-stage optimization strategy to learn the prompts. In the inner loop, we optimize the optimal transport distance to align visual features and prompts by the Sinkhorn algorithm, while in the outer loop, we learn the prompts by this distance from the supervised data. Extensive experiments are conducted on the few-shot recognition task and the improvement demonstrates the superiority of our method. The code is available at https://github.com/CHENGY12/PLOT.
Improved Training of Physics-Informed Neural Networks Using Energy-Based Priors: a Study on Electrical Impedance Tomography
Authors: Akarsh Pokkunuru; Pedram Rooshenas; Thilo Strauss; Anuj Abhishek; Taufiquar Khan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Physics-informed neural networks (PINNs) are attracting significant attention for solving partial differential equation (PDE) based inverse problems, including electrical impedance tomography (EIT). EIT is non-linear and especially its inverse problem is highly ill-posed. Therefore, successful training of PINN is extremely sensitive to interplay between different loss terms and hyper-parameters, including the learning rate. In this work, we propose a Bayesian approach through data-driven energy-based model (EBM) as a prior, to improve the overall accuracy and quality of tomographic reconstruction. In particular, the EBM is trained over the possible solutions of the PDEs with different boundary conditions. By imparting such prior onto physics-based training, PINN convergence is expedited by more than ten times faster to the PDE’s solution. Evaluation outcome shows that our proposed method is more robust for solving the EIT problem. Our code is available at: https://rooshenasgroup.github.io/eit_ebprior.
Energy-based Out-of-Distribution Detection for Graph Neural Networks
Authors: Qitian Wu; Yiting Chen; Chenxiao Yang; Junchi Yan
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Representation learning on semi-structured data, e.g., graphs, has become a central problem in deep learning community as relational structures are pervasive in real situations and induce data inter-dependence that hinders trivial adaptation of existing approaches in other domains where the inputs are assumed to be i.i.d. sampled. However, current models in this regime mostly focus on improving testing performance of in-distribution data and largely ignores the potential risk w.r.t. out-of-distribution (OOD) testing samples that may cause negative outcome if the model is overconfident in prediction on them. In this paper, we identify a provably effective OOD discriminator based on an energy function directly extracted from a graph neural network trained with standard supervised classification loss. This paves a way for a simple and efficient OOD detection model for GNN-based semi-supervised learning on graphs, which we call GNN-Safe. It also has nice theoretical properties that guarantee an overall distinguishable margin between the detection scores for in-distribution and OOD samples, which, more critically, can be further strengthened by a non-learning-based structured propagation scheme. Extensive experiments over five real-world datasets validate the practical efficacy of the proposed model for detecting various OOD instances that are inter-connected in a graph with up to 17.0% improvement on average AUROC over competitive peer models and without sacrificing in-distribution testing accuracy.
Differentially private Bias-Term Only Fine-tuning of Foundation Models
Authors: Zhiqi Bu; Yu-Xiang Wang; Sheng Zha; George Karypis
Venue: ICLR 2023 (Submitted to ICLR 2023)

Abstract: We study the problem of differentially private (DP) fine-tuning of large pre-trained models — a recent privacy-preserving approach suitable for solving downstream tasks with sensitive data. Existing work has demonstrated that high accuracy is possible under strong privacy constraint, yet requires significant computational overhead or modifications to the network architecture.

We propose differentially private bias-term fine-tuning (DP-BiTFiT), which matches the state-of-the-art accuracy for DP algorithms and the efficiency of the standard BiTFiT. DP-BiTFiT is model agnostic (not modifying the network architecture), parameter efficient (only training about $0.1\%$ of the parameters), and computation efficient (almost removing the overhead caused by DP, in both the time and space complexity). On a wide range of tasks, DP-BiTFiT is $2\sim 30\times$ faster and uses $2\sim 8\times$ less memory than DP full fine-tuning, even faster than the standard full fine-tuning. This amazing efficiency enables us to conduct DP fine-tuning on language and vision tasks with long-sequence texts and high-resolution images, which were computationally difficult using existing methods.
More Centralized Training, Still Decentralized Execution: Multi-Agent Conditional Policy Factorization
Authors: Jiangxing Wang; Deheng Ye; Zongqing Lu
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: In cooperative multi-agent reinforcement learning (MARL), combining value decomposition with actor-critic enables agents to learn stochastic policies, which are more suitable for the partially observable environment. Given the goal of learning local policies that enable decentralized execution, agents are commonly assumed to be independent of each other, even in centralized training. However, such an assumption may prohibit agents from learning the optimal joint policy. To address this problem, we explicitly take the dependency among agents into centralized training. Although this leads to the optimal joint policy, it may not be factorized for decentralized execution. Nevertheless, we theoretically show that from such a joint policy, we can always derive another joint policy that achieves the same optimality but can be factorized for decentralized execution. To this end, we propose multi-agent conditional policy factorization (MACPF), which takes more centralized training but still enables decentralized execution. We empirically verify MACPF in various cooperative MARL tasks and demonstrate that MACPF achieves better performance or faster convergence than baselines. Our code is available at https://github.com/PKU-RL/FOP-DMAC-MACPF.
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion
Authors: Han Wu; Jie Yin; Bala Rajaratnam; Jianyuan Guo
Venue: ICLR 2023 (ICLR 2023 poster)

Abstract: Knowledge graphs (KGs) are powerful in terms of their inference abilities, but are also notorious for their incompleteness and long-tail distribution of relations. To address these challenges and expand the coverage of KGs, few-shot KG completion aims to make predictions for triplets involving novel relations when only a few training triplets are provided as reference. Previous methods have focused on designing local neighbor aggregators to learn entity-level information and/or imposing sequential dependency assumption at the triplet level to learn meta relation information. However, pairwise triplet-level interactions and context-level relational information have been largely overlooked for learning meta representations of few-shot relations. In this paper, we propose a hierarchical relational learning method (HiRe) for few-shot KG completion. By jointly capturing three levels of relational information (entity-level, triplet-level and context-level), HiRe can effectively learn and refine the meta representation of few-shot relations, and consequently generalize well to new unseen relations. Extensive experiments on two benchmark datasets validate the superiority of HiRe over state-of-the-art methods. The code of HiRe can be found in supplementary material and will be released after acceptance.