On E-commerce platforms, new products often suffer from the cold-start problem: limited interaction data reduces their search visibility and hurts relevance ranking. To address this, we propose a simple yet effective behavior feature boosting method that leverages substitute relationships among products (BFS). BFS identifies substitutes—products that satisfy similar user needs—and aggregates their behavioral signals (e.g., clicks, add-to-carts, purchases, and ratings) to provide a warm start for new items. Incorporating these enriched signals into ranking models mitigates cold-start effects and improves relevance and competitiveness. Experiments on a large E-commerce platform, both offline and online, show that BFS significantly improves search relevance and product discovery for cold-start products. BFS is scalable and practical, improving user experience while increasing exposure for newly launched items in E-commerce search. The BFS-enhanced ranking model has been launched in production and has served customers since 2025.

Federated Learning (FL) offers a promising framework for collaborative and privacy-preserving machine learning across distributed data sources. However, the substantial communication costs associated with FL significantly challenge its efficiency. Specifically, in each communication round, the communication costs scale linearly with the model's dimension, which presents a formidable obstacle, especially in large model scenarios. Despite various communication-efficient strategies, the intrinsic dimension-dependent communication cost remains a major bottleneck for current FL implementations. This paper proposes a novel dimension-free communication algorithm - DeComFL, which leverages the zeroth-order optimization techniques and reduces the communication cost from $\mathcal{O}(d)$ to $\mathcal{O}(1)$ by transmitting only a constant number of scalar values between clients and the server in each round, regardless of the dimension $d$ of the model parameters. Theoretically, in non-convex functions, we prove that our algorithm achieves state-of-the-art rates, which show a linear speedup of the number of clients and local steps under standard assumptions. With additional low effective rank assumption, we can further show that the convergence rate is independent of the model dimension $d$ as well. Empirical evaluations, encompassing both classic deep learning training and large language model fine-tuning, demonstrate significant reductions in communication overhead. Notably, DeComFL achieves this by transmitting only around 1MB of data in total between the server and a client to fine-tune a model with billions of parameters. The code is available at https://github.com/ZidongLiu/DeComFL.

Arihant Jain, Purav Aggarwal, Rishav Sahay, Chaosheng Dong, Anoop Saladi. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

This paper introduces MO-LightGBM, an open-source library built upon LightGBM, specifically designed to offer an integrated, versatile, and easily adaptable framework for Multi-objective Learning to Rank (MOLTR). MO-LightGBM supports diverse Multi-objective optimization (MOO) settings and incorporates 12 state-of-the-art optimization strategies. Its modular architecture enhances usability and flexibility, allowing researchers and practitioners to easily develop new MOO methodologies, perform rigorous comparisons with existing techniques, and effectively deploy MOO algorithms in practical ranking applications. We illustrate the utility of MO-LightGBM through a Bi-objective Learning to Rank example and present visualizations of the results. MO-LightGBM is available at https://github.com/amazon-science/MO-LightGBM.

Recently, multi-objective optimization (MOO) has gained attention for its broad applications in ML, operations research, and engineering. However, MOO algorithm design remains in its infancy and many existing MOO methods suffer from unsatisfactory convergence rate and sample complexity performance. To address this challenge, in this paper, we propose an algorithm called STIMULUS (**st**ochastic path-**i**ntegrated **mul**ti-gradient rec**u**rsive e**s**timator), a new and robust approach for solving MOO problems. Different from the traditional methods, STIMULUS introduces a simple yet powerful recursive framework for updating stochastic gradient estimates to improve convergence performance with low sample complexity. In addition, we introduce an enhanced version of \algns, termed \algmns, which incorporates a momentum term to further expedite convergence. We establish $\mathcal{O}(1/T)$ convergence rates of the proposed methods for non-convex settings and $\mathcal{O}(\exp{-\mu T})$ for strongly convex settings, where $T$ is the total number of iteration rounds. Additionally, we achieve the state-of-the-art $O\left(n+\sqrt{n}\epsilon^{-1}\right)$ sample complexities for non-convex settings and $\mathcal{O}\left(n+ \sqrt{n} \ln ({\mu/\epsilon})\right)$ for strongly convex settings, where $\epsilon>0$ is a desired stationarity error. Moreover, to alleviate the periodic full gradient evaluation requirement in STIMULUS and STIMULUS-M, we further propose enhanced versions with adaptive batching called STIMULUS$^+$/ STIMULUS-M$^+$ and provide their theoretical analysis.

Graph Neural Networks (GNNs) have become the de facto standard for modeling graph-structured data in various applications. Among them, implicit GNNs have shown a superior ability to effectively capture long-range dependencies in underlying graphs. However, implicit GNNs tend to be computationally expensive and have high memory usage, due to 1) their use of full-batch training; and 2) they require a large number of iterations to solve a fixed-point equation. These compromise the scalability and efficiency of implicit GNNs especially on large graphs. In this paper, we aim to answer the question: how can we efficiently train implicit GNNs to provide effective predictions on large graphs? We propose a new scalable and effective implicit GNN (SEIGNN) with a mini-batch training method and a stochastic solver, which can be trained efficiently on large graphs. Specifically, SEIGNN can more effectively incorporate global and long-range information by introducing coarse-level nodes in the mini-batch training method. It also achieves reduced training time by obtaining unbiased approximate solutions with fewer iterations in the proposed solver. Comprehensive experiments on various large graphs demonstrate that SEIGNN outperforms baselines and achieves higher accuracy with less training time compared with existing implicit GNNs.

Sequential recommendation requires understanding the dynamic patterns of users' behaviors, contexts, and preferences from their historical interactions. While most research emphasizes item-level user-item interactions, they often overlook underlying shopping intentions, such as preferences for ballpoint pens or miniatures. Identifying these latent intentions is vital for enhancing shopping experiences on platforms like Amazon. Despite its significance, the area of main shopping intention detection remains under-investigated in the academic literature. To fill this gap, we introduce a graph-regularized stochastic Transformer approach, G-STO. It considers intentions as product sets and user preferences as intention composites, both modeled as stochastic Gaussian embeddings in latent space. We also employ a global intention relational graph as prior knowledge for regularization, ensuring related intentions are distributionally close. These regularized embeddings are then input into Transformer-based models to capture sequential intention transitions. On testing our model with three real-world datasets, it outperformed the baselines by 18.08% in Hit@1, 7.01% in Hit@10, and 6.11% in NDCG@10.

Learning to Rank (LTR) technique is ubiquitous in Information Retrieval systems, especially in search ranking applications. The relevance labels used to train ranking models are often noisy measurements of human behavior, such as product ratings in product searches. This results in non-unique ground truth rankings and ambiguity. To address this, Multi-Label LTR (MLLTR) is used to train models using multiple relevance criteria, capturing conflicting but important goals, such as product quality and purchase likelihood for improved revenue in product searches. This research leverages Multi-Objective Optimization (MOO) in MLLTR and employs modern MOO algorithms to solve the problem. A general framework is proposed to combine label information to characterize trade-offs among goals, and allows for the use of gradient-based MOO algorithms. We test the proposed framework on four publicly available LTR datasets and one E-commerce dataset to show its efficacy.

In Learning-to-Rank (LTR) problems, the task of delivering relevant search results and allocating fair exposure to items of a protected group can conflict. Previous works in Fair LTR have attempted to resolve this by combining the objectives of relevant ranking and fair ranking into a single linear combination, but this approach is limited by the nonconvexity of the objective functions and can result in suboptimal relevance in ranking outputs. To address this, we propose a solution using Multi-Objective Optimization (MOO) algorithms. We extend these algorithms to querywise MOO to reduce the exposure disparity, not only on average but also at the query level. Interestingly, for moderate fairness requirements, it improves the relevance of ranking instead of deteriorating. We attribute this improvement to the benefits of multi-task learning and study the effect of fair ranking on the relevant ranking task. Moreover, we significantly improve the computational efficiency compared to previous methods by using the Gumbel max trick to sample the Plackett-Luce distribution. We evaluate our proposed methods on three real-world datasets and show their improvement in relevance ranking over state-of-the-art solutions.

In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms in MOO literature remain limited to centralized learning settings, which do not satisfy the distributed nature and data privacy needs of such multi-agent multi-task learning applications. This motivates us to propose a new federated multi-objective learning (FMOL) framework with multiple clients distributively and collaboratively solving an MOO problem while keeping their training data private. Notably, our FMOL framework allows a different set of objective functions across different clients to support a wide range of applications, which advances and generalizes the MOO formulation to the federated learning paradigm for the first time. For this FMOL framework, we propose two new federated multi-objective optimization (FMOO) algorithms called federated multi-gradient descent averaging (FMGDA) and federated stochastic multi-gradient descent averaging (FSMGDA). Both algorithms allow local updates to significantly reduce communication costs, while achieving the {\em same} convergence rates as those of their algorithmic counterparts in the single-objective federated learning. Our extensive experiments also corroborate the efficacy of our proposed FMOO algorithms.

Multi-objective optimization (MOO) and multi-task learning (MTL) have gained much popularity with prevalent use cases such as production model development of regression / classification / ranking models with MOO, and training deep learning models with MTL. Despite the long history of research in MOO, its application to machine learning requires development of solution strategy, and algorithms have recently been developed to solve specific problems such as discovery of any Pareto optimal (PO) solution, and that with a particular form of preference. In this paper, we develop a novel and generic framework to discover a PO solution with multiple forms of preferences. It allows us to formulate a generic MOO / MTL problem to express a preference, which is solved to achieve both alignment with the preference and PO, at the same time. Specifically, we apply the framework to solve the weighted Chebyshev problem and an extension of that. The former is known as a method to discover the Pareto front, the latter helps to find a model that outperforms an existing model with only one run. Experimental results demonstrate not only the method achieves competitive performance with existing methods, but also it allows us to achieve the performance from different forms of preferences.

Inverse multiobjective optimization provides a general framework for the unsupervised learning task of inferring parameters of a multiobjective decision making problem (DMP), based on a set of observed decisions from the human expert. However, the performance of this framework relies critically on the availability of an accurate DMP, sufficient decisions of high quality, and a parameter space that contains enough information about the DMP. To hedge against the uncertainties in the hypothetical DMP, the data, and the parameter space, we investigate in this paper the distributionally robust approach for inverse multiobjective optimization. Specifically, we leverage the Wasserstein metric to construct a ball centered at the empirical distribution of these decisions. We then formulate a Wasserstein distributionally robust inverse multiobjective optimization problem (WRO-IMOP) that minimizes a worst-case expected loss function, where the worst case is taken over all distributions in the Wasserstein ball. We show that the excess risk of the WRO-IMOP estimator has a sub-linear convergence rate. Furthermore, we propose the semi-infinite reformulations of the WRO-IMOP and develop a cutting-plane algorithm that converges to an approximate solution in finite iterations. Finally, we demonstrate the effectiveness of our method on both a synthetic multiobjective quadratic program and a real world portfolio optimization problem.

In this paper, we investigate a new multi-armed bandit (MAB) online learning model that considers real-world phenomena in many recommender systems: (i) the learning agent cannot pull the arms by itself and thus has to offer rewards to users to incentivize arm-pulling indirectly; and (ii) if users with specific arm preferences are well rewarded, they induce a "self-reinforcing" effect in the sense that they will attract more users of similar arm preferences. Besides addressing the tradeoff of exploration and exploitation, another key feature of this new MAB model is to balance reward and incentivizing payment. The goal of the agent is to maximize the total reward over a fixed time horizon $T$ with a low total payment. Our contributions in this paper are two-fold: (i) We propose a new MAB model with random arm selection that considers the relationship of users’ self-reinforcing preferences and incentives; and (ii) We leverage the properties of a multi-color Polya urn with nonlinear feedback model to propose two MAB policies termed "At-Least-$n$ Explore-Then-Commit" and "UCB-List". We prove that both policies achieve $O(log T)$ expected regret with $O(log T)$ expected payment over a time horizon $T$. We conduct numerical simulations to demonstrate and verify the performances of these two policies and study their robustness under various settings.

We consider a new unsupervised learning task of inferring parameters of a multiobjective decision making model, based on a set of observed decisions from the human expert. This setting is important in applications (such as the task of portfolio management) where it may be difficult to obtain the human expert’s intrinsic decision making model. We formulate such a learning problem as an inverse multiobjective optimization problem (IMOP) and propose its first sophisticated model with statistical guarantees. Then, we reveal several fundamental connections between IMOP, K-means clustering, and manifold learning. Leveraging these critical insights and connections, we propose two algorithms to solve IMOP through manifold learning and clustering. Numerical results confirm the effectiveness of our model and the computational efficacy of algorithms.

Inverse optimization is a powerful paradigm for learning preferences and restrictions that explain the behavior of a decision maker, based on a set of external signal and the corresponding decision pairs. However, most inverse optimization algorithms are designed specifically in batch setting, where all the data is available in advance. As a consequence, there has been rare use of these methods in an online setting suitable for real-time applications. In this paper, we propose a general framework for inverse optimization through online learning. Specifically, we develop an online learning algorithm that uses an implicit update rule which can handle noisy data. Moreover, under additional regularity assumptions in terms of the data and the model, we prove that our algorithm converges at a rate of $\mathcal{O}(1/\sqrt{T})$ and is statistically consistent. In our experiments, we show the online learning approach can learn the parameters with great accuracy and is very robust to noises, and achieves a dramatic improvement in computational efficacy over the batch learning approach.