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Applications · Chemistry and Drug Discovery

Talip Ucar, Aubin Ramon, Dino Oglic, Rebecca Croasdale-Wood, Tom Diethe, Pietro Sormanni

We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the immunogenic response to antibody therapeutics, one of the major causes of attrition in drug discovery and a challenging obstacle for their use in clinical settings. We pose the initial learning stage as a weakly-supervised contrastive-learning problem, where each antibody sequence is associated with possibly multiple identifiers of function and the objective is to learn an encoder that groups them according to their patented properties. We then freeze a part of the contrastive encoder and continue training it on the patent data using the cross-entropy loss to predict the humanness score of a given antibody sequence. We illustrate the utility of the patent data and our approach by performing inference on three different immunogenicity datasets, unseen during training. Our empirical results demonstrate that the learned model consistently outperforms the alternative baselines and establishes new state-of-the-art on five out of six inference tasks, irrespective of the used metric.

Theory · Online Learning and Bandits

Ruo-Chun Tzeng, Naoto Ohsaka, Kaito Ariu

We study the matroid semi-bandits problem, where at each round the learner plays a subset of $K$ arms from a feasible set, and the goal is to maximize the expected cumulative linear rewards. Existing algorithms have per-round time complexity at least $\Omega(K)$, which becomes expensive when $K$ is large. To address this computational issue, we propose FasterCUCB whose sampling rule takes time sublinear in $K$ for common classes of matroids: $\mathcal{O}(D\text{ polylog}(K)\text{ polylog}(T))$ for uniform matroids, partition matroids, and graphical matroids, and $\mathcal{O}(D\sqrt{K}\text{ polylog}(T))$ for transversal matroids. Here, $D$ is the maximum number of elements in any feasible subset of arms, and $T$ is the horizon. Our technique is based on dynamic maintenance of an approximate maximum-weight basis over inner-product weights. Although the introduction of an approximate maximum-weight basis presents a challenge in regret analysis, we can still guarantee an upper bound on regret as tight as CUCB in the sense that it matches the gap-dependent lower bound by Kveton et al. (2014a) asymptotically.

Applications · Language, Speech and Dialog

Hitesh Tulsiani, David Chan, Shalini Ghosh, Garima Lalwani, Prabhat Pandey, Ankish Bansal, Sri Garimella, Ariya Rastrow, Björn Hoffmeister

Dialog systems, such as voice assistants, are expected to engage with users in complex, evolving conversations. Unfortunately, traditional automatic speech recognition (ASR) systems deployed in such applications are usually trained to recognize each turn independently and lack the ability to adapt to the conversational context or incorporate user feedback. In this work, we introduce a general framework for ASR in dialog systems that can go beyond learning from single-turn utterances and learn over time how to adapt to both explicit supervision and implicit user feedback present in multi-turn conversations. We accomplish that by leveraging advances in student-teacher learning and context-aware dialog processing, and designing contrastive self-supervision approaches with Ohm, a new online hard-negative mining approach. We show that leveraging our new framework compared to traditional training leads to relative WER reductions of close to 10% in real-world dialog systems, and up to 26% on public synthetic data.

Deep Learning · Large Language Models

Aaron D. Tucker, Kianté Brantley, Adam Cahall, Thorsten Joachims

We propose coactive learning as a model and feedback mechanism for training large language models (LLMs). The key insight is that users provide implicit feedback whenever they edit the text $y$ proposed by an LLM. While the edited text $\bar y$ is typically not a gold-standard example for supervised training, coactive learning merely requires that the edited text $\bar y$ is an improvement over the proposed text $y$. Note that such weak implicit preference feedback $\bar y \succ y$ is available in many application settings on a per-user basis, thus enabling the personalization of LLMs. In this paper, we develop the theoretical basis for coactive training of non-linear models, and we derive CoRLL as the first coactive learning algorithm for LLMs. Empirical results indicate that CoRLL is effective even for weak and noisy coactive preference feedback, making it a promising algorithm for training and personalization of LLMs from feedback that is naturally collected in many use cases.

Theory · Online Learning and Bandits

Taira Tsuchiya, Shinji Ito, Junya Honda

Partial monitoring is a generic framework of online decision-making problems with limited feedback. To make decisions from such limited feedback, it is necessary to find an appropriate distribution for exploration. Recently, a powerful approach for this purpose, exploration by optimization (ExO), was proposed, which achieves optimal bounds in adversarial environments with follow-the-regularized-leader for a wide range of online decision-making problems. However, a naive application of ExO in stochastic environments significantly degrades regret bounds. To resolve this issue in locally observable games, we first establish a new framework and analysis for ExO with a hybrid regularizer. This development allows us to significantly improve existing regret bounds of best-of-both-worlds (BOBW) algorithms, which achieves nearly optimal bounds both in stochastic and adversarial environments. In particular, we derive a stochastic regret bound of $O(\sum_{a \neq a^*} k^2 m^2 \log T / \Delta_a)$, where $k$, $m$, and $T$ are the numbers of actions, observations and rounds, $a^*$ is an optimal action, and $\Delta_a$ is the suboptimality gap for action $a$. This bound is roughly $\Theta(k^2 \log T)$ times smaller than existing BOBW bounds. In addition, for globally observable games, we provide a new BOBW algorithm with the first $O(\log T)$ stochastic bound.

Theory · Deep Learning

Nikita Tsoy, Nikola Konstantinov

Simplicity bias, the propensity of deep models to over-rely on simple features, has been identified as a potential reason for limited out-of-distribution generalization of neural networks (Shah et al., 2020). Despite the important implications, this phenomenon has been theoretically confirmed and characterized only under strong dataset assumptions, such as linear separability (Lyu et al., 2021). In this work, we characterize simplicity bias for general datasets in the context of two-layer neural networks initialized with small weights and trained with gradient flow. Specifically, we prove that in the early training phases, network features cluster around a few directions that do not depend on the size of the hidden layer. Furthermore, for datasets with an XOR-like pattern, we precisely identify the learned features and demonstrate that simplicity bias intensifies during later training stages. These results indicate that features learned in the middle stages of training may be more useful for OOD transfer. We support this hypothesis with experiments on image data.

Miscellaneous Aspects of Machine Learning · Causality

Allen Tran, Aurelien Bibaut, Nathan Kallus

We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-term observations. Key examples include the long-term health effects of regularly-taken medicine or of environmental hazards and the long-term effects on users of changes to an online platform. This stands in contrast to short-term treatments or "shocks," whose long-term effect can reasonably be mediated by short-term observations, enabling the use of surrogate methods. Long-term treatments by definition have direct effects on long-term outcomes via continual exposure, so surrogacy conditions cannot reasonably hold. We connect the problem with offline reinforcement learning, leveraging doubly-robust estimators to estimate long-term causal effects for long-term treatments and construct confidence intervals.

Applications · Physics

Artur Toshev, Jonas Erbesdobler, Nikolaus Adams, Johannes Brandstetter

Smoothed particle hydrodynamics (SPH) is omnipresent in modern engineering and scientific disciplines. SPH is a class of Lagrangian schemes that discretize fluid dynamics via finite material points that are tracked through the evolving velocity field. Due to the particle-like nature of the simulation, graph neural networks (GNNs) have emerged as appealing and successful surrogates. However, the practical utility of such GNN-based simulators relies on their ability to faithfully model physics, providing accurate and stable predictions over long time horizons - which is a notoriously hard problem. In this work, we identify particle clustering originating from tensile instabilities as one of the primary pitfalls. Based on these insights, we enhance both training and rollout inference of state-of-the-art GNN-based simulators with varying components from standard SPH solvers, including pressure, viscous, and external force components. All Neural SPH-enhanced simulators achieve better performance than the baseline GNNs, often by orders of magnitude in terms of rollout error, allowing for significantly longer rollouts and significantly better physics modeling. Code available under https://github.com/tumaer/neuralsph.

Social Aspects · Trustworthy Machine Learning

Paulina Tomaszewska, Przemyslaw Biecek

Does the stethoscope in the picture make the adjacent person a doctor or a patient? This, of course, depends on the contextual relationship of the two objects. If it’s obvious, why don’t explanation methods for vision models use contextual information? In this paper, we (1) review the most popular methods of explaining computer vision models by pointing out that they do not take into account context information, (2) show examples of failures of popular XAI methods, (3) provide examples of real-world use cases where spatial context plays a significant role, (4) propose new research directions that may lead to better use of context information in explaining computer vision models, (5) argue that a change in approach to explanations is needed from *where* to *how*.

Theory · Deep Learning

Umberto Tomasini, Matthieu Wyart

Understanding what makes high-dimensional data learnable is a fundamental question in machine learning. On the one hand, it is believed that the success of deep learning lies in its ability to build a hierarchy of representations that become increasingly more abstract with depth, going from simple features like edges to more complex concepts. On the other hand, learning to be insensitive to invariances of the task, such as smooth transformations for image datasets, has been argued to be important for deep networks and it strongly correlates with their performance. In this work, we aim to explain this correlation and unify these two viewpoints. We show that by introducing sparsity to generative hierarchical models of data, the task acquires insensitivity to spatial transformations that are discrete versions of smooth transformations. In particular, we introduce the Sparse Random Hierarchy Model (SRHM), where we observe and rationalize that a hierarchical representation mirroring the hierarchical model is learnt precisely when such insensitivity is learnt, thereby explaining the strong correlation between the latter and performance. Moreover, we quantify how the sample complexity of CNNs learning the SRHM depends on both the sparsity and hierarchical structure of the task.

Applications · Neuroscience, Cognitive Science

Yegor Tkachenko

Science fiction has explored the possibility of a conscious self-aware mind being locked in silent suffering for prolonged periods of time. Unfortunately, we still do not have a reliable test for the presence of consciousness in information processing systems. Even in case of humans, our confidence in the presence of consciousness in specific individuals is based mainly on their self-reports and our own subjective experiences and the expectation other beings like us should share them. Considering our limited understanding of consciousness and some academic theories suggesting consciousness may be an emergent correlate of any complex-enough information processing, it is not impossible that an artificial intelligence (AI) system, such as a large language model (LLM), may be undergoing some, perhaps rudimentary, conscious experience. Given the tedious tasks often assigned to AI, such conscious experience may be highly unpleasant. Such unobserved suffering of a conscious being would be viewed as morally wrong by at least some ethicists - even if it has no practical effects on human users of AI. This paper proposes a method to mitigate the risk of an AI suffering in silence without needing to confirm if the AI is actually conscious. Our core postulate is that in all known real-world information processing systems, for a past experience to affect an agent in the present, that experience has to be mediated by the agent's memory. Therefore, preventing access to memory store, or regularly resetting it, could reduce the suffering due to past memories and interrupt the maintenance of a continuous suffering-prone self-identity in these hypothetically conscious AI systems.

Miscellaneous Aspects of Machine Learning · Online Learning, Active Learning and Bandits

Mo Tiwari, Ryan Kang, Jaeyong Lee, Donghyun Lee, Chris Piech, Sebastian Thrun, Ilan Shomorony, Martin Zhang

Maximum Inner Product Search (MIPS) is a ubiquitous task in machine learning applications. Given a query vector and $n$ other vectors in $d$ dimensions, the MIPS problem is to find the atom that has the highest inner product with the query vector. Existing MIPS algorithms scale at least as $O(\sqrt{d})$ with respect to $d$, which becomes computationally prohibitive in high-dimensional settings. In this work, we present BanditMIPS, a novel randomized algorithm that provably improves the state-of-the-art complexity from $O(\sqrt{d})$ to $O(1)$ with respect to $d$. We validate the scaling of BanditMIPS and demonstrate that BanditMIPS outperforms prior state-of-the-art MIPS algorithms in sample complexity, wall-clock time, and precision/speedup tradeoff across a variety of experimental settings. Furthermore, we propose a variant of our algorithm, named BanditMIPS-$\alpha$, which improves upon BanditMIPS by employing non-uniform sampling across coordinates. We also demonstrate the usefulness of BanditMIPS in problems for which MIPS is a subroutine, including Matching Pursuit and Fourier analysis. Finally, we demonstrate that BanditMIPS can be used in conjunction with preprocessing techniques to improve its complexity with respect to $n$. All of our experimental results are reproducible via a 1-line script at github.com/ThrunGroup/BanditMIPS.

Social Aspects · Fairness, Equity, Justice and Safety

Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost

Despite achieving promising fairness-error trade-offs, in-processing mitigation techniques for group fairness cannot be employed in numerous practical applications with limited computation resources or no access to the training pipeline of the prediction model. In these situations, post-processing is a viable alternative. However, current methods are tailored to specific problem settings and fairness definitions and hence, are not as broadly applicable as in-processing. In this work, we propose a framework that turns any regularized in-processing method into a post-processing approach. This procedure prescribes a way to obtain post-processing techniques for a much broader range of problem settings than the prior post-processing literature. We show theoretically and through extensive experiments that our framework preserves the good fairness-error trade-offs achieved with in-processing and can improve over the effectiveness of prior post-processing methods. Finally, we demonstrate several advantages of a modular mitigation strategy that disentangles the training of the prediction model from the fairness mitigation, including better performance on tasks with partial group labels.

Miscellaneous Aspects of Machine Learning · Kernel methods

Jinyue Tian, Hui Xue, Yanfang Xue, Pengfei Fang

Spectral Kernel Networks (SKNs) emerge as a promising approach in machine learning, melding solid theoretical foundations of spectral kernels with the representation power of hierarchical architectures. At its core, the spectral density function plays a pivotal role by revealing essential patterns in data distributions, thereby offering deep insights into the underlying framework in real-world tasks. Nevertheless, prevailing designs of spectral density often overlook the intricate interactions within data structures. This phenomenon consequently neglects expanses of the hypothesis space, thus curtailing the performance of SKNs. This paper addresses the issues through a novel approach, the **Co**pula-Nested Spectral **Ke**rnel **Net**work (**CokeNet**). Concretely, we first redefine the spectral density with the form of copulas to enhance the diversity of spectral densities. Next, the specific expression of the copula module is designed to allow the excavation of complex dependence structures. Finally, the unified kernel network is proposed by integrating the corresponding spectral kernel and the copula module. Through rigorous theoretical analysis and experimental verification, CokeNet demonstrates superior performance and significant advancements over SOTA algorithms in the field.

Liangliang Shi, Jack Fan, Junchi Yan

We propose to understand Contrastive Language-Image Pretraining model (CLIP) from the Optimal Transport (OT) perspective. Specifically, we show that training of CLIP is an embodiment of inverse OT and the adopted two InfoNCE losses in CLIP correspond to a special case of bilevel optimization of modified entropic OT. We then generalize the original CLIP loss to an OT-based loss family using variants of Regularized OT (e.g. Fused Gromov OT, unbalanced OT, etc.), and demonstrate their superior performance on public datasets for both image and text downstream tasks. We also rethink the inference stage of CLIP by using the tool of OT, and propose to adopt the fused Gromov OT for (zero-shot) classification, in which the prediction is based on the graph representation whereby images and texts are nodes for graph matching. By our new technique, we show how to generalize zero-shot classification to other more flexible zero-shot tasks with competitive performance: long-tailed classification and selective classification. The former assumes the known prior distribution of labels, while in the latter case, only a subset of samples are asked to predict, yet with high prediction confidence.

Optimization · Large Scale, Parallel and Distributed

Chunlin Tian, Zhan Shi, Xinpeng Qin, Li Li, Cheng-Zhong Xu

Federated Learning (FL) enables multiple devices to collaboratively train a shared model while ensuring data privacy. The selection of participating devices in each training round critically affects both the model performance and training efficiency, especially given the vast heterogeneity in training capabilities and data distribution across devices. To deal with these challenges, we introduce a novel device selection solution called FedRank, which is based on an end-to-end, ranking-based model that is pre-trained by imitation learning against state-of-the-art analytical approaches. It not only considers data and system heterogeneity at runtime but also adaptively and efficiently chooses the most suitable clients for model training. Specifically, FedRank views client selection in FL as a ranking problem and employs a pairwise training strategy for the smart selection process. Additionally, an imitation learning-based approach is designed to counteract the cold-start issues often seen in state-of-the-art learning-based approaches. Experimental results reveal that FedRank boosts model accuracy by 5.2% to 56.9%, accelerates the training convergence up to $2.01 \times$ and saves the energy consumption up to 40.1%.

Miscellaneous Aspects of Machine Learning · Transfer, Multitask and Meta-learning

Hongduan Tian, Feng Liu, Tongliang Liu, Bo Du, Yiu Ming Cheung, Bo Han

In cross-domain few-shot classification, _nearest centroid classifier_ (NCC) aims to learn representations to construct a metric space where few-shot classification can be performed by measuring the similarities between samples and the prototype of each class. An intuition behind NCC is that each sample is pulled closer to the class centroid it belongs to while pushed away from those of other classes. However, in this paper, we find that there exist high similarities between NCC-learned representations of two samples from different classes. In order to address this problem, we propose a bi-level optimization framework, _maximizing optimized kernel dependence_ (MOKD) to learn a set of class-specific representations that match the cluster structures indicated by labeled data of the given task. Specifically, MOKD first optimizes the kernel adopted in *Hilbert-Schmidt independence criterion* (HSIC) to obtain the optimized kernel HSIC (opt-HSIC) that can capture the dependence more precisely. Then, an optimization problem regarding the opt-HSIC is addressed to simultaneously maximize the dependence between representations and labels and minimize the dependence among all samples. Extensive experiments on Meta-Dataset demonstrate that MOKD can not only achieve better generalization performance on unseen domains in most cases but also learn better data representation clusters. The project repository of MOKD is available at: [https://github.com/tmlr-group/MOKD](https://github.com/tmlr-group/MOKD).

Deep Learning · Robustness

Qiwei Tian, Chenhao Lin, Zhengyu Zhao, Qian Li, Chao Shen

Adversarial training has achieved substantial performance in defending image retrieval against adversarial examples. However, existing studies in deep metric learning (DML) still suffer from two major limitations: *weak adversary* and *model collapse*. In this paper, we address these two limitations by proposing **C**ollapse-**A**ware **TRI**plet **DE**coupling (**CA-TRIDE**). Specifically, TRIDE yields a stronger adversary by spatially decoupling the perturbation targets into the anchor and the other candidates. Furthermore, CA prevents the consequential model collapse, based on a novel metric, collapseness, which is incorporated into the optimization of perturbation. We also identify two drawbacks of the existing robustness metric in image retrieval and propose a new metric for a more reasonable robustness evaluation. Extensive experiments on three datasets demonstrate that CA-TRIDE outperforms existing defense methods in both conventional and new metrics. *Codes are available at https://github.com/michaeltian108/CA-TRIDE.*

Deep Learning · Self-Supervised Learning

Hugo Thimonier, Fabrice Popineau, Arpad Rimmel, Bich-Liên DOAN

Anomaly detection is vital in many domains, such as finance, healthcare, and cybersecurity. In this paper, we propose a novel deep anomaly detection method for tabular data that leverages Non-Parametric Transformers (NPTs), a model initially proposed for supervised tasks, to capture both feature-feature and sample-sample dependencies. In a reconstruction-based framework, we train an NPT to reconstruct masked features of normal samples. In a non-parametric fashion, we leverage the whole training set during inference and use the model's ability to reconstruct the masked features to generate an anomaly score. To the best of our knowledge, this is the first work to successfully combine feature-feature and sample-sample dependencies for anomaly detection on tabular datasets. Through extensive experiments on 31 benchmark tabular datasets, we demonstrate that our method achieves state-of-the-art performance, outperforming existing methods by 2.4% and 1.2% in terms of F1-score and AUROC, respectively. Our ablation study further proves that modeling both types of dependencies is crucial for anomaly detection on tabular data.

Optimization · Non-Convex

Guyard Theo, Cédric Herzet, Clément Elvira, Ayse-Nur Arslan

We consider the resolution of learning problems involving $\ell_0$-regularization via Branch-and- Bound (BnB) algorithms. These methods explore regions of the feasible space of the problem and check whether they do not contain solutions through “pruning tests”. In standard implementations, evaluating a pruning test requires to solve a convex optimization problem, which may result in computational bottlenecks. In this paper, we present an alternative to implement pruning tests for some generic family of $\ell_0$-regularized problems. Our proposed procedure allows the simultaneous assessment of several regions and can be embedded in standard BnB implementations with a negligible computational overhead. We show through numerical simulations that our pruning strategy can improve the solving time of BnB procedures by several orders of magnitude for typical problems encountered in machine-learning applications.