@article {5541, title = {RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4}, journal = {IEEE Robotics and Automation Letters}, volume = {9}, year = {2024}, month = {04/2024}, pages = {3045 - 3052}, abstract = {

Deep Reinforcement Learning (DRL) has proven effective in learning control policies using robotic grippers, but much less practical for solving the problem of grasping with dexterous hands {\textendash} especially on real robotic platforms {\textendash} due to the high dimensionality of the problem. In this letter, we focus on the multi-fingered grasping task with the anthropomorphic hand of the iCub humanoid. We propose the RESidual learning with PREtrained CriTics (RESPRECT) method that, starting from a policy pre-trained on a large set of objects, can learn a residual policy to grasp a novel object in a fraction ( \~{}5{\texttimes} faster) of the timesteps required to train a policy from scratch, without requiring any task demonstration. To our knowledge, this is the first Residual Reinforcement Learning (RRL) approach that learns a residual policy on top of another policy pre-trained with DRL. We exploit some components of the pre-trained policy during residual learning that further speed-up the training. We benchmark our results in the iCub simulated environment, and we show that RESPRECT can be effectively used to learn a multi-fingered grasping policy on the real iCub robot.

}, doi = {10.1109/LRA.2024.3363532}, url = {https://ieeexplore.ieee.org/document/10423830/}, author = {Ceola, Federico and Rosasco, Lorenzo and Natale, Lorenzo and Ceola, Federico} } @article {5517, title = {Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods}, journal = {Image and Vision Computing}, volume = {142}, year = {2024}, month = {02/2024}, pages = {104894}, abstract = {

The impressive performance of deep learning architectures is associated with a massive increase in model complexity. Millions of parameters need to be tuned, with training and inference time scaling accordingly, together with energy consumption. But is massive fine-tuning always necessary? In this paper, focusing on image classification, we consider a simple transfer learning approach exploiting pre-trained convolutional features as input for a fast-to-train kernel method. We refer to this approach as top-tuning since only the kernel classifier is trained on the target dataset. In our study, we perform more than 3000 training processes focusing on 32 small to medium-sized target datasets, a typical situation where transfer learning is necessary. We show that the top-tuning approach provides comparable accuracy with respect to fine-tuning, with a training time between one and two orders of magnitude smaller. These results suggest that top-tuning is an effective alternative to fine-tuning in small/medium datasets, being especially useful when training time efficiency and computational resources saving are crucial.

}, issn = {02628856}, doi = {10.1016/j.imavis.2023.104894}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0262885623002688}, author = {Alfano, Paolo Didier and Pastore, Vito Paolo and Rosasco, Lorenzo and Odone, Francesca} } @article {5532, title = {Fast iterative regularization by reusing dataAbstract}, journal = {Journal of Inverse and Ill-posed Problems}, year = {2023}, month = {10/2023}, abstract = {

Discrete inverse problems correspond to solving a system of equations in a stable way with respect to noise in the data. A typical approach to select a meaningful solution is to introduce a regularizer. While for most applications the regularizer is convex, in many cases it is neither smooth nor strongly convex. In this paper, we propose and study two new iterative regularization methods, based on a primal-dual algorithm, to regularize inverse problems efficiently. Our analysis, in the noise free case, provides convergence rates for the Lagrangian and the feasibility gap. In the noisy case, it provides stability bounds and early stopping rules with theoretical guarantees. The main novelty of our work is the exploitation of some a priori knowledge about the solution set: we show that the linear equations determined by the data can be used more than once along the iterations. We discuss various approaches to reuse linear equations that are at the same time consistent with our assumptions and flexible in the implementation. Finally, we illustrate our theoretical findings with numerical simulations for robust sparse recovery and image reconstruction. We confirm the efficiency of the proposed regularization approaches, comparing the results with state-of-the-art methods.

}, issn = {0928-0219}, doi = {10.1515/jiip-2023-0009}, url = {https://www.degruyter.com/document/doi/10.1515/jiip-2023-0009/html}, author = {Vega, Cristian and Molinari, Cesare and Rosasco, Lorenzo and Villa, Silvia} } @article {5241, title = {For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability}, journal = {Analysis and Applications}, volume = {21}, year = {2023}, month = {01/2023}, pages = {193 - 215}, abstract = {

In this paper, we study kernel ridge-less regression, including the case of interpolating solutions. We prove that maximizing the leave-one-out (CVloo) stability minimizes the expected error. Further, we also prove that the minimum norm solution {\textemdash} to which gradient algorithms are known to converge {\textemdash} is the most stable solution. More precisely, we show that the minimum norm interpolating solution minimizes a bound on CVloo stability, which in turn is controlled by the smallest singular value, hence the condition number, of the empirical kernel matrix. These quantities can be characterized in the asymptotic regime where both the dimension (d) and cardinality (n) of the data go to infinity (with nd{\textrightarrow}γ as d,n{\textrightarrow}$\infty$). Our results suggest that the property of CVloostability of the learning algorithm with respect to perturbations of the training set may provide a more general framework than the classical theory of Empirical Risk Minimization (ERM). While ERM was developed to deal with the classical regime in which the architecture of the learning network is fixed and n{\textrightarrow}$\infty$, the modern regime focuses on interpolating regressors and overparameterized models, when both d and n go to infinity. Since the stability framework is known to be equivalent to the classical theory in the classical regime, our results here suggest that it may be interesting to extend it beyond kernel regression to other overparameterized algorithms such as deep networks.

}, keywords = {Algorithmic stability, high dimensional statistics, kernel regression, minimum norm interpolation, overparameterization}, issn = {0219-5305}, doi = {10.1142/S0219530522400115}, url = {https://www.worldscientific.com/doi/10.1142/S0219530522400115}, author = {Rangamani, Akshay and Rosasco, Lorenzo and Tomaso Poggio} } @article {5290, title = {Implicit regularization with strongly convex bias: Stability and acceleration}, journal = {Analysis and Applications}, volume = {21}, year = {2023}, month = {-01/2023}, pages = {165 - 191}, abstract = {

Implicit regularization refers to the property of optimization algorithms to be biased towards a certain class of solutions. This property is relevant to understand the behavior of modern machine learning algorithms as well as to design efficient computational methods. While the case where the bias is given by a Euclidean norm is well understood, implicit regularization schemes for more general classes of biases are much less studied. In this work, we consider the case where the bias is given by a strongly convex functional, in the context of linear models, and data possibly corrupted by noise. In particular, we propose and analyze accelerated optimization methods and highlight a trade-off between convergence speed and stability. Theoretical findings are complemented by an empirical analysis on high-dimensional inverse problems in machine learning and signal processing, showing excellent results compared to the state of the art.

}, issn = {0219-5305}, doi = {10.1142/S0219530522400139}, url = {https://www.worldscientific.com/doi/10.1142/S0219530522400139}, author = {Villa, Silvia and Matet, Simon and Vu, Bằng C{\^o}ng and Rosasco, Lorenzo} } @article {5317, title = {Physics informed machine learning for wind speed prediction}, journal = {Energy}, volume = {268}, year = {2023}, month = {04/2023}, pages = {126628}, abstract = {

The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. Here we take an alternative data-driven approach based on supervised learning. We analyze massive datasets of wind measured from anemometers located at 10\ m height in 32 locations in central and north-west Italy. We train supervised learning algorithms using the past history of wind to predict its value at future horizons. Using data from single locations and horizons, we compare systematically several algorithms where we vary the input/output variables, the memory and the linear vs non-linear model. We then compare performance of the best algorithms across all locations and forecasting horizons. We find that the optimal design as well as its performance change with the location. We demonstrate that the presence of a diurnal cycle provides a rationale to understand this variation. We conclude with a systematic comparison with state of the art algorithms. When focusing on publicly available datasets, our algorithm improves performance of 0.3\ m/s on average. In the aggregate, these comparisons show that, when the model is accurately designed, shallow algorithms are competitive with deep architectures.

}, issn = {03605442}, doi = {10.1016/j.energy.2023.126628}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0360544223000221}, author = {Lagomarsino-Oneto, Daniele and Meanti, Giacomo and Pagliana, Nicol{\`o} and Verri, Alessandro and Mazzino, Andrea and Rosasco, Lorenzo and Seminara, Agnese} } @article {5522, title = {A structured prediction approach for robot imitation learning}, journal = {The International Journal of Robotics Research}, volume = {43}, year = {2023}, month = {11/2023}, pages = {113 - 133}, abstract = {

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised learning that enables learning models to operate on output spaces with complex structures. Through the lens of structured prediction, we show how robots can learn to imitate trajectories belonging to not only Euclidean spaces but also Riemannian manifolds. Exploiting ideas from information theory, we propose a class of loss functions based on the f-divergence to measure the information loss between the demonstrated and reproduced probabilistic trajectories. Different types of f-divergence will result in different policies, which we call imitation modes. Furthermore, our approach enables the incorporation of spatial and temporal trajectory modulation, which is necessary for robots to be adaptive to the change in working conditions. We benchmark our algorithm against state-of-the-art methods in terms of trajectory reproduction and adaptation. The quantitative evaluation shows that our approach outperforms other algorithms regarding both accuracy and efficiency. We also report real-world experimental results on learning manifold trajectories in a polishing task with a KUKA LWR robot arm, illustrating the effectiveness of our algorithmic framework.

}, issn = {0278-3649}, doi = {10.1177/02783649231204656}, url = {http://journals.sagepub.com/doi/10.1177/02783649231204656}, author = {Duan, Anqing and Batzianoulis, Iason and Camoriano, Raffaello and Rosasco, Lorenzo and Pucci, Daniele and Billard, Aude} } @article {5295, title = {Learning new physics efficiently with nonparametric methodsAbstract}, journal = {The European Physical Journal C}, volume = {82}, year = {2022}, month = {10/2022}, abstract = {

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D{\textquoteright}Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.

}, doi = {10.1140/epjc/s10052-022-10830-y}, url = {https://link.springer.com/10.1140/epjc/s10052-022-10830-y}, author = {Letizia, Marco and Losapio, Gianvito and Rando, Marco and Grosso, Gaia and Wulzer, Andrea and Pierini, Maurizio and Zanetti, Marco and Rosasco, Lorenzo} }