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Publications
Constrained Robotic Navigation on Preferred Terrains Using LLMs and Speech Instruction: Exploiting the Power of Adverbs
Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving con… (see more)voluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated block-coordinate descent optimizer, which avoid the degenerate solutions of our loss. Interestingly, our simple fine-tuning approach achieves highly competitive performances in comparison to state-of-the-art methods, while being applicable to black-box settings and removing the need for specific episodic pre-training strategies. Furthermore, we introduce a new benchmark to assess the robustness of the competing methods to domain shifts. In this setting, our fine-tuning baseline obtains consistently better results than meta-learning methods.
At least half of the world's population do not have access to essential health services. Worse, large numbers of households are being pushed… (see more) into poverty because they must pay for health care out of their own pockets.
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (see more)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness … (see more)in Artificial Intelligence (AI) systems. The discussion begins with a detailed definition of robustness, portraying it as the ability of ML models to maintain stable performance across varied and unexpected environmental conditions. ML robustness is dissected through several lenses: its complementarity with generalizability; its status as a requirement for trustworthy AI; its adversarial vs non-adversarial aspects; its quantitative metrics; and its indicators such as reproducibility and explainability. The chapter delves into the factors that impede robustness, such as data bias, model complexity, and the pitfalls of underspecified ML pipelines. It surveys key techniques for robustness assessment from a broad perspective, including adversarial attacks, encompassing both digital and physical realms. It covers non-adversarial data shifts and nuances of Deep Learning (DL) software testing methodologies. The discussion progresses to explore amelioration strategies for bolstering robustness, starting with data-centric approaches like debiasing and augmentation. Further examination includes a variety of model-centric methods such as transfer learning, adversarial training, and randomized smoothing. Lastly, post-training methods are discussed, including ensemble techniques, pruning, and model repairs, emerging as cost-effective strategies to make models more resilient against the unpredictable. This chapter underscores the ongoing challenges and limitations in estimating and achieving ML robustness by existing approaches. It offers insights and directions for future research on this crucial concept, as a prerequisite for trustworthy AI systems.
Electricity price forecasting is a challenging task for decision-makers in deregulated power markets due to the inherent characteristics of … (see more)electricity prices, e.g., high frequency and volatility. Therefore, accurate forecasting of electricity prices can assist market participants in maximizing their profit. Accordingly, we proposed a novel hybrid Deep Learning model to forecast one-step, two-step, and three-step ahead Ontario electricity prices based on a Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU). Our model consists of three consecutive CNN-GRU models combined in parallel with different input data. We downsampled input data via pooling layers at the beginning of two streams of the model to capture different frequencies of price patterns concurrently. Also, a set of external variables, including previous prices, electricity load, generation, import and export, and weather data, were considered in our forecasting models to test whether these features improve the efficiency of the models. Finally, three experiments in various weeks of 2022 were carried out in the Ontario electricity market to assess the model. The results indicate that the proposed model reduced the forecasting error significantly by 63.3% in the first experiment, 41.8% in the second, and 28.2% in the third, on average, with respect to a Root Mean Square Error (RMSE). Also, the proposed model was compared with outperformed several baseline models, including statistical time-series, Machine Learning, and Deep Learning models. Furthermore, the comparison of results in univariate and multivariate settings indicated that adding variables to forecasting models did not help reduce forecasting errors.
