Zobrazeno 1 - 10
of 386
pro vyhledávání: '"MADDISON, P. J."'
Given a collection of feature maps indexed by a set $\mathcal{T}$, we study the performance of empirical risk minimization (ERM) on regression problems with square loss over the union of the linear classes induced by these feature maps. This setup ai
Externí odkaz:
http://arxiv.org/abs/2411.12029
Knowing the effect of an intervention is critical for human decision-making, but current approaches for causal effect estimation rely on manual data collection and structuring, regardless of the causal assumptions. This increases both the cost and ti
Externí odkaz:
http://arxiv.org/abs/2407.07018
Autor:
Dong, Honghua, Su, Qidong, Gao, Yubo, Li, Zhaoyu, Ruan, Yangjun, Pekhimenko, Gennady, Maddison, Chris J., Si, Xujie
Large Language Models (LLMs) have become increasingly capable of handling diverse tasks with the aid of well-crafted prompts and integration of external tools, but as task complexity rises, the workflow involving LLMs can be complicated and thus chal
Externí odkaz:
http://arxiv.org/abs/2406.13161
We study the problem of designing minimax procedures in linear regression under the quantile risk. We start by considering the realizable setting with independent Gaussian noise, where for any given noise level and distribution of inputs, we obtain t
Externí odkaz:
http://arxiv.org/abs/2406.12145
Autor:
Cotta, Leonardo, Maddison, Chris J.
Frontier Large Language Models (LLMs) can be socially discriminatory or sensitive to spurious features of their inputs. Because only well-resourced corporations can train frontier LLMs, we need robust test-time strategies to control such biases. Exis
Externí odkaz:
http://arxiv.org/abs/2406.07685
Autor:
Thudi, Anvith, Maddison, Chris J.
Machine learning models are often required to perform well across several pre-defined settings, such as a set of user groups. Worst-case performance is a common metric to capture this requirement, and is the objective of group distributionally robust
Externí odkaz:
http://arxiv.org/abs/2406.01477
Understanding how language model performance varies with scale is critical to benchmark and algorithm development. Scaling laws are one approach to building this understanding, but the requirement of training models across many different scales has l
Externí odkaz:
http://arxiv.org/abs/2405.10938
Identifying how much a model ${\widehat{p}}_{\theta}(Y|X)$ knows about the stochastic real-world process $p(Y|X)$ it was trained on is important to ensure it avoids producing incorrect or "hallucinated" answers or taking unsafe actions. But this is d
Externí odkaz:
http://arxiv.org/abs/2402.08733
Autor:
Ruan, Yangjun, Dong, Honghua, Wang, Andrew, Pitis, Silviu, Zhou, Yongchao, Ba, Jimmy, Dubois, Yann, Maddison, Chris J., Hashimoto, Tatsunori
Recent advances in Language Model (LM) agents and tool use, exemplified by applications like ChatGPT Plugins, enable a rich set of capabilities but also amplify potential risks - such as leaking private data or causing financial losses. Identifying t
Externí odkaz:
http://arxiv.org/abs/2309.15817
Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memory resources. In this work, we show how to leverage randomness and desi
Externí odkaz:
http://arxiv.org/abs/2308.04412