308 research outputs found
Robust Bandit Learning with Imperfect Context
A standard assumption in contextual multi-arm bandit is that the true context
is perfectly known before arm selection. Nonetheless, in many practical
applications (e.g., cloud resource management), prior to arm selection, the
context information can only be acquired by prediction subject to errors or
adversarial modification. In this paper, we study a contextual bandit setting
in which only imperfect context is available for arm selection while the true
context is revealed at the end of each round. We propose two robust arm
selection algorithms: MaxMinUCB (Maximize Minimum UCB) which maximizes the
worst-case reward, and MinWD (Minimize Worst-case Degradation) which minimizes
the worst-case regret. Importantly, we analyze the robustness of MaxMinUCB and
MinWD by deriving both regret and reward bounds compared to an oracle that
knows the true context. Our results show that as time goes on, MaxMinUCB and
MinWD both perform as asymptotically well as their optimal counterparts that
know the reward function. Finally, we apply MaxMinUCB and MinWD to online edge
datacenter selection, and run synthetic simulations to validate our theoretical
analysis
Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Many problems, such as online ad display, can be formulated as online
bipartite matching. The crucial challenge lies in the nature of
sequentially-revealed online item information, based on which we make
irreversible matching decisions at each step. While numerous expert online
algorithms have been proposed with bounded worst-case competitive ratios, they
may not offer satisfactory performance in average cases. On the other hand,
reinforcement learning (RL) has been applied to improve the average
performance, but it lacks robustness and can perform arbitrarily poorly. In
this paper, we propose a novel RL-based approach to edge-weighted online
bipartite matching with robustness guarantees (LOMAR), achieving both good
average-case and worst-case performance. The key novelty of LOMAR is a new
online switching operation which, based on a judicious condition to hedge
against future uncertainties, decides whether to follow the expert's decision
or the RL decision for each online item. We prove that for any ,
LOMAR is -competitive against any given expert online algorithm. To
improve the average performance, we train the RL policy by explicitly
considering the online switching operation. Finally, we run empirical
experiments to demonstrate the advantages of LOMAR compared to existing
baselines. Our code is available at: https://github.com/Ren-Research/LOMARComment: Accepted by ICML 202
Robust Learning for Smoothed Online Convex Optimization with Feedback Delay
We study a challenging form of Smoothed Online Convex Optimization, a.k.a.
SOCO, including multi-step nonlinear switching costs and feedback delay. We
propose a novel machine learning (ML) augmented online algorithm,
Robustness-Constrained Learning (RCL), which combines untrusted ML predictions
with a trusted expert online algorithm via constrained projection to robustify
the ML prediction. Specifically,we prove that RCL is able to
guarantee-competitiveness against any given expert for
any, while also explicitly training the ML model in a
robustification-aware manner to improve the average-case performance.
Importantly,RCL is the first ML-augmented algorithm with a provable robustness
guarantee in the case of multi-step switching cost and feedback delay.We
demonstrate the improvement of RCL in both robustness and average performance
using battery management for electrifying transportationas a case study.Comment: Accepted by NeurIPS 202
Making AI Less "Thirsty": Uncovering and Addressing the Secret Water Footprint of AI Models
The growing carbon footprint of artificial intelligence (AI) models,
especially large ones such as GPT-3, has been undergoing public scrutiny.
Unfortunately, however, the equally important and enormous water (withdrawal
and consumption) footprint of AI models has remained under the radar. For
example, training GPT-3 in Microsoft's state-of-the-art U.S. data centers can
directly evaporate 700,000 liters of clean freshwater, but such information has
been kept a secret. More critically, the global AI demand may be accountable
for 4.2 -- 6.6 billion cubic meters of water withdrawal in 2027, which is more
than the total annual water withdrawal of 4 -- 6 Denmark or half of the United
Kingdom. This is very concerning, as freshwater scarcity has become one of the
most pressing challenges shared by all of us in the wake of the rapidly growing
population, depleting water resources, and aging water infrastructures. To
respond to the global water challenges, AI models can, and also must, take
social responsibility and lead by example by addressing their own water
footprint. In this paper, we provide a principled methodology to estimate the
water footprint of AI models, and also discuss the unique spatial-temporal
diversities of AI models' runtime water efficiency. Finally, we highlight the
necessity of holistically addressing water footprint along with carbon
footprint to enable truly sustainable AI.Comment: New updates include discussion on water withdrawal and water
consumption, scope definition for water, and new estimates of GPT-3's water
footprint based on Microsoft's new WUE and PUE data. Source codes available
at: https://github.com/Ren-Research/Making-AI-Less-Thirst
CryoAlign: feature-based method for global and local 3D alignment of EM density maps
Advances on cryo-electron imaging technologies have led to a rapidly
increasing number of density maps. Alignment and comparison of density maps
play a crucial role in interpreting structural information, such as
conformational heterogeneity analysis using global alignment and atomic model
assembly through local alignment. Here, we propose a fast and accurate global
and local cryo-electron microscopy density map alignment method CryoAlign,
which leverages local density feature descriptors to capture spatial structure
similarities. CryoAlign is the first feature-based EM map alignment tool, in
which the employment of feature-based architecture enables the rapid
establishment of point pair correspondences and robust estimation of alignment
parameters. Extensive experimental evaluations demonstrate the superiority of
CryoAlign over the existing methods in both alignment accuracy and speed
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